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Hegde ST, Khan AI, Perez-Saez J, Khan II, Hulse JD, Islam MT, Khan ZH, Ahmed S, Bertuna T, Rashid M, Rashid R, Hossain MZ, Shirin T, Wiens KE, Gurley ES, Bhuiyan TR, Qadri F, Azman AS. Clinical surveillance systems obscure the true cholera infection burden in an endemic region. Nat Med 2024; 30:888-895. [PMID: 38378884 PMCID: PMC10957480 DOI: 10.1038/s41591-024-02810-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 01/09/2024] [Indexed: 02/22/2024]
Abstract
Our understanding of cholera transmission and burden largely relies on clinic-based surveillance, which can obscure trends, bias burden estimates and limit the impact of targeted cholera-prevention measures. Serological surveillance provides a complementary approach to monitoring infections, although the link between serologically derived infections and medically attended disease incidence-shaped by immunological, behavioral and clinical factors-remains poorly understood. We unravel this cascade in a cholera-endemic Bangladeshi community by integrating clinic-based surveillance, healthcare-seeking and longitudinal serological data through statistical modeling. Combining the serological trajectories with a reconstructed incidence timeline of symptomatic cholera, we estimated an annual Vibrio cholerae O1 infection incidence rate of 535 per 1,000 population (95% credible interval 514-556), with incidence increasing by age group. Clinic-based surveillance alone underestimated the number of infections and reported cases were not consistently correlated with infection timing. Of the infections, 4 in 3,280 resulted in symptoms, only 1 of which was reported through the surveillance system. These results impart insights into cholera transmission dynamics and burden in the epicenter of the seventh cholera pandemic, where >50% of our study population had an annual V. cholerae O1 infection, and emphasize the potential for a biased view of disease burden and infection risk when depending solely on clinical surveillance data.
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Affiliation(s)
- Sonia T Hegde
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MA, USA
| | - Ashraful Islam Khan
- Infectious Disease Division, icddr,b (International Centre for Diarrhoeal Disease Research, Bangladesh), Dhaka, Bangladesh
| | - Javier Perez-Saez
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MA, USA
- Unit of Population Epidemiology, Geneva University Hospitals, Geneva, Switzerland
- Geneva Centre for Emerging Viral Diseases, Geneva University Hospitals, Geneva, Switzerland
| | - Ishtiakul Islam Khan
- Infectious Disease Division, icddr,b (International Centre for Diarrhoeal Disease Research, Bangladesh), Dhaka, Bangladesh
| | - Juan Dent Hulse
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MA, USA
| | - Md Taufiqul Islam
- Infectious Disease Division, icddr,b (International Centre for Diarrhoeal Disease Research, Bangladesh), Dhaka, Bangladesh
| | - Zahid Hasan Khan
- Infectious Disease Division, icddr,b (International Centre for Diarrhoeal Disease Research, Bangladesh), Dhaka, Bangladesh
| | - Shakeel Ahmed
- Bangladesh Institute of Tropical and Infectious Diseases, Chattogram, Bangladesh
| | - Taner Bertuna
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MA, USA
| | - Mamunur Rashid
- Bangladesh Institute of Tropical and Infectious Diseases, Chattogram, Bangladesh
| | - Rumana Rashid
- Bangladesh Institute of Tropical and Infectious Diseases, Chattogram, Bangladesh
| | - Md Zakir Hossain
- Bangladesh Institute of Tropical and Infectious Diseases, Chattogram, Bangladesh
| | - Tahmina Shirin
- Institute of Epidemiology, Disease Control and Research, Dhaka, Bangladesh
| | - Kirsten E Wiens
- Department of Epidemiology, Temple University, Philadelphia, PA, USA
| | - Emily S Gurley
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MA, USA
| | - Taufiqur Rahman Bhuiyan
- Infectious Disease Division, icddr,b (International Centre for Diarrhoeal Disease Research, Bangladesh), Dhaka, Bangladesh
| | - Firdausi Qadri
- Infectious Disease Division, icddr,b (International Centre for Diarrhoeal Disease Research, Bangladesh), Dhaka, Bangladesh.
| | - Andrew S Azman
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MA, USA.
- Geneva Centre for Emerging Viral Diseases, Geneva University Hospitals, Geneva, Switzerland.
- Division of Tropical and Humanitarian Medicine, Geneva University Hospitals, Geneva, Switzerland.
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Lorthe E, Richard V, Dumont R, Loizeau A, Perez-Saez J, Baysson H, Zaballa ME, Lamour J, Pullen N, Schrempft S, Barbe RP, Posfay-Barbe KM, Guessous I, Stringhini S. Socioeconomic conditions and children's mental health and quality of life during the COVID-19 pandemic: An intersectional analysis. SSM Popul Health 2023; 23:101472. [PMID: 37560087 PMCID: PMC10407575 DOI: 10.1016/j.ssmph.2023.101472] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 07/20/2023] [Accepted: 07/21/2023] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND Children and adolescents are highly vulnerable to the impact of sustained stressors during developmentally sensitive times. We investigated how demographic characteristics intersect with socioeconomic dimensions to shape the social patterning of quality of life and mental health in children and adolescents, two years into the COVID-19 pandemic. METHODS We used data from the prospective SEROCoV-KIDS cohort study of children and adolescents living in Geneva (Switzerland, 2022). We conducted an intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy by nesting participants within 48 social strata defined by intersecting sex, age, immigrant background, parental education and financial hardship in Bayesian multilevel logistic models for poor health-related quality of life (HRQoL, measured with PedsQL) and mental health difficulties (measured with the Strengths and Difficulties Questionnaire). RESULTS Among participants aged 2-17 years, 240/2096 (11.5%, 95%CI 10.1-12.9) had poor HRQoL and 105/2135 (4.9%, 95%CI 4.0-5.9) had mental health difficulties. The predicted proportion of poor HRQoL ranged from 3.4% for 6-11 years old Swiss girls with highly educated parents and no financial hardship to 34.6% for 12-17 years old non-Swiss girls with highly educated parents and financial hardship. Intersectional strata involving adolescents and financial hardship showed substantially worse HRQoL than their counterparts. Between-stratum variations in the predicted frequency of mental health difficulties were limited (range 4.4%-6.5%). CONCLUSIONS We found considerable differences in adverse outcomes across social strata. Our results suggest that, post-pandemic, interventions to address social inequities in HRQoL should focus on specific intersectional strata involving adolescents and families experiencing financial hardship, while those aiming to improve mental health should target all children and adolescents.
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Affiliation(s)
- Elsa Lorthe
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- Université Paris Cité, Inserm, INRAE, Centre for Research in Epidemiology and Statistics Paris (CRESS), Paris, France
| | - Viviane Richard
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Roxane Dumont
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Andrea Loizeau
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Javier Perez-Saez
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA
| | - Hélène Baysson
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Maria-Eugenia Zaballa
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Julien Lamour
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Nick Pullen
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Stephanie Schrempft
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Rémy P. Barbe
- Division of Child and Adolescent Psychiatry, Department of Woman, Child, and Adolescent Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Klara M. Posfay-Barbe
- Department of Woman, Child, and Adolescent Medicine, Geneva University Hospitals, Geneva, Switzerland
- Department of Pediatrics, Gynecology & Obstetrics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Idris Guessous
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Silvia Stringhini
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- University Center for General Medicine and Public Health, University of Lausanne, Lausanne, Switzerland
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3
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Hegde S, Khan AI, Perez-Saez J, Khan II, Hulse JD, Islam MT, Khan ZH, Ahmed S, Bertuna T, Rashid M, Rashid R, Hossain MZ, Shirin T, Wiens K, Gurley ES, Bhuiyan TR, Qadri F, Azman AS. Estimating the gap between clinical cholera and true community infections: findings from an integrated surveillance study in an endemic region of Bangladesh. medRxiv 2023:2023.07.18.23292836. [PMID: 37502941 PMCID: PMC10371108 DOI: 10.1101/2023.07.18.23292836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Our understanding of cholera transmission and burden largely rely on clinic-based surveillance, which can obscure trends, bias burden estimates and limit the impact of targeted cholera-prevention measures. Serologic surveillance provides a complementary approach to monitoring infections, though the link between serologically-derived infections and medically-attended disease - shaped by immunological, behavioral, and clinical factors - remains poorly understood. We unravel this cascade in a cholera-endemic Bangladeshi community by integrating clinic-based surveillance, healthcare seeking, and longitudinal serological data through statistical modeling. We found >50% of the study population had a V. cholerae O1 infection annually, and infection timing was not consistently correlated with reported cases. Four in 2,340 infections resulted in symptoms, only one of which was reported through the surveillance system. These results provide new insights into cholera transmission dynamics and burden in the epicenter of the 7th cholera pandemic and provide a framework to synthesize serological and clinical surveillance data.
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Affiliation(s)
- Sonia Hegde
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | | | - Javier Perez-Saez
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
- Unit of Population Epidemiology, Geneva University Hospitals, Geneva, Switzerland
| | | | - Juan Dent Hulse
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | | | | | - Shakeel Ahmed
- Bangladesh Institute of Tropical and Infectious Diseases, Chattogram, Bangladesh
| | - Taner Bertuna
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Mamunur Rashid
- Bangladesh Institute of Tropical and Infectious Diseases, Chattogram, Bangladesh
| | - Rumuna Rashid
- Bangladesh Institute of Tropical and Infectious Diseases, Chattogram, Bangladesh
| | - Md Zakir Hossain
- Bangladesh Institute of Tropical and Infectious Diseases, Chattogram, Bangladesh
| | - Tahmina Shirin
- Institute of Epidemiology, Disease Control and Research, Dhaka, Bangladesh
| | - Kirsten Wiens
- Department of Epidemiology, Temple University, Philadelphia, USA
| | - Emily S Gurley
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | | | | | - Andrew S Azman
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
- Geneva Centre for Emerging Viral Diseases, Geneva University Hospitals, Geneva, Switzerland
- Division of Tropical and Humanitarian Medicine, Geneva University Hospitals, Geneva, Switzerland
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4
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Perez-Saez J, Zaballa ME, Lamour J, Yerly S, Dubos R, Courvoisier DS, Villers J, Balavoine JF, Pittet D, Kherad O, Vuilleumier N, Kaiser L, Guessous I, Stringhini S, Azman AS. Long term anti-SARS-CoV-2 antibody kinetics and correlate of protection against Omicron BA.1/BA.2 infection. Nat Commun 2023; 14:3032. [PMID: 37230973 DOI: 10.1038/s41467-023-38744-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 05/11/2023] [Indexed: 05/27/2023] Open
Abstract
Binding antibody levels against SARS-CoV-2 have shown to be correlates of protection against infection with pre-Omicron lineages. This has been challenged by the emergence of immune-evasive variants, notably the Omicron sublineages, in an evolving immune landscape with high levels of cumulative incidence and vaccination coverage. This in turn limits the use of widely available commercial high-throughput methods to quantify binding antibodies as a tool to monitor protection at the population-level. Here we show that anti-Spike RBD antibody levels, as quantified by the immunoassay used in this study, are an indirect correlate of protection against Omicron BA.1/BA.2 for individuals previously infected by SARS-CoV-2. Leveraging repeated serological measurements between April 2020 and December 2021 on 1083 participants of a population-based cohort in Geneva, Switzerland, and using antibody kinetic modeling, we found up to a three-fold reduction in the hazard of having a documented positive SARS-CoV-2 infection during the Omicron BA.1/BA.2 wave for anti-S antibody levels above 800 IU/mL (HR 0.30, 95% CI 0.22-0.41). However, we did not detect a reduction in hazard among uninfected participants. These results provide reassuring insights into the continued interpretation of SARS-CoV-2 binding antibody measurements as an independent marker of protection at both the individual and population levels.
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Affiliation(s)
- Javier Perez-Saez
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland.
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
| | - María-Eugenia Zaballa
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Julien Lamour
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Sabine Yerly
- Division of Laboratory Medicine, Department of Diagnostics, Geneva University Hospitals, Geneva, Switzerland
| | - Richard Dubos
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Delphine S Courvoisier
- General Directorate of Health, Geneva, Switzerland
- Division of Quality of Care, Geneva University Hospitals, Geneva, Switzerland
| | - Jennifer Villers
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | | | - Didier Pittet
- Department of Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Infection Control Program and World Health Organization Collaborating Centre on Patient Safety, Geneva University Hospitals, Geneva, Switzerland
| | - Omar Kherad
- Department of Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Internal Medicine, Hôpital de la Tour, Geneva, Switzerland
| | - Nicolas Vuilleumier
- Division of Laboratory Medicine, Department of Diagnostics, Geneva University Hospitals, Geneva, Switzerland
- Department of Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Laurent Kaiser
- Division of Laboratory Medicine, Department of Diagnostics, Geneva University Hospitals, Geneva, Switzerland
- Department of Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Infectious Diseases, Department of Medicine, Geneva University Hospitals, Geneva, Switzerland
- Geneva Centre for Emerging Viral Diseases, Geneva University Hospitals, Geneva, Switzerland
| | - Idris Guessous
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division and Department of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Silvia Stringhini
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- University Centre for General Medicine and Public Health, University of Lausanne, Lausanne, Switzerland
| | - Andrew S Azman
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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Azman AS, Perez-Saez J. [Cholera and Climate: What do we know?]. Rev Med Suisse 2023; 19:845-848. [PMID: 37139878 DOI: 10.53738/revmed.2023.19.825.845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Cholera is an acute diarrheal disease caused by the bacteria Vibrio cholerae. Each year, 100'000 people die from cholera. The links between cholera, weather and climate are visible in the seasonality of cholera globally, but evidence to date illustrates that the relationships between them are highly heterogeneous across settings, with differences in both the direction and strength of the associations. Before we can devise evidence-based scenarios on how climate change may influence cholera burden in the future, more detailed case studies, using more robust climate and epidemiological data from across the globe, are needed. In the meantime, provision of sustainable water and sanitation is of the highest priority to offset potential impacts of climate change on cholera.
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Affiliation(s)
- Andrew S Azman
- Department of epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, W6508, Baltimore, MD 21205, États-Unis
| | - Javier Perez-Saez
- Department of epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, W6508, Baltimore, MD 21205, États-Unis
- Unité d'épidémiologie populationnelle, Service de médecine de premier recours, Hôpitaux universitaires de Genève, 1211 Genève 14
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6
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Schrempft S, Pullen N, Baysson H, Wisniak A, Zaballa ME, Pennacchio F, Vollenweider P, Marques-Vidal P, Preisig M, Guessous I, Stringhini S, Arm-Vernez I, Azman AS, Ba F, Bachmann D, Bal A, Balavoine JF, Balavoine M, Barbe RP, Baysson H, Beigbeder L, Berthelot J, Bleich P, Boehm L, Bryand G, Bucolli V, Chappuis F, Collombet P, Courvoisier D, Cudet A, Davidovic V, de Mestral Vargas C, D'ippolito P, Dubos R, Dumont R, Eckerle I, El Merjani N, Flahault A, Francioli N, Frangville M, Graindorge C, Guessous I, Harnal S, Hurst S, Kaiser L, Kherad O, Lamour J, Lescuyer P, L'Huissier F, Lombard FB, Loizeau AJ, Lorthe E, Martinez C, Ménard L, Menon L, Metral-Boffod L, Meyer B, Moulin A, Nehme M, Noël N, Pennacchio F, Perez-Saez J, Pittet D, Portier J, Posfay-Barbe KM, Poulain G, Pugin C, Pullen N, Randrianandrasana ZF, Richard V, Rinaldi F, Rizzo J, Rochat D, Sakvarelidze I, Samir K, Santa Ramirez HA, Schrempft S, Semaani C, Stringhini S, Testini S, Rivas DU, Verolet C, Villers J, Violot G, Vuilleumier N, Wisniak A, Yerly S, Zaballa ME. Prevalence and predictors of psychological distress before, during, and after a COVID-19 pandemic wave in Switzerland, 2021. J Psychiatr Res 2023; 158:192-201. [PMID: 36592533 PMCID: PMC9794129 DOI: 10.1016/j.jpsychires.2022.12.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 10/04/2022] [Accepted: 12/22/2022] [Indexed: 12/28/2022]
Abstract
There are concerns about acute and long-term mental health effects of the COVID-19 pandemic. This study examined the prevalence and predictors of psychological distress before, during, and after a pandemic wave in Switzerland, 2021. Prevalence of psychological distress was estimated in adults aged 35-96 years using the General Health Questionnaire-12 administered in June 2021 (Specchio-COVID19 cohort, N = 3965), and compared to values from 2003 to 2006 (CoLaus|PsyCoLaus cohort, N = 5667). Anxiety and depression were assessed from February to June 2021 using the Generalised Anxiety Disorder scale-2 and the Patient Health Questionnaire-2, respectively. Prevalence of psychological distress in June 2021, after the pandemic wave (16.0% [95% CI, 14.6%-17.4%]) was comparable to pre-pandemic levels (15.1% [14.0%-16.2%]). Anxiety and depression were highest at the start of the pandemic wave in February 2021, and declined from February to June with the relaxation of measures. Predictors of psychological distress included being younger, female, a single parent, unemployed, a change in working hours or job loss in the past 6 months, greater perceived severity and contagiousness of COVID-19, and self-reported post COVID-19. By June 2021, following a pandemic wave, prevalence of psychological distress in Switzerland was closer to pre-pandemic levels. These findings highlight the need for additional mental health support during times of stricter government policies relating to COVID-19; yet they also suggest that individuals can adapt relatively quickly to the changing context.
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Affiliation(s)
- Stephanie Schrempft
- Division of Primary Care, Unit of Population Epidemiology, Geneva University Hospitals, Geneva, Switzerland.
| | - Nick Pullen
- Division of Primary Care, Unit of Population Epidemiology, Geneva University Hospitals, Geneva, Switzerland
| | - Hélène Baysson
- Division of Primary Care, Unit of Population Epidemiology, Geneva University Hospitals, Geneva, Switzerland; Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Ania Wisniak
- Division of Primary Care, Unit of Population Epidemiology, Geneva University Hospitals, Geneva, Switzerland
| | - María-Eugenia Zaballa
- Division of Primary Care, Unit of Population Epidemiology, Geneva University Hospitals, Geneva, Switzerland
| | - Francesco Pennacchio
- Division of Primary Care, Unit of Population Epidemiology, Geneva University Hospitals, Geneva, Switzerland
| | - Peter Vollenweider
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Pedro Marques-Vidal
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Martin Preisig
- Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Idris Guessous
- Division of Primary Care, Unit of Population Epidemiology, Geneva University Hospitals, Geneva, Switzerland; Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Silvia Stringhini
- Division of Primary Care, Unit of Population Epidemiology, Geneva University Hospitals, Geneva, Switzerland; Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland; University Centre for General Medicine and Public Health, University of Lausanne, Lausanne, Switzerland
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7
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Zaballa ME, Perez-Saez J, de Mestral C, Pullen N, Lamour J, Turelli P, Raclot C, Baysson H, Pennacchio F, Villers J, Duc J, Richard V, Dumont R, Semaani C, Loizeau AJ, Graindorge C, Lorthe E, Balavoine JF, Pittet D, Schibler M, Vuilleumier N, Chappuis F, Kherad O, Azman AS, Posfay-Barbe KM, Kaiser L, Trono D, Stringhini S, Guessous I. Seroprevalence of anti-SARS-CoV-2 antibodies and cross-variant neutralization capacity after the Omicron BA.2 wave in Geneva, Switzerland: a population-based study. Lancet Reg Health Eur 2023; 24:100547. [PMID: 36474728 PMCID: PMC9714630 DOI: 10.1016/j.lanepe.2022.100547] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 10/28/2022] [Accepted: 11/02/2022] [Indexed: 12/04/2022]
Abstract
Background More than two years into the COVID-19 pandemic, most of the population has developed anti-SARS-CoV-2 antibodies from infection and/or vaccination. However, public health decision-making is hindered by the lack of up-to-date and precise characterization of the immune landscape in the population. Here, we estimated anti-SARS-CoV-2 antibodies seroprevalence and cross-variant neutralization capacity after Omicron became dominant in Geneva, Switzerland. Methods We conducted a population-based serosurvey between April 29 and June 9, 2022, recruiting children and adults of all ages from age-stratified random samples of the general population of Geneva, Switzerland. We tested for anti-SARS-CoV-2 antibodies using commercial immunoassays targeting either the spike (S) or nucleocapsid (N) protein, and for antibody neutralization capacity against different SARS-CoV-2 variants using a cell-free Spike trimer-ACE2 binding-based surrogate neutralization assay. We estimated seroprevalence and neutralization capacity using a Bayesian modeling framework accounting for the demographics, vaccination, and infection statuses of the Geneva population. Findings Among the 2521 individuals included in the analysis, the estimated total antibodies seroprevalence was 93.8% (95% CrI 93.1-94.5), including 72.4% (70.0-74.7) for infection-induced antibodies. Estimates of neutralizing antibodies in a representative subsample (N = 1160) ranged from 79.5% (77.1-81.8) against the Alpha variant to 46.7% (43.0-50.4) against the Omicron BA.4/BA.5 subvariants. Despite having high seroprevalence of infection-induced antibodies (76.7% [69.7-83.0] for ages 0-5 years, 90.5% [86.5-94.1] for ages 6-11 years), children aged <12 years had substantially lower neutralizing activity than older participants, particularly against Omicron subvariants. Overall, vaccination was associated with higher neutralizing activity against pre-Omicron variants. Vaccine booster alongside recent infection was associated with higher neutralizing activity against Omicron subvariants. Interpretation While most of the Geneva population has developed anti-SARS-CoV-2 antibodies through vaccination and/or infection, less than half has neutralizing activity against the currently circulating Omicron BA.5 subvariant. Hybrid immunity obtained through booster vaccination and infection confers the greatest neutralization capacity, including against Omicron. Funding General Directorate of Health in Geneva canton, Private Foundation of the Geneva University Hospitals, European Commission ("CoVICIS" grant), and a private foundation advised by CARIGEST SA.
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Affiliation(s)
- María-Eugenia Zaballa
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Javier Perez-Saez
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland,Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Carlos de Mestral
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland,University Centre for General Medicine and Public Health, University of Lausanne, Lausanne, Switzerland
| | - Nick Pullen
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Julien Lamour
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Priscilla Turelli
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Charlène Raclot
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Hélène Baysson
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland,Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Francesco Pennacchio
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Jennifer Villers
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Julien Duc
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Viviane Richard
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Roxane Dumont
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Claire Semaani
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Andrea Jutta Loizeau
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Clément Graindorge
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Elsa Lorthe
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | | | - Didier Pittet
- Department of Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland,Infection Control Program and World Health Organization Collaborating Centre on Patient Safety, Geneva University Hospitals, Geneva, Switzerland
| | - Manuel Schibler
- Division of Laboratory Medicine, Department of Diagnostics, Geneva University Hospitals, Geneva, Switzerland
| | - Nicolas Vuilleumier
- Department of Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland,Division of Laboratory Medicine, Department of Diagnostics, Geneva University Hospitals, Geneva, Switzerland
| | - François Chappuis
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland,Division and Department of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Omar Kherad
- Department of Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland,Division of Internal Medicine, Hôpital de la Tour, Geneva, Switzerland
| | - Andrew S. Azman
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland,Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Klara M. Posfay-Barbe
- Department of Woman, Child, and Adolescent Medicine, Geneva University Hospitals, Geneva, Switzerland,Department of Pediatrics, Gynecology & Obstetrics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Laurent Kaiser
- Department of Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland,Division of Laboratory Medicine, Department of Diagnostics, Geneva University Hospitals, Geneva, Switzerland,Division of Infectious Diseases, Department of Medicine, Geneva University Hospitals, Geneva, Switzerland,Geneva Centre for Emerging Viral Diseases, Geneva University Hospitals, Geneva, Switzerland
| | - Didier Trono
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Silvia Stringhini
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland,University Centre for General Medicine and Public Health, University of Lausanne, Lausanne, Switzerland,Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland,Corresponding author. Division of Primary Care, Geneva University Hospitals, 1205, Geneva, Switzerland
| | - Idris Guessous
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland,Division and Department of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
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8
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Lorthe E, Bellon M, Michielin G, Berthelot J, Zaballa ME, Pennacchio F, Bekliz M, Laubscher F, Arefi F, Perez-Saez J, Azman AS, L’Huillier AG, Posfay-Barbe KM, Kaiser L, Guessous I, Maerkl SJ, Eckerle I, Stringhini S. Epidemiological, virological and serological investigation of a SARS-CoV-2 outbreak (Alpha variant) in a primary school: A prospective longitudinal study. PLoS One 2022; 17:e0272663. [PMID: 35976947 PMCID: PMC9385020 DOI: 10.1371/journal.pone.0272663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 07/24/2022] [Indexed: 11/19/2022] Open
Abstract
Objectives To report a prospective epidemiological, virological and serological investigation of a SARS-CoV-2 outbreak in a primary school. Methods As part of a longitudinal, prospective, school-based surveillance study, this investigation involved repeated testing of 73 pupils, 9 teachers, 13 non-teaching staff and 26 household members of participants who tested positive, with rapid antigen tests and/or RT-PCR (Day 0–2 and Day 5–7), serologies on dried capillary blood samples (Day 0–2 and Day 30), contact tracing interviews and SARS-CoV-2 whole genome sequencing. Results We identified 20 children (aged 4 to 6 years from 4 school classes), 2 teachers and a total of 4 household members who were infected by the Alpha variant during this outbreak. Infection attack rates were between 11.8 and 62.0% among pupils from the 4 school classes, 22.2% among teachers and 0% among non-teaching staff. Secondary attack rate among household members was 15.4%. Symptoms were reported by 63% of infected children, 100% of teachers and 50% of household members. All analysed sequences but one showed 100% identity. Serological tests detected 8 seroconversions unidentified by SARS-CoV-2 virological tests. Conclusions This study confirmed child-to-child and child-to-adult SARS-CoV-2 transmission and introduction into households. Effective measures to limit transmission in schools have the potential to reduce the overall community circulation.
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Affiliation(s)
- Elsa Lorthe
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- * E-mail:
| | - Mathilde Bellon
- Department of Microbiology and Molecular Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Center for Emerging Viral Diseases, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Grégoire Michielin
- Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Julie Berthelot
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - María-Eugenia Zaballa
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Francesco Pennacchio
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Meriem Bekliz
- Department of Microbiology and Molecular Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Florian Laubscher
- Laboratory of Virology, Department of Diagnostics, Geneva University Hospitals, Geneva, Switzerland
| | - Fatemeh Arefi
- Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Javier Perez-Saez
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Andrew S. Azman
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
- Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Arnaud G. L’Huillier
- Laboratory of Virology, Department of Diagnostics, Geneva University Hospitals, Geneva, Switzerland
- Department of Pediatrics, Gynecology & Obstetrics, Pediatric Infectious Disease Unit, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Klara M. Posfay-Barbe
- Department of Pediatrics, Gynecology & Obstetrics, Pediatric Infectious Disease Unit, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Laurent Kaiser
- Center for Emerging Viral Diseases, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
- Division of Infectious Diseases, Department of Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Idris Guessous
- Division of Primary Care, Geneva University Hospitals, Geneva, Switzerland
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Sebastian J. Maerkl
- Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Isabella Eckerle
- Department of Microbiology and Molecular Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Center for Emerging Viral Diseases, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
- Laboratory of Virology, Department of Diagnostics, Geneva University Hospitals, Geneva, Switzerland
- Division of Infectious Diseases, Department of Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Silvia Stringhini
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- University Center for General Medicine and Public Health, University of Lausanne, Lausanne, Switzerland
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9
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Truelove S, Smith CP, Qin M, Mullany LC, Borchering RK, Lessler J, Shea K, Howerton E, Contamin L, Levander J, Kerr J, Hochheiser H, Kinsey M, Tallaksen K, Wilson S, Shin L, Rainwater-Lovett K, Lemairtre JC, Dent J, Kaminsky J, Lee EC, Perez-Saez J, Hill A, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Pastore y Piontti A, Vespignani A, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Orr M, Harrison G, Hurt B, Chen J, Vullikanti A, Marathe M, Hoops S, Bhattacharya P, Machi D, Chen S, Paul R, Janies D, Thill JC, Galanti M, Yamana TK, Pei S, Shaman JL, Healy JM, Slayton RB, Biggerstaff M, Johansson MA, Runge MC, Viboud C. Projected resurgence of COVID-19 in the United States in July-December 2021 resulting from the increased transmissibility of the Delta variant and faltering vaccination. eLife 2022; 11:e73584. [PMID: 35726851 PMCID: PMC9232215 DOI: 10.7554/elife.73584] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 06/03/2022] [Indexed: 01/01/2023] Open
Abstract
In Spring 2021, the highly transmissible SARS-CoV-2 Delta variant began to cause increases in cases, hospitalizations, and deaths in parts of the United States. At the time, with slowed vaccination uptake, this novel variant was expected to increase the risk of pandemic resurgence in the US in summer and fall 2021. As part of the COVID-19 Scenario Modeling Hub, an ensemble of nine mechanistic models produced 6-month scenario projections for July-December 2021 for the United States. These projections estimated substantial resurgences of COVID-19 across the US resulting from the more transmissible Delta variant, projected to occur across most of the US, coinciding with school and business reopening. The scenarios revealed that reaching higher vaccine coverage in July-December 2021 reduced the size and duration of the projected resurgence substantially, with the expected impacts was largely concentrated in a subset of states with lower vaccination coverage. Despite accurate projection of COVID-19 surges occurring and timing, the magnitude was substantially underestimated 2021 by the models compared with the of the reported cases, hospitalizations, and deaths occurring during July-December, highlighting the continued challenges to predict the evolving COVID-19 pandemic. Vaccination uptake remains critical to limiting transmission and disease, particularly in states with lower vaccination coverage. Higher vaccination goals at the onset of the surge of the new variant were estimated to avert over 1.5 million cases and 21,000 deaths, although may have had even greater impacts, considering the underestimated resurgence magnitude from the model.
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Affiliation(s)
- Shaun Truelove
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
| | - Claire P Smith
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
| | - Michelle Qin
- Harvard UniversityCambridge, MassachusettsUnited States
| | - Luke C Mullany
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
- Johns Hopkins University Applied Physics LaboratoryLaurelUnited States
| | | | - Justin Lessler
- University of North Carolina at Chapel HillChapel HillUnited States
| | - Katriona Shea
- Pennsylvania State UniversityUniversity ParkUnited States
| | - Emily Howerton
- Pennsylvania State UniversityUniversity ParkUnited States
| | | | | | | | | | - Matt Kinsey
- Johns Hopkins University Applied Physics LaboratoryLaurelUnited States
| | - Kate Tallaksen
- Johns Hopkins University Applied Physics LaboratoryLaurelUnited States
| | - Shelby Wilson
- Johns Hopkins University Applied Physics LaboratoryLaurelUnited States
| | - Lauren Shin
- Johns Hopkins University Applied Physics LaboratoryLaurelUnited States
| | | | | | - Juan Dent
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
| | - Joshua Kaminsky
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
| | - Elizabeth C Lee
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
| | - Javier Perez-Saez
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
| | - Alison Hill
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
| | | | | | | | - Kunpeng Mu
- Northeastern UniversityBostonUnited States
| | | | | | | | | | | | | | | | - Bryan Lewis
- University of VirginiaCharlottesvilleUnited States
| | - Brian Klahn
- University of VirginiaCharlottesvilleUnited States
| | | | - Mark Orr
- University of VirginiaCharlottesvilleUnited States
| | | | | | | | | | | | - Stefan Hoops
- University of VirginiaCharlottesvilleUnited States
| | | | - Dustin Machi
- University of VirginiaCharlottesvilleUnited States
| | - Shi Chen
- University of North Carolina at CharlotteCharlotteUnited States
| | - Rajib Paul
- University of North Carolina at CharlotteCharlotteUnited States
| | - Daniel Janies
- University of North Carolina at CharlotteCharlotteUnited States
| | | | | | | | - Sen Pei
- Columbia UniversityNew YorkUnited States
| | | | | | | | | | | | | | - Cecile Viboud
- Fogarty International Center, National Institutes of HealthBethesdaUnited States
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10
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Cramer EY, Ray EL, Lopez VK, Bracher J, Brennen A, Castro Rivadeneira AJ, Gerding A, Gneiting T, House KH, Huang Y, Jayawardena D, Kanji AH, Khandelwal A, Le K, Mühlemann A, Niemi J, Shah A, Stark A, Wang Y, Wattanachit N, Zorn MW, Gu Y, Jain S, Bannur N, Deva A, Kulkarni M, Merugu S, Raval A, Shingi S, Tiwari A, White J, Abernethy NF, Woody S, Dahan M, Fox S, Gaither K, Lachmann M, Meyers LA, Scott JG, Tec M, Srivastava A, George GE, Cegan JC, Dettwiller ID, England WP, Farthing MW, Hunter RH, Lafferty B, Linkov I, Mayo ML, Parno MD, Rowland MA, Trump BD, Zhang-James Y, Chen S, Faraone SV, Hess J, Morley CP, Salekin A, Wang D, Corsetti SM, Baer TM, Eisenberg MC, Falb K, Huang Y, Martin ET, McCauley E, Myers RL, Schwarz T, Sheldon D, Gibson GC, Yu R, Gao L, Ma Y, Wu D, Yan X, Jin X, Wang YX, Chen Y, Guo L, Zhao Y, Gu Q, Chen J, Wang L, Xu P, Zhang W, Zou D, Biegel H, Lega J, McConnell S, Nagraj VP, Guertin SL, Hulme-Lowe C, Turner SD, Shi Y, Ban X, Walraven R, Hong QJ, Kong S, van de Walle A, Turtle JA, Ben-Nun M, Riley S, Riley P, Koyluoglu U, DesRoches D, Forli P, Hamory B, Kyriakides C, Leis H, Milliken J, Moloney M, Morgan J, Nirgudkar N, Ozcan G, Piwonka N, Ravi M, Schrader C, Shakhnovich E, Siegel D, Spatz R, Stiefeling C, Wilkinson B, Wong A, Cavany S, España G, Moore S, Oidtman R, Perkins A, Kraus D, Kraus A, Gao Z, Bian J, Cao W, Ferres JL, Li C, Liu TY, Xie X, Zhang S, Zheng S, Vespignani A, Chinazzi M, Davis JT, Mu K, Pastore y Piontti A, Xiong X, Zheng A, Baek J, Farias V, Georgescu A, Levi R, Sinha D, Wilde J, Perakis G, Bennouna MA, Nze-Ndong D, Singhvi D, Spantidakis I, Thayaparan L, Tsiourvas A, Sarker A, Jadbabaie A, Shah D, Della Penna N, Celi LA, Sundar S, Wolfinger R, Osthus D, Castro L, Fairchild G, Michaud I, Karlen D, Kinsey M, Mullany LC, Rainwater-Lovett K, Shin L, Tallaksen K, Wilson S, Lee EC, Dent J, Grantz KH, Hill AL, Kaminsky J, Kaminsky K, Keegan LT, Lauer SA, Lemaitre JC, Lessler J, Meredith HR, Perez-Saez J, Shah S, Smith CP, Truelove SA, Wills J, Marshall M, Gardner L, Nixon K, Burant JC, Wang L, Gao L, Gu Z, Kim M, Li X, Wang G, Wang Y, Yu S, Reiner RC, Barber R, Gakidou E, Hay SI, Lim S, Murray C, Pigott D, Gurung HL, Baccam P, Stage SA, Suchoski BT, Prakash BA, Adhikari B, Cui J, Rodríguez A, Tabassum A, Xie J, Keskinocak P, Asplund J, Baxter A, Oruc BE, Serban N, Arik SO, Dusenberry M, Epshteyn A, Kanal E, Le LT, Li CL, Pfister T, Sava D, Sinha R, Tsai T, Yoder N, Yoon J, Zhang L, Abbott S, Bosse NI, Funk S, Hellewell J, Meakin SR, Sherratt K, Zhou M, Kalantari R, Yamana TK, Pei S, Shaman J, Li ML, Bertsimas D, Lami OS, Soni S, Bouardi HT, Ayer T, Adee M, Chhatwal J, Dalgic OO, Ladd MA, Linas BP, Mueller P, Xiao J, Wang Y, Wang Q, Xie S, Zeng D, Green A, Bien J, Brooks L, Hu AJ, Jahja M, McDonald D, Narasimhan B, Politsch C, Rajanala S, Rumack A, Simon N, Tibshirani RJ, Tibshirani R, Ventura V, Wasserman L, O’Dea EB, Drake JM, Pagano R, Tran QT, Ho LST, Huynh H, Walker JW, Slayton RB, Johansson MA, Biggerstaff M, Reich NG. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States. Proc Natl Acad Sci U S A 2022; 119:e2113561119. [PMID: 35394862 PMCID: PMC9169655 DOI: 10.1073/pnas.2113561119] [Citation(s) in RCA: 87] [Impact Index Per Article: 43.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 01/24/2022] [Indexed: 01/15/2023] Open
Abstract
Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.
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Affiliation(s)
- Estee Y. Cramer
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Evan L. Ray
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Velma K. Lopez
- COVID-19 Response, Centers for Disease Control and Prevention; Atlanta, GA 30333
| | - Johannes Bracher
- Chair of Econometrics and Statistics, Karlsruhe Institute of Technology, 76185 Karlsruhe, Germany
- Computational Statistics Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany
| | | | | | - Aaron Gerding
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Tilmann Gneiting
- Computational Statistics Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany
- Institute of Stochastics, Karlsruhe Institute of Technology, 69118 Karlsruhe, Germany
| | - Katie H. House
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Yuxin Huang
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Dasuni Jayawardena
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Abdul H. Kanji
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Ayush Khandelwal
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Khoa Le
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Anja Mühlemann
- Institute of Mathematical Statistics and Actuarial Science, University of Bern, CH-3012 Bern, Switzerland
| | - Jarad Niemi
- Department of Statistics, Iowa State University, Ames, IA 50011
| | - Apurv Shah
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Ariane Stark
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Yijin Wang
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Nutcha Wattanachit
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Martha W. Zorn
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | | | - Sansiddh Jain
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Nayana Bannur
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Ayush Deva
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Mihir Kulkarni
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Srujana Merugu
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Alpan Raval
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Siddhant Shingi
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Avtansh Tiwari
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Jerome White
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | | | - Spencer Woody
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712
| | - Maytal Dahan
- Texas Advanced Computing Center, Austin, TX 78758
| | - Spencer Fox
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712
| | | | | | - Lauren Ancel Meyers
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712
| | - James G. Scott
- Department of Information, Risk, and Operations Management, University of Texas at Austin, Austin, TX 78712
| | - Mauricio Tec
- Department of Statistics and Data Sciences, University of Texas at Austin, Austin, TX 78712
| | - Ajitesh Srivastava
- Ming Hsieh Department of Computer and Electrical Engineering, University of Southern California, Los Angeles, CA 90089
| | - Glover E. George
- US Army Engineer Research and Development Center, Vicksburg, MS 39180
| | - Jeffrey C. Cegan
- US Army Engineer Research and Development Center, Concord, MA 01742
| | - Ian D. Dettwiller
- US Army Engineer Research and Development Center, Vicksburg, MS 39180
| | | | | | - Robert H. Hunter
- US Army Engineer Research and Development Center, Vicksburg, MS 39180
| | - Brandon Lafferty
- US Army Engineer Research and Development Center, Vicksburg, MS 39180
| | - Igor Linkov
- US Army Engineer Research and Development Center, Concord, MA 01742
| | - Michael L. Mayo
- US Army Engineer Research and Development Center, Vicksburg, MS 39180
| | - Matthew D. Parno
- US Army Engineer Research and Development Center, Hanover, NH 03755
| | | | | | - Yanli Zhang-James
- Department of Psychiatry and Behavioral Sciences, State University of New York Upstate Medical University, Syracuse, NY 13210
| | - Samuel Chen
- School of Medicine, State University of New York Upstate Medical University, Syracuse, NY 13210
| | - Stephen V. Faraone
- Department of Psychiatry and Behavioral Sciences, State University of New York Upstate Medical University, Syracuse, NY 13210
| | - Jonathan Hess
- Department of Psychiatry and Behavioral Sciences, State University of New York Upstate Medical University, Syracuse, NY 13210
| | - Christopher P. Morley
- Department of Public Health & Preventive Medicine, State University of New York Upstate Medical University, Syracuse, NY 13210
| | - Asif Salekin
- Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY 13207
| | - Dongliang Wang
- Department of Public Health & Preventive Medicine, State University of New York Upstate Medical University, Syracuse, NY 13210
| | | | - Thomas M. Baer
- Department of Physics, Trinity University, San Antonio, TX 78212
| | - Marisa C. Eisenberg
- Department of Complex Systems, University of Michigan, Ann Arbor, MI 48109
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109
- School of Public Health, Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109
| | - Karl Falb
- Department of Physics, University of Michigan, Ann Arbor, MI, 48109
| | - Yitao Huang
- Department of Physics, University of Michigan, Ann Arbor, MI, 48109
| | - Emily T. Martin
- School of Public Health, Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109
| | - Ella McCauley
- Department of Physics, University of Michigan, Ann Arbor, MI, 48109
| | - Robert L. Myers
- Department of Physics, University of Michigan, Ann Arbor, MI, 48109
| | - Tom Schwarz
- Department of Physics, University of Michigan, Ann Arbor, MI, 48109
| | - Daniel Sheldon
- College of Information and Computer Sciences, University of Massachusetts, Amherst, MA 01003
| | - Graham Casey Gibson
- School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA 01003
| | - Rose Yu
- Department of Computer Science and Engineering, University of California, San Diego, CA 92093
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115
| | - Liyao Gao
- Department of Statistics, University of Washington, Seattle, WA 98185
| | - Yian Ma
- Halıcıoğlu Data Science Institute, University of California, San Diego, CA 92093
| | - Dongxia Wu
- Department of Computer Science and Engineering, University of California, San Diego, CA 92093
| | - Xifeng Yan
- Department of Computer Science, University of California, Santa Barbara, CA 93106
| | - Xiaoyong Jin
- Department of Computer Science, University of California, Santa Barbara, CA 93106
| | - Yu-Xiang Wang
- Department of Computer Science, University of California, Santa Barbara, CA 93106
| | - YangQuan Chen
- Mechatronics, Embedded Systems and Automation Lab, Department of Mechanical Engineering, University of California, Merced, CA 95301
| | - Lihong Guo
- Jilin University, Changchun City, Jilin Province, 130012, People's Republic of China
| | - Yanting Zhao
- University of Science and Technology of China, Heifei, Anhui, 230027, People's Republic of China
| | - Quanquan Gu
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Jinghui Chen
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Lingxiao Wang
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Pan Xu
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Weitong Zhang
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Difan Zou
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Hannah Biegel
- Department of Mathematics, University of Arizona, Tucson, AZ 85721
| | - Joceline Lega
- Department of Mathematics, University of Arizona, Tucson, AZ 85721
| | | | - V. P. Nagraj
- Quality Assurance and Data Science, Signature Science, LLC, Charlottesville, VA 22911
| | - Stephanie L. Guertin
- Quality Assurance and Data Science, Signature Science, LLC, Charlottesville, VA 22911
| | | | - Stephen D. Turner
- Quality Assurance and Data Science, Signature Science, LLC, Charlottesville, VA 22911
| | - Yunfeng Shi
- Department of Materials Science and Engineering, Rensselaer Polytechnic Institute, Troy, NY 12309
| | - Xuegang Ban
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195
| | | | - Qi-Jun Hong
- School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ 85287
- School of Engineering, Brown University, Providence, RI 02912
| | | | | | - James A. Turtle
- Infectious Disease Group, Predictive Science, Inc, San Diego, CA 92121
| | - Michal Ben-Nun
- Infectious Disease Group, Predictive Science, Inc, San Diego, CA 92121
| | - Steven Riley
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College, W2 1PG London, United Kingdom
| | - Pete Riley
- Infectious Disease Group, Predictive Science, Inc, San Diego, CA 92121
| | | | | | - Pedro Forli
- Oliver Wyman Digital, Oliver Wyman, Sao Paolo, Brazil 04711-904
| | - Bruce Hamory
- Health & Life Sciences, Oliver Wyman, Boston, MA 02110
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- Health & Life Sciences, Oliver Wyman, New York, NY 10036
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- Financial Services, Oliver Wyman, New York, NY 10036
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- Financial Services, Oliver Wyman, New York, NY 10036
| | | | - Gokce Ozcan
- Financial Services, Oliver Wyman, New York, NY 10036
| | - Noah Piwonka
- Health & Life Sciences, Oliver Wyman, New York, NY 10036
| | - Matt Ravi
- Core Consultant Group, Oliver Wyman, New York, NY 10036
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- Health & Life Sciences, Oliver Wyman, New York, NY 10036
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- Financial Services, Oliver Wyman, New York, NY 10036
| | - Ryan Spatz
- Core Consultant Group, Oliver Wyman, New York, NY 10036
| | - Chris Stiefeling
- Financial Services, Oliver Wyman Digital, Toronto, ON, Canada M5J 0A1
| | | | | | - Sean Cavany
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556
| | - Guido España
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556
| | - Sean Moore
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556
| | - Rachel Oidtman
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556
- Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637
| | - Alex Perkins
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556
| | - David Kraus
- Department of Mathematics and Statistics, Masaryk University, 61137 Brno, Czech Republic
| | - Andrea Kraus
- Department of Mathematics and Statistics, Masaryk University, 61137 Brno, Czech Republic
| | | | | | - Wei Cao
- Microsoft, Redmond, WA 98029
| | | | | | | | | | | | | | - Alessandro Vespignani
- Institute for Scientific Interchange Foundation, Turin, 10133, Italy
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Matteo Chinazzi
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Jessica T. Davis
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Kunpeng Mu
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Ana Pastore y Piontti
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Xinyue Xiong
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Andrew Zheng
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Jackie Baek
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Vivek Farias
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02142
| | - Andreea Georgescu
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Retsef Levi
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02142
| | - Deeksha Sinha
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Joshua Wilde
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
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- Technology, Operations and Statistics (TOPS) group, Stern School of Business, New York University, New York, NY 10012
| | | | | | | | - Arnab Sarker
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Ali Jadbabaie
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Devavrat Shah
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Nicolas Della Penna
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Leo A. Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA 02139
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- Statistical Sciences Group, Los Alamos National Laboratory, Los Alamos, NM 87545
| | - Lauren Castro
- Information Systems and Modeling Group, Los Alamos National Laboratory, Los Alamos, NM 87545
| | - Geoffrey Fairchild
- Information Systems and Modeling Group, Los Alamos National Laboratory, Los Alamos, NM 87545
| | - Isaac Michaud
- Statistical Sciences Group, Los Alamos National Laboratory, Los Alamos, NM 87545
| | - Dean Karlen
- Department of Physics and Astronomy, University of Victoria, Victoria, BC, V8W 2Y2, Canada
- Physical Sciences Division, TRIUMF, Vancouver, BC, V8W 2Y2, Canada
| | - Matt Kinsey
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723
| | - Luke C. Mullany
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723
| | | | - Lauren Shin
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723
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- Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723
| | - Elizabeth C. Lee
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Juan Dent
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Kyra H. Grantz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Alison L. Hill
- Institute for Computational Medicine, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21218
| | - Joshua Kaminsky
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | | | - Lindsay T. Keegan
- Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, UT 84108
| | - Stephen A. Lauer
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Joseph C. Lemaitre
- Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Hannah R. Meredith
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Javier Perez-Saez
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Sam Shah
- Unaffiliated, San Francisco, CA 94122
| | - Claire P. Smith
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Shaun A. Truelove
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
- International Vaccine Access Center, Johns Hopkins University, Baltimore, MD 21231
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21231
| | | | - Maximilian Marshall
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218
| | - Lauren Gardner
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218
| | - Kristen Nixon
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218
| | | | - Lily Wang
- Department of Statistics, Iowa State University, Ames, IA 50011
| | - Lei Gao
- Department of Finance, Iowa State University, Ames, IA 50011
| | - Zhiling Gu
- Department of Statistics, Iowa State University, Ames, IA 50011
| | - Myungjin Kim
- Department of Statistics, Iowa State University, Ames, IA 50011
| | - Xinyi Li
- School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC 29634
| | - Guannan Wang
- Department of Mathematics, College of William & Mary, Williamsburg, VA 23187
| | - Yueying Wang
- Department of Statistics, Iowa State University, Ames, IA 50011
| | - Shan Yu
- Department of Statistics, University of Virginia, Charlottesville, VA 22904
| | - Robert C. Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - Ryan Barber
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - Emmanuela Gakidou
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - Simon I. Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - Steve Lim
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - Chris Murray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - David Pigott
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | | | | | | | | | - B. Aditya Prakash
- College of Computing, Georgia Institute of Technology, Atlanta, GA 30308
| | - Bijaya Adhikari
- Department of Computer Science, University of Iowa, Iowa City, IA 52242
| | - Jiaming Cui
- College of Computing, Georgia Institute of Technology, Atlanta, GA 30308
| | | | - Anika Tabassum
- Department of Computer Science, Virginia Tech, Falls Church, VA 22043
| | - Jiajia Xie
- College of Computing, Georgia Institute of Technology, Atlanta, GA 30308
| | - Pinar Keskinocak
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
| | - John Asplund
- Advanced Data Analytics, Metron, Inc., Reston, VA 20190
| | - Arden Baxter
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
| | - Buse Eylul Oruc
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
| | - Nicoleta Serban
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
| | | | | | | | | | | | | | | | | | | | - Thomas Tsai
- Department of Health Policy and Management, Harvard University, Cambridge, MA 02138
| | | | | | | | - Sam Abbott
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Nikos I. Bosse
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Sebastian Funk
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Joel Hellewell
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Sophie R. Meakin
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Katharine Sherratt
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Mingyuan Zhou
- McCombs School of Business, The University of Texas at Austin, Austin, TX 78712
| | - Rahi Kalantari
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712
| | - Teresa K. Yamana
- Department of Environmental Health Sciences, Columbia University, New York, NY 10032
| | - Sen Pei
- Department of Environmental Health Sciences, Columbia University, New York, NY 10032
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Columbia University, New York, NY 10032
| | - Michael L. Li
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Dimitris Bertsimas
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02142
| | - Omar Skali Lami
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Saksham Soni
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Hamza Tazi Bouardi
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Turgay Ayer
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
- Winship Cancer Institute, Emory University Medical School, Atlanta, GA 30322
| | - Madeline Adee
- Radiology-Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114
| | - Jagpreet Chhatwal
- Radiology-Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114
| | - Ozden O. Dalgic
- Health Economic Modeling, Value Analytics Labs, 34776 İstanbul, Turkey
| | - Mary A. Ladd
- Radiology-Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114
| | - Benjamin P. Linas
- Department of Medicine, Section of Infectious Diseases, Boston University School of Medicine, Boston, MA 02118
| | - Peter Mueller
- Radiology-Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114
| | - Jade Xiao
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
| | - Yuanjia Wang
- Department of Biostatistics, Columbia University, New York, NY 10032
- Department of Psychiatry, Columbia University, New York, NY 10032
| | - Qinxia Wang
- Department of Biostatistics, Columbia University, New York, NY 10032
| | - Shanghong Xie
- Department of Biostatistics, Columbia University, New York, NY 10032
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - Alden Green
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Jacob Bien
- Marshall School of Business, Department of Data Sciences and Operations (DSO), University of Southern California, Los Angeles, CA 90089
| | - Logan Brooks
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Addison J. Hu
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Maria Jahja
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Daniel McDonald
- Department of Statistics, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Balasubramanian Narasimhan
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA 94305
- Department of Statistics, Stanford University, Stanford, CA 94305
| | - Collin Politsch
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Samyak Rajanala
- Department of Statistics, Stanford University, Stanford, CA 94305
| | - Aaron Rumack
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Noah Simon
- Department of Biostatistics, University of Washington, Seattle, WA 98195
| | - Ryan J. Tibshirani
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Rob Tibshirani
- Department of Statistics, Stanford University, Stanford, CA 94305
| | - Valerie Ventura
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Larry Wasserman
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Eamon B. O’Dea
- Odum School of Ecology, University of Georgia, Athens, GA 30602
| | - John M. Drake
- Odum School of Ecology, University of Georgia, Athens, GA 30602
| | | | - Quoc T. Tran
- Catalog Data Science, Walmart Inc., Sunnyvale, CA 94085
| | - Lam Si Tung Ho
- Department of Mathematics and Statistics, Dalhousie University, Halifax, NS, B3H 4R2, Canada
| | - Huong Huynh
- Virtual Power System Inc, Milpitas, CA 95035
| | - Jo W. Walker
- COVID-19 Response, Centers for Disease Control and Prevention; Atlanta, GA 30333
| | - Rachel B. Slayton
- COVID-19 Response, Centers for Disease Control and Prevention; Atlanta, GA 30333
| | - Michael A. Johansson
- COVID-19 Response, Centers for Disease Control and Prevention; Atlanta, GA 30333
| | - Matthew Biggerstaff
- COVID-19 Response, Centers for Disease Control and Prevention; Atlanta, GA 30333
| | - Nicholas G. Reich
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
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11
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Perez-Saez J, Lessler J, Lee EC, Luquero FJ, Malembaka EB, Finger F, Langa JP, Yennan S, Zaitchik B, Azman AS. The seasonality of cholera in sub-Saharan Africa: a statistical modelling study. The Lancet Global Health 2022; 10:e831-e839. [PMID: 35461521 PMCID: PMC9090905 DOI: 10.1016/s2214-109x(22)00007-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 01/04/2022] [Indexed: 11/30/2022] Open
Abstract
Background Cholera remains a major threat in sub-Saharan Africa (SSA), where some of the highest case-fatality rates are reported. Knowing in what months and where cholera tends to occur across the continent could aid in improving efforts to eliminate cholera as a public health concern. However, largely due to the absence of unified large-scale datasets, no continent-wide estimates exist. In this study, we aimed to estimate cholera seasonality across SSA and explore the correlation between hydroclimatic variables and cholera seasonality. Methods Using the global cholera database of the Global Task Force on Cholera Control, we developed statistical models to synthesise data across spatial and temporal scales to infer the seasonality of excess (defined as incidence higher than the 2010–16 mean incidence rate) suspected cholera occurrence in SSA. We developed a Bayesian statistical model to infer the monthly risk of excess cholera at the first and second administrative levels. Seasonality patterns were then grouped into spatial clusters. Finally, we studied the association between seasonality estimates and hydroclimatic variables (mean monthly fraction of area flooded, mean monthly air temperature, and cumulative monthly precipitation). Findings 24 (71%) of the 34 countries studied had seasonal patterns of excess cholera risk, corresponding to approximately 86% of the SSA population. 12 (50%) of these 24 countries also had subnational differences in seasonality patterns, with strong differences in seasonality strength between regions. Seasonality patterns clustered into two macroregions (west Africa and the Sahel vs eastern and southern Africa), which were composed of subregional clusters with varying degrees of seasonality. Exploratory association analysis found most consistent and positive correlations between cholera seasonality and precipitation and, to a lesser extent, between cholera seasonality and temperature and flooding. Interpretation Widespread cholera seasonality in SSA offers opportunities for intervention planning. Further studies are needed to study the association between cholera and climate. Funding US National Aeronautics and Space Administration Applied Sciences Program and the Bill & Melinda Gates Foundation.
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Affiliation(s)
- Javier Perez-Saez
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA; Unité d'Épidémiologie Populationnelle, Geneva University Hospitals, Geneva, Switzerland
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA; Department of Epidemiology, Gillings School of Global Public Health, and University of North Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Elizabeth C Lee
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | | | - Espoir Bwenge Malembaka
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA; Center for Tropical Diseases and Global Health, Université Catholique de Bukavu, Bukavu, Democratic Republic of the Congo
| | | | | | - Sebastian Yennan
- Surveillance and Epidemiology, Nigeria Centre for Disease Control, Abuja, Nigeria
| | - Benjamin Zaitchik
- Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Andrew S Azman
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA; Unité d'Épidémiologie Populationnelle, Geneva University Hospitals, Geneva, Switzerland; Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland.
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12
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Richard A, Wisniak A, Perez-Saez J, Garrison-Desany H, Petrovic D, Piumatti G, Baysson H, Picazio A, Pennacchio F, De Ridder D, Chappuis F, Vuilleumier N, Low N, Hurst S, Eckerle I, Flahault A, Kaiser L, Azman AS, Guessous I, Stringhini S. Seroprevalence of anti-SARS-CoV-2 IgG antibodies, risk factors for infection and associated symptoms in Geneva, Switzerland: a population-based study. Scand J Public Health 2022; 50:124-135. [PMID: 34664529 PMCID: PMC8808008 DOI: 10.1177/14034948211048050] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 07/08/2021] [Accepted: 08/23/2021] [Indexed: 01/08/2023]
Abstract
Aims: To assess SARS-CoV-2 seroprevalence over the first epidemic wave in the canton of Geneva, Switzerland, as well as risk factors for infection and symptoms associated with IgG seropositivity. Methods: Between April and June 2020, former participants of a representative survey of the 20-74-year-old population of canton Geneva were invited to participate in the study, along with household members aged over 5 years. Blood samples were tested for anti-SARS-CoV-2 immunoglobulin G. Questionnaires were self-administered. We estimated seroprevalence with a Bayesian model accounting for test performance and sampling design. Results: We included 8344 participants, with an overall adjusted seroprevalence of 7.8% (95% credible interval 6.8-8.9). Seroprevalence was highest among 18-49 year-olds (9.5%), and lowest in 5-9-year-old children (4.3%) and individuals >65 years (4.7-5.4%). Odds of seropositivity were significantly reduced for female retirees and unemployed men compared to employed individuals, and smokers compared to non-smokers. We found no significant association between occupation, level of education, neighborhood income and the risk of being seropositive. The symptom most strongly associated with seropositivity was anosmia/dysgeusia. Conclusions: Anti-SARS-CoV-2 population seroprevalence remained low after the first wave in Geneva. Socioeconomic factors were not associated with seropositivity in this sample. The elderly, young children and smokers were less frequently seropositive, although it is not clear how biology and behaviours shape these differences.
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Affiliation(s)
- Aude Richard
- Division of Primary Care, Geneva University Hospitals, Switzerland
- Institute of Global Health, University of Geneva, Switzerland
| | - Ania Wisniak
- Division of Primary Care, Geneva University Hospitals, Switzerland
- Institute of Global Health, University of Geneva, Switzerland
| | - Javier Perez-Saez
- Institute of Global Health, University of Geneva, Switzerland
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, USA
| | | | - Dusan Petrovic
- Division of Primary Care, Geneva University Hospitals, Switzerland
- University Centre for General Medicine and Public Health (UNISANTE), University of Lausanne, Switzerland
| | - Giovanni Piumatti
- Division of Primary Care, Geneva University Hospitals, Switzerland
- Faculty of BioMedicine, Università della Svizzera Italiana, Switzerland
| | - Hélène Baysson
- Division of Primary Care, Geneva University Hospitals, Switzerland
- Department of Health and Community Medicine, University of Geneva, Switzerland
| | - Attilio Picazio
- Division of Primary Care, Geneva University Hospitals, Switzerland
| | | | - David De Ridder
- Division of Primary Care, Geneva University Hospitals, Switzerland
- Department of Health and Community Medicine, University of Geneva, Switzerland
| | - François Chappuis
- Department of Health and Community Medicine, University of Geneva, Switzerland
- Division of Tropical and Humanitarian Medicine, Geneva University Hospitals, Switzerland
| | - Nicolas Vuilleumier
- Division of Laboratory Medicine, Geneva University Hospitals, Switzerland
- Department of Medicine, University of Geneva, Switzerland
| | - Nicola Low
- Institute of Social and Preventive Medicine, University of Bern, Switzerland
| | - Samia Hurst
- Institute for Ethics, History, and the Humanities, University of Geneva, Switzerland
| | - Isabella Eckerle
- Geneva Center for Emerging Viral Diseases and Laboratory of Virology, Geneva University Hospitals, Switzerland
- Department of Microbiology and Molecular Medicine, University of Geneva, Switzerland
| | - Antoine Flahault
- Institute of Global Health, University of Geneva, Switzerland
- Department of Health and Community Medicine, University of Geneva, Switzerland
- Division of Tropical and Humanitarian Medicine, Geneva University Hospitals, Switzerland
| | - Laurent Kaiser
- Department of Medicine, University of Geneva, Switzerland
- Geneva Center for Emerging Viral Diseases and Laboratory of Virology, Geneva University Hospitals, Switzerland
- Division of Infectious Diseases, Geneva University Hospitals, Switzerland
| | - Andrew S. Azman
- Institute of Global Health, University of Geneva, Switzerland
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, USA
| | - Idris Guessous
- Division of Primary Care, Geneva University Hospitals, Switzerland
- Department of Health and Community Medicine, University of Geneva, Switzerland
| | - Silvia Stringhini
- Division of Primary Care, Geneva University Hospitals, Switzerland
- University Centre for General Medicine and Public Health (UNISANTE), University of Lausanne, Switzerland
- Department of Health and Community Medicine, University of Geneva, Switzerland
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13
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Perez-Saez J, Lee EC, Wada NI, Alqunaibet AM, Almudarra SS, Alsukait RF, Dong D, Zhang Y, El Saharty S, Herbst CH, Lessler J. Effect of non-pharmaceutical interventions in the early phase of the COVID-19 epidemic in Saudi Arabia. PLOS Glob Public Health 2022; 2:e0000237. [PMID: 36962205 PMCID: PMC10021433 DOI: 10.1371/journal.pgph.0000237] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 04/13/2022] [Indexed: 11/19/2022]
Abstract
Non-pharmaceutical interventions have been widely employed to control the COVID-19 pandemic. Their associated effect on SARS-CoV-2 transmission have however been unequally studied across regions. Few studies have focused on the Gulf states despite their potential role for global pandemic spread, in particular in the Kingdom of Saudi Arabia through religious pilgrimages. We study the association between NPIs and SARS-CoV-2 transmission in the Kingdom of Saudi Arabia during the first pandemic wave between March and October 2020. We infer associations between NPIs introduction and lifting through a spatial SEIR-type model that allows for inferences of region-specific changes in transmission intensity. We find that reductions in transmission were associated with NPIs implemented shortly after the first reported case including Isolate and Test with School Closure (region-level mean estimates of the reduction in R0 ranged from 25-41%), Curfew (20-70% reduction), and Lockdown (50-60% reduction), although uncertainty in the estimates was high, particularly for the Isolate and Test with School Closure NPI (95% Credible Intervals from 1% to 73% across regions). Transmission was found to increase progressively in most regions during the last part of NPI relaxation phases. These results can help informing the policy makers in the planning of NPI scenarios as the pandemic evolves with the emergence of SARS-CoV-2 variants and the availability of vaccination.
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Affiliation(s)
- Javier Perez-Saez
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Elizabeth C Lee
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Nikolas I Wada
- Johns Hopkins Novel Coronavirus Research Compendium, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | | | | | - Reem F Alsukait
- Community Health Sciences, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia
- Health, Nutrition and Population Global Practice, World Bank Group, Washington DC, United States of America
| | - Di Dong
- Health, Nutrition and Population Global Practice, World Bank Group, Washington DC, United States of America
| | - Yi Zhang
- Health, Nutrition and Population Global Practice, World Bank Group, Washington DC, United States of America
| | - Sameh El Saharty
- Health, Nutrition and Population Global Practice, World Bank Group, Washington DC, United States of America
| | - Christopher H Herbst
- Health, Nutrition and Population Global Practice, World Bank Group, Washington DC, United States of America
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, United States of America
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Abstract
BACKGROUND The economic impact of schistosomiasis and the underlying tradeoffs between water resources development and public health concerns have yet to be quantified. Schistosomiasis exerts large health, social and financial burdens on infected individuals and households. While irrigation schemes are one of the most important policy responses designed to reduce poverty, particularly in sub-Saharan Africa, they facilitate the propagation of schistosomiasis and other diseases. METHODS We estimate the economic impact of schistosomiasis in Burkina Faso via its effect on agricultural production. We create an original dataset that combines detailed household and agricultural surveys with high-resolution geo-statistical disease maps. We develop new methods that use the densities of the intermediate host snails of schistosomiasis as instrumental variables together with panel, spatial and machine learning techniques. RESULTS We estimate that the elimination of schistosomiasis in Burkina Faso would increase average crop yields by around 7%, rising to 32% for high infection clusters. Keeping schistosomiasis unchecked, in turn, would correspond to a loss of gross domestic product of approximately 0.8%. We identify the disease burden as a shock to the agricultural productivity of farmers. The poorest households engaged in subsistence agriculture bear a far heavier disease burden than their wealthier counterparts, experiencing an average yield loss due to schistosomiasis of between 32 and 45%. We show that the returns to water resources development are substantially reduced once its health effects are taken into account: villages in proximity of large-scale dams suffer an average yield loss of around 20%, and this burden decreases as distance between dams and villages increases. CONCLUSIONS This study provides a rigorous estimation of how schistosomiasis affects agricultural production and how it is both a driver and a consequence of poverty. It further quantifies the tradeoff between the economics of water infrastructures and their impact on public health. Although we focus on Burkina Faso, our approach can be applied to any country in which schistosomiasis is endemic.
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Affiliation(s)
- Daniele Rinaldo
- Department of Economics and Land, Environment, Economics and Policy Institute (LEEP), University of Exeter, Exeter, England.
| | - Javier Perez-Saez
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Penelope Vounatsou
- Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland
| | - Jürg Utzinger
- Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland
| | - Jean-Louis Arcand
- Department of International Economics, The Graduate Institute of International and Development Studies, Geneva, Switzerland.,Fondation pour les études et recherches sur le développement international (FERDI), Clermont-Ferrand, France
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15
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Perez-Saez J, Zaballa ME, Yerly S, Andrey DO, Meyer B, Eckerle I, Balavoine JF, Chappuis F, Pittet D, Trono D, Kherad O, Vuilleumier N, Kaiser L, Guessous I, Stringhini S, Azman AS. Persistence of anti-SARS-CoV-2 antibodies: immunoassay heterogeneity and implications for serosurveillance. Clin Microbiol Infect 2021; 27:1695.e7-1695.e12. [PMID: 34245905 PMCID: PMC8261139 DOI: 10.1016/j.cmi.2021.06.040] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 06/17/2021] [Accepted: 06/26/2021] [Indexed: 12/13/2022]
Abstract
OBJECTIVES Serological studies have been critical in tracking the evolution of the COVID-19 pandemic. Data on anti-SARS-CoV-2 antibodies persistence remain sparse, especially from infected individuals with few to no symptoms. The objective of the study was to quantify the sensitivity for detecting historic SARS-CoV-2 infections as a function of time since infection for three commercially available SARS-CoV-2 immunoassays and to explore the implications of decaying immunoassay sensitivity in estimating seroprevalence. METHODS We followed a cohort of mostly mild/asymptomatic SARS-CoV-2-infected individuals (n = 354) at least 8 months after their presumed infection date and tested their serum for anti-SARS-CoV-2 antibodies with three commercially available assays: Roche-N, Roche-RBD and EuroImmun-S1. We developed a latent class statistical model to infer the specificity and time-varying sensitivity of each assay and show through simulations how inappropriately accounting for test performance can lead to biased serosurvey estimates. RESULTS Antibodies were detected at follow-up in 74-100% of participants, depending on immunoassays. Both Roche assays maintain high sensitivity, with the EuroImmun assay missing 40% of infections after 9 months. Simulations reveal that without appropriate adjustment for time-varying assay sensitivity, seroprevalence surveys may underestimate infection rates. DISCUSSION Antibodies persist for at least 8 months after infection in a cohort of mildly infected individuals with detection depending on assay choice. Appropriate assay performance adjustment is important for the interpretation of serological studies in the case of diminishing sensitivity after infection.
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Affiliation(s)
- Javier Perez-Saez
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA; Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - María-Eugenia Zaballa
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Sabine Yerly
- Laboratory of Virology, Department of Diagnostics, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Diego O Andrey
- Division of Laboratory Medicine, Department of Diagnostics, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland; Division of Infectious Diseases, Department of Medicine, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Benjamin Meyer
- Centre for Vaccinology, Department of Pathology and Immunology, University of Geneva, Geneva, Switzerland
| | - Isabella Eckerle
- Laboratory of Virology, Department of Diagnostics, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland; Division of Infectious Diseases, Department of Medicine, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland; Geneva Centre for Emerging Viral Diseases, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | | | - François Chappuis
- Division and Department of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Didier Pittet
- Infection Control Program and World Health Organization Collaborating Center on Patient Safety, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Didier Trono
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Omar Kherad
- Division of Internal Medicine, Hôpital de la Tour and Faculty of Medicine, Geneva, Switzerland
| | - Nicolas Vuilleumier
- Division of Laboratory Medicine, Department of Diagnostics, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland; Department of Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Laurent Kaiser
- Laboratory of Virology, Department of Diagnostics, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland; Division of Infectious Diseases, Department of Medicine, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland; Geneva Centre for Emerging Viral Diseases, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Idris Guessous
- Division and Department of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland; Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Silvia Stringhini
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland; Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland; University Center for General Medicine and Public Health, University of Lausanne, Lausanne, Switzerland
| | - Andrew S Azman
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA; Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland.
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16
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Stringhini S, Zaballa ME, Pullen N, Perez-Saez J, de Mestral C, Loizeau AJ, Lamour J, Pennacchio F, Wisniak A, Dumont R, Baysson H, Richard V, Lorthe E, Semaani C, Balavoine JF, Pittet D, Vuilleumier N, Chappuis F, Kherad O, Azman AS, Posfay-Barbe K, Kaiser L, Guessous I. Seroprevalence of anti-SARS-CoV-2 antibodies 6 months into the vaccination campaign in Geneva, Switzerland, 1 June to 7 July 2021. Euro Surveill 2021; 26:2100830. [PMID: 34713799 PMCID: PMC8555371 DOI: 10.2807/1560-7917.es.2021.26.43.2100830] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 10/08/2021] [Indexed: 11/20/2022] Open
Abstract
BackgroundUp-to-date seroprevalence estimates are critical to describe the SARS-CoV-2 immune landscape and to guide public health decisions.AimWe estimate seroprevalence of anti-SARS-CoV-2 antibodies 15 months into the COVID-19 pandemic and 6 months into the vaccination campaign.MethodsWe conducted a population-based cross-sectional serosurvey between 1 June and 7 July 2021, recruiting participants from age- and sex-stratified random samples of the general population. We tested participants for anti-SARS-CoV-2 antibodies targeting the spike (S) or nucleocapsid (N) proteins using the Roche Elecsys immunoassays. We estimated the anti-SARS-CoV-2 antibodies seroprevalence following vaccination and/or infection (anti-S antibodies), or infection only (anti-N antibodies).ResultsAmong 3,355 individuals (54.1% women; 20.8% aged < 18 years and 13.4% aged ≥ 65 years), 2,161 (64.4%) had anti-S antibodies and 906 (27.0%) had anti-N antibodies. The total seroprevalence was 66.1% (95% credible interval (CrI): 64.1-68.0). We estimated that 29.9% (95% Crl: 28.0-31.9) of the population developed antibodies after infection; the rest having developed antibodies via vaccination. Seroprevalence estimates differed markedly across age groups, being lowest among children aged 0-5 years (20.8%; 95% Crl: 15.5-26.7) and highest among older adults aged ≥ 75 years (93.1%; 95% Crl: 89.6-96.0). Seroprevalence of antibodies developed via infection and/or vaccination was higher among participants with higher educational level.ConclusionMost of the population has developed anti-SARS-CoV-2 antibodies, despite most teenagers and children remaining vulnerable to infection. As the SARS-CoV-2 Delta variant spreads and vaccination rates stagnate, efforts are needed to address vaccine hesitancy, particularly among younger individuals and to minimise spread among children.
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Affiliation(s)
- Silvia Stringhini
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- University Centre for General Medicine and Public Health, University of Lausanne, Lausanne, Switzerland
| | - María-Eugenia Zaballa
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Nick Pullen
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Javier Perez-Saez
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States
- Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Carlos de Mestral
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- University Centre for General Medicine and Public Health, University of Lausanne, Lausanne, Switzerland
| | - Andrea Jutta Loizeau
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Julien Lamour
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Francesco Pennacchio
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Ania Wisniak
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Roxane Dumont
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Hélène Baysson
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Viviane Richard
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Elsa Lorthe
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Claire Semaani
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | | | - Didier Pittet
- Department of Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Infection Control Program and World Health Organization Collaborating Centre on Patient Safety, Geneva University Hospitals, Geneva, Switzerland
| | - Nicolas Vuilleumier
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Laboratory Medicine, Geneva University Hospitals, Geneva, Switzerland
- Department of Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - François Chappuis
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division and Department of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Omar Kherad
- Division of Internal Medicine, Hôpital de la Tour, Geneva, Switzerland
- Department of Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Andrew S Azman
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States
- Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Klara Posfay-Barbe
- Department of Woman, Child, and Adolescent Medicine, Geneva University Hospitals, Geneva, Switzerland
- Department of Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Laurent Kaiser
- Geneva Centre for Emerging Viral Diseases and Laboratory Virology, Geneva University Hospitals, Geneva, Switzerland
- Division of Laboratory Medicine, Geneva University Hospitals, Geneva, Switzerland
- Department of Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Idris Guessous
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division and Department of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
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17
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Meredith HR, Giles JR, Perez-Saez J, Mande T, Rinaldo A, Mutembo S, Kabalo EN, Makungo K, Buckee CO, Tatem AJ, Metcalf CJE, Wesolowski A. Characterizing human mobility patterns in rural settings of sub-Saharan Africa. eLife 2021; 10:e68441. [PMID: 34533456 PMCID: PMC8448534 DOI: 10.7554/elife.68441] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 08/21/2021] [Indexed: 11/27/2022] Open
Abstract
Human mobility is a core component of human behavior and its quantification is critical for understanding its impact on infectious disease transmission, traffic forecasting, access to resources and care, intervention strategies, and migratory flows. When mobility data are limited, spatial interaction models have been widely used to estimate human travel, but have not been extensively validated in low- and middle-income settings. Geographic, sociodemographic, and infrastructure differences may impact the ability for models to capture these patterns, particularly in rural settings. Here, we analyzed mobility patterns inferred from mobile phone data in four Sub-Saharan African countries to investigate the ability for variants on gravity and radiation models to estimate travel. Adjusting the gravity model such that parameters were fit to different trip types, including travel between more or less populated areas and/or different regions, improved model fit in all four countries. This suggests that alternative models may be more useful in these settings and better able to capture the range of mobility patterns observed.
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Affiliation(s)
- Hannah R Meredith
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
| | - John R Giles
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
| | - Javier Perez-Saez
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
| | - Théophile Mande
- Bureau d'Etudes Scientifiques et Techniques - Eau, Energie, Environnement (BEST-3E), Ouagadougou, Burkina Faso
| | - Andrea Rinaldo
- Dipartimento di Ingegneria Civile Edile ed Ambientale, Università di Padova, Padova, Italy
- Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Simon Mutembo
- Department of International Health, International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
- Macha Research Trust, Choma, Zambia
| | - Elliot N Kabalo
- Zambia Information and Communications Technology Authority, Lusaka, Zambia
| | | | - Caroline O Buckee
- Department of Epidemiology and the Center for Communicable Disease Dynamics, Harvard TH Chan School of Public Health, Boston, United States
| | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, United Kingdom
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology and the Princeton School of Public and International Affairs, Princeton University, Princeton, United States
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
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18
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Truelove S, Smith CP, Qin M, Mullany LC, Borchering RK, Lessler J, Shea K, Howerton E, Contamin L, Levander J, Salerno J, Hochheiser H, Kinsey M, Tallaksen K, Wilson S, Shin L, Rainwater-Lovett K, Lemaitre JC, Dent J, Kaminsky J, Lee EC, Perez-Saez J, Hill A, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Piontti APY, Vespignani A, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Schlitt J, Corbett P, Telionis PA, Wang L, Peddireddy AS, Hurt B, Chen J, Vullikanti A, Marathe M, Hoops S, Bhattacharya P, Machi D, Chen S, Paul R, Janies D, Thill JC, Galanti M, Yamana T, Pei S, Shaman J, Reich NG, Healy JM, Slayton RB, Biggerstaff M, Johansson MA, Runge MC, Viboud C. Projected resurgence of COVID-19 in the United States in July-December 2021 resulting from the increased transmissibility of the Delta variant and faltering vaccination. medRxiv 2021:2021.08.28.21262748. [PMID: 34494030 PMCID: PMC8423228 DOI: 10.1101/2021.08.28.21262748] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
WHAT IS ALREADY KNOWN ABOUT THIS TOPIC? The highly transmissible SARS-CoV-2 Delta variant has begun to cause increases in cases, hospitalizations, and deaths in parts of the United States. With slowed vaccination uptake, this novel variant is expected to increase the risk of pandemic resurgence in the US in July-December 2021. WHAT IS ADDED BY THIS REPORT? Data from nine mechanistic models project substantial resurgences of COVID-19 across the US resulting from the more transmissible Delta variant. These resurgences, which have now been observed in most states, were projected to occur across most of the US, coinciding with school and business reopening. Reaching higher vaccine coverage in July-December 2021 reduces the size and duration of the projected resurgence substantially. The expected impact of the outbreak is largely concentrated in a subset of states with lower vaccination coverage. WHAT ARE THE IMPLICATIONS FOR PUBLIC HEALTH PRACTICE? Renewed efforts to increase vaccination uptake are critical to limiting transmission and disease, particularly in states with lower current vaccination coverage. Reaching higher vaccination goals in the coming months can potentially avert 1.5 million cases and 21,000 deaths and improve the ability to safely resume social contacts, and educational and business activities. Continued or renewed non-pharmaceutical interventions, including masking, can also help limit transmission, particularly as schools and businesses reopen.
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Affiliation(s)
- Shaun Truelove
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Claire P Smith
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | | | - Luke C Mullany
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Johns Hopkins University Applied Physics Laboratories, Laurel, Maryland
| | | | | | - Katriona Shea
- The Pennsylvania State University, State College, Pennsylvania
| | - Emily Howerton
- The Pennsylvania State University, State College, Pennsylvania
| | | | | | | | | | - Matt Kinsey
- Johns Hopkins University Applied Physics Laboratories, Laurel, Maryland
| | - Kate Tallaksen
- Johns Hopkins University Applied Physics Laboratories, Laurel, Maryland
| | - Shelby Wilson
- Johns Hopkins University Applied Physics Laboratories, Laurel, Maryland
| | - Lauren Shin
- Johns Hopkins University Applied Physics Laboratories, Laurel, Maryland
| | | | | | - Juan Dent
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Joshua Kaminsky
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Elizabeth C Lee
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Javier Perez-Saez
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Alison Hill
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Dean Karlen
- University of Victoria, Victoria, British Columbia, Canada
| | | | | | - Kunpeng Mu
- Northeastern University, Boston, Massachusetts
| | | | | | | | | | | | | | | | - Bryan Lewis
- University of Virginia, Charlottesville, Virginia
| | - Brian Klahn
- University of Virginia, Charlottesville, Virginia
| | | | | | | | | | - Lijing Wang
- University of Virginia, Charlottesville, Virginia
| | | | | | | | | | | | - Stefan Hoops
- University of Virginia, Charlottesville, Virginia
| | | | - Dustin Machi
- University of Virginia, Charlottesville, Virginia
| | - Shi Chen
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | - Rajib Paul
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | - Daniel Janies
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | - Jean-Claude Thill
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | | | | | | | | | | | | | | | | | | | | | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland
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19
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Stringhini S, Zaballa ME, Pullen N, de Mestral C, Perez-Saez J, Dumont R, Picazio A, Pennacchio F, Dibner Y, Yerly S, Baysson H, Vuilleumier N, Balavoine JF, Bachmann D, Trono D, Pittet D, Chappuis F, Kherad O, Kaiser L, Azman AS, Guessous I. Large variation in anti-SARS-CoV-2 antibody prevalence among essential workers in Geneva, Switzerland. Nat Commun 2021; 12:3455. [PMID: 34103517 PMCID: PMC8187639 DOI: 10.1038/s41467-021-23796-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 05/10/2021] [Indexed: 01/24/2023] Open
Abstract
Limited data exist on SARS-CoV-2 infection rates across sectors and occupations, hindering our ability to make rational policy, including vaccination prioritization, to protect workers and limit SARS-CoV-2 spread. Here, we present results from our SEROCoV-WORK + study, a serosurvey of workers recruited after the first wave of the COVID-19 pandemic in Geneva, Switzerland. We tested workers (May 18-September 18, 2020) from 16 sectors and 32 occupations for anti-SARS-CoV-2 IgG antibodies. Of 10,513 participants, 1026 (9.8%) tested positive. The seropositivity rate ranged from 4.2% in the media sector to 14.3% in the nursing home sector. We found considerable within-sector variability: nursing home (0%-31.4%), homecare (3.9%-12.6%), healthcare (0%-23.5%), public administration (2.6%-24.6%), and public security (0%-16.7%). Seropositivity rates also varied across occupations, from 15.0% among kitchen staff and 14.4% among nurses, to 5.4% among domestic care workers and 2.8% among journalists. Our findings show that seropositivity rates varied widely across sectors, between facilities within sectors, and across occupations, reflecting a higher exposure in certain sectors and occupations.
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Affiliation(s)
- Silvia Stringhini
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland.
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland.
- University Center for General Medicine and Public Health, University of Lausanne, Geneva, Switzerland.
| | - María-Eugenia Zaballa
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Nick Pullen
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Carlos de Mestral
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- University Center for General Medicine and Public Health, University of Lausanne, Geneva, Switzerland
| | - Javier Perez-Saez
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Roxane Dumont
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Attilio Picazio
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Francesco Pennacchio
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Yaron Dibner
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Sabine Yerly
- Division of Laboratory Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Helene Baysson
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Nicolas Vuilleumier
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Laboratory Medicine, Geneva University Hospitals, Geneva, Switzerland
- Department of Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | | | - Delphine Bachmann
- Hirslanden Clinique des Grangettes and Hirslanden Clinique La Colline, Geneva, Switzerland
| | - Didier Trono
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Didier Pittet
- Infection Control Program and World Health Organization Collaborating Center on Patient Safety, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - François Chappuis
- Division and Department of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Omar Kherad
- Division of Internal Medicine, Hôpital de la Tour and Faculty of Medicine, Geneva, Switzerland
| | - Laurent Kaiser
- Division of Laboratory Medicine, Geneva University Hospitals, Geneva, Switzerland
- Department of Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Geneva Center for Emerging Viral Diseases and Laboratory Virology, Geneva University Hospitals, Geneva, Switzerland
| | - Andrew S Azman
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Idris Guessous
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division and Department of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
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20
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Kerr GH, Badr HS, Gardner LM, Perez-Saez J, Zaitchik BF. Associations between meteorology and COVID-19 in early studies: Inconsistencies, uncertainties, and recommendations. One Health 2021; 12:100225. [PMID: 33585669 PMCID: PMC7871781 DOI: 10.1016/j.onehlt.2021.100225] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 01/06/2021] [Accepted: 02/04/2021] [Indexed: 12/12/2022] Open
Abstract
Meteorological variables, such as the ambient temperature and humidity, play a well-established role in the seasonal transmission of respiratory viruses and influenza in temperate climates. Since the onset of the novel coronavirus disease 2019 (COVID-19) pandemic, a growing body of literature has attempted to characterize the sensitivity of COVID-19 to meteorological factors and thus understand how changes in the weather and seasonality may impede COVID-19 transmission. Here we select a subset of this literature, summarize the diversity in these studies' scopes and methodologies, and show the lack of consensus in their conclusions on the roles of temperature, humidity, and other meteorological factors on COVID-19 transmission dynamics. We discuss how several aspects of studies' methodologies may challenge direct comparisons across studies and inflate the importance of meteorological factors on COVID-19 transmission. We further comment on outstanding challenges for this area of research and how future studies might overcome them by carefully considering robust modeling approaches, adjusting for mediating and covariate effects, and choosing appropriate scales of analysis.
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Affiliation(s)
- Gaige Hunter Kerr
- Department of Occupational and Environmental Health, George Washington University, Washington, DC, USA
| | - Hamada S. Badr
- Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Lauren M. Gardner
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Javier Perez-Saez
- Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Benjamin F. Zaitchik
- Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD, USA
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21
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Borchering RK, Viboud C, Howerton E, Smith CP, Truelove S, Runge MC, Reich NG, Contamin L, Levander J, Salerno J, van Panhuis W, Kinsey M, Tallaksen K, Obrecht RF, Asher L, Costello C, Kelbaugh M, Wilson S, Shin L, Gallagher ME, Mullany LC, Rainwater-Lovett K, Lemaitre JC, Dent J, Grantz KH, Kaminsky J, Lauer SA, Lee EC, Meredith HR, Perez-Saez J, Keegan LT, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Pastore Y Piontti A, Vespignani A, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Schlitt J, Corbett P, Telionis PA, Wang L, Peddireddy AS, Hurt B, Chen J, Vullikanti A, Marathe M, Healy JM, Slayton RB, Biggerstaff M, Johansson MA, Shea K, Lessler J. Modeling of Future COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Rates and Nonpharmaceutical Intervention Scenarios - United States, April-September 2021. MMWR Morb Mortal Wkly Rep 2021. [PMID: 33988185 DOI: 10.15585/mmwr.mm7019e3externalicon] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/27/2023]
Abstract
After a period of rapidly declining U.S. COVID-19 incidence during January-March 2021, increases occurred in several jurisdictions (1,2) despite the rapid rollout of a large-scale vaccination program. This increase coincided with the spread of more transmissible variants of SARS-CoV-2, the virus that causes COVID-19, including B.1.1.7 (1,3) and relaxation of COVID-19 prevention strategies such as those for businesses, large-scale gatherings, and educational activities. To provide long-term projections of potential trends in COVID-19 cases, hospitalizations, and deaths, COVID-19 Scenario Modeling Hub teams used a multiple-model approach comprising six models to assess the potential course of COVID-19 in the United States across four scenarios with different vaccination coverage rates and effectiveness estimates and strength and implementation of nonpharmaceutical interventions (NPIs) (public health policies, such as physical distancing and masking) over a 6-month period (April-September 2021) using data available through March 27, 2021 (4). Among the four scenarios, an accelerated decline in NPI adherence (which encapsulates NPI mandates and population behavior) was shown to undermine vaccination-related gains over the subsequent 2-3 months and, in combination with increased transmissibility of new variants, could lead to surges in cases, hospitalizations, and deaths. A sharp decline in cases was projected by July 2021, with a faster decline in the high-vaccination scenarios. High vaccination rates and compliance with public health prevention measures are essential to control the COVID-19 pandemic and to prevent surges in hospitalizations and deaths in the coming months.
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22
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Borchering RK, Viboud C, Howerton E, Smith CP, Truelove S, Runge MC, Reich NG, Contamin L, Levander J, Salerno J, van Panhuis W, Kinsey M, Tallaksen K, Obrecht RF, Asher L, Costello C, Kelbaugh M, Wilson S, Shin L, Gallagher ME, Mullany LC, Rainwater-Lovett K, Lemaitre JC, Dent J, Grantz KH, Kaminsky J, Lauer SA, Lee EC, Meredith HR, Perez-Saez J, Keegan LT, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Pastore y Piontti A, Vespignani A, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Schlitt J, Corbett P, Telionis PA, Wang L, Peddireddy AS, Hurt B, Chen J, Vullikanti A, Marathe M, Healy JM, Slayton RB, Biggerstaff M, Johansson MA, Shea K, Lessler J. Modeling of Future COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Rates and Nonpharmaceutical Intervention Scenarios - United States, April-September 2021. MMWR Morb Mortal Wkly Rep 2021; 70:719-724. [PMID: 33988185 PMCID: PMC8118153 DOI: 10.15585/mmwr.mm7019e3] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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23
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Stringhini S, Zaballa ME, Perez-Saez J, Pullen N, de Mestral C, Picazio A, Pennacchio F, Wisniak A, Richard A, Baysson H, Loizeau A, Balavoine JF, Trono D, Pittet D, Posfay-Barbe K, Flahault A, Chappuis F, Kherad O, Vuilleumier N, Kaiser L, Azman AS, Guessous I. Seroprevalence of anti-SARS-CoV-2 antibodies after the second pandemic peak. Lancet Infect Dis 2021; 21:600-601. [PMID: 33539733 PMCID: PMC8063076 DOI: 10.1016/s1473-3099(21)00054-2] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 01/18/2021] [Indexed: 01/25/2023]
Affiliation(s)
- Silvia Stringhini
- Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland,Faculty of Medicine, University of Geneva, Switzerland
| | | | - Javier Perez-Saez
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Nick Pullen
- Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Carlos de Mestral
- Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Attilio Picazio
- Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Francesco Pennacchio
- Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Ania Wisniak
- Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Aude Richard
- Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland,Faculty of Medicine, University of Geneva, Switzerland
| | - Helene Baysson
- Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland,Faculty of Medicine, University of Geneva, Switzerland
| | - Andrea Loizeau
- Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland,Faculty of Medicine, University of Geneva, Switzerland
| | | | - Didier Trono
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Didier Pittet
- Infection Control Program and WHO Collaborating Centre on Patient Safety, Geneva University Hospitals, Geneva, Switzerland,Faculty of Medicine, University of Geneva, Switzerland
| | - Klara Posfay-Barbe
- Division of General Pediatrics, Geneva University Hospitals, Geneva, Switzerland,Faculty of Medicine, University of Geneva, Switzerland
| | | | - François Chappuis
- Division of Tropical and Humanitarian Medicine, Geneva University Hospitals, Geneva, Switzerland,Faculty of Medicine, University of Geneva, Switzerland
| | - Omar Kherad
- Faculty of Medicine, University of Geneva, Switzerland,Division of Internal Medicine, Hôpital de la Tour, Geneva, Switzerland
| | - Nicolas Vuilleumier
- Division of Laboratory Medicine, Geneva University Hospitals, Geneva, Switzerland,Faculty of Medicine, University of Geneva, Switzerland
| | - Laurent Kaiser
- Geneva Center for Emerging Viral Diseases, Geneva University Hospitals, Geneva, Switzerland,Faculty of Medicine, University of Geneva, Switzerland
| | - Andrew S Azman
- Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland,Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Idris Guessous
- Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland,Faculty of Medicine, University of Geneva, Switzerland
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Lemaitre JC, Grantz KH, Kaminsky J, Meredith HR, Truelove SA, Lauer SA, Keegan LT, Shah S, Wills J, Kaminsky K, Perez-Saez J, Lessler J, Lee EC. A scenario modeling pipeline for COVID-19 emergency planning. Sci Rep 2021; 11:7534. [PMID: 33824358 PMCID: PMC8024322 DOI: 10.1038/s41598-021-86811-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 03/18/2021] [Indexed: 01/05/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) has caused strain on health systems worldwide due to its high mortality rate and the large portion of cases requiring critical care and mechanical ventilation. During these uncertain times, public health decision makers, from city health departments to federal agencies, sought the use of epidemiological models for decision support in allocating resources, developing non-pharmaceutical interventions, and characterizing the dynamics of COVID-19 in their jurisdictions. In response, we developed a flexible scenario modeling pipeline that could quickly tailor models for decision makers seeking to compare projections of epidemic trajectories and healthcare impacts from multiple intervention scenarios in different locations. Here, we present the components and configurable features of the COVID Scenario Pipeline, with a vignette detailing its current use. We also present model limitations and active areas of development to meet ever-changing decision maker needs.
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Affiliation(s)
- Joseph C Lemaitre
- Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Kyra H Grantz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Joshua Kaminsky
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Hannah R Meredith
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Shaun A Truelove
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Stephen A Lauer
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Lindsay T Keegan
- Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
| | - Sam Shah
- Unaffiliated, San Francisco, USA
| | | | | | - Javier Perez-Saez
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Elizabeth C Lee
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
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25
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Lee EC, Chao DL, Lemaitre JC, Matrajt L, Pasetto D, Perez-Saez J, Finger F, Rinaldo A, Sugimoto JD, Halloran ME, Longini IM, Ternier R, Vissieres K, Azman AS, Lessler J, Ivers LC. Achieving coordinated national immunity and cholera elimination in Haiti through vaccination: a modelling study. Lancet Glob Health 2020; 8:e1081-e1089. [PMID: 32710864 PMCID: PMC7738665 DOI: 10.1016/s2214-109x(20)30310-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 02/17/2020] [Accepted: 05/06/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND Cholera was introduced into Haiti in 2010. Since then, more than 820 000 cases and nearly 10 000 deaths have been reported. Oral cholera vaccine (OCV) is safe and effective, but has not been seen as a primary tool for cholera elimination due to a limited period of protection and constrained supplies. Regionally, epidemic cholera is contained to the island of Hispaniola, and the lowest numbers of cases since the epidemic began were reported in 2019. Hence, Haiti may represent a unique opportunity to eliminate cholera with OCV. METHODS In this modelling study, we assessed the probability of elimination, time to elimination, and percentage of cases averted with OCV campaign scenarios in Haiti through simulations from four modelling teams. For a 10-year period from January 19, 2019, to Jan 13, 2029, we compared a no vaccination scenario with five OCV campaign scenarios that differed in geographical scope, coverage, and rollout duration. Teams used weekly department-level reports of suspected cholera cases from the Haiti Ministry of Public Health and Population to calibrate the models and used common vaccine-related assumptions, but other model features were determined independently. FINDINGS Among campaigns with the same vaccination coverage (70% fully vaccinated), the median probability of elimination after 5 years was 0-18% for no vaccination, 0-33% for 2-year campaigns focused in the two departments with the highest historical incidence, 0-72% for three-department campaigns, and 35-100% for nationwide campaigns. Two-department campaigns averted a median of 12-58% of infections, three-department campaigns averted 29-80% of infections, and national campaigns averted 58-95% of infections. Extending the national campaign to a 5-year rollout (compared to a 2-year rollout), reduced the probability of elimination to 0-95% and the proportion of cases averted to 37-86%. INTERPRETATION Models suggest that the probability of achieving zero transmission of Vibrio cholerae in Haiti with current methods of control is low, and that bolder action is needed to promote elimination of cholera from the region. Large-scale cholera vaccination campaigns in Haiti would offer the opportunity to synchronise nationwide immunity, providing near-term population protection while improvements to water and sanitation promote long-term cholera elimination. FUNDING Bill & Melinda Gates Foundation, Global Good Fund, Institute for Disease Modeling, Swiss National Science Foundation, and US National Institutes of Health.
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Affiliation(s)
- Elizabeth C Lee
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Joseph C Lemaitre
- Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Laura Matrajt
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Damiano Pasetto
- Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, Venice, Italy
| | - Javier Perez-Saez
- Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Flavio Finger
- Centre for Mathematical Modelling of Infectious Diseases and Department for Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Andrea Rinaldo
- Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Jonathan D Sugimoto
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - M Elizabeth Halloran
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA; Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Ira M Longini
- Department of Biostatistics, College of Public Health and Health Professions, and Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
| | - Ralph Ternier
- Partners In Health/Zanmi Lasante, Port-au-Prince, Haiti
| | | | - Andrew S Azman
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Louise C Ivers
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA; Center for Global Health, Massachusetts General Hospital, Boston, MA, USA.
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26
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Perez-Saez J, Lauer SA, Kaiser L, Regard S, Delaporte E, Guessous I, Stringhini S, Azman AS. Serology-informed estimates of SARS-CoV-2 infection fatality risk in Geneva, Switzerland. Lancet Infect Dis 2020; 21:e69-e70. [PMID: 32679085 PMCID: PMC7833057 DOI: 10.1016/s1473-3099(20)30584-3] [Citation(s) in RCA: 100] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 06/24/2020] [Indexed: 12/26/2022]
Affiliation(s)
- Javier Perez-Saez
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, MD, USA; Institute of Global Health, University of Geneva, Switzerland
| | - Stephen A Lauer
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, MD, USA
| | - Laurent Kaiser
- Faculty of Medicine, University of Geneva, Switzerland; Geneva Center for Emerging Viral Diseases and Laboratory of Virology, Geneva University Hospitals, Switzerland; Division of Laboratory Medicine, Geneva University Hospitals, Switzerland
| | - Simon Regard
- Department of Acute Medicine, Division of Emergency, Geneva University Hospitals, Switzerland; Cantonal Health Service, General Directorate for Health, Geneva, Switzerland
| | - Elisabeth Delaporte
- Cantonal Health Service, General Directorate for Health, Geneva, Switzerland
| | - Idris Guessous
- Faculty of Medicine, University of Geneva, Switzerland; Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Switzerland
| | - Silvia Stringhini
- Faculty of Medicine, University of Geneva, Switzerland; Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Switzerland
| | - Andrew S Azman
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, MD, USA; Institute of Global Health, University of Geneva, Switzerland; Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Switzerland.
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27
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Hoover CM, Sokolow SH, Kemp J, Sanchirico JN, Lund AJ, Jones IJ, Higginson T, Riveau G, Savaya A, Coyle S, Wood CL, Micheli F, Casagrandi R, Mari L, Gatto M, Rinaldo A, Perez-Saez J, Rohr JR, Sagi A, Remais JV, De Leo GA. Modelled effects of prawn aquaculture on poverty alleviation and schistosomiasis control. Nat Sustain 2020; 2:611-620. [PMID: 33313425 PMCID: PMC7731924 DOI: 10.1038/s41893-019-0301-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Accepted: 04/26/2019] [Indexed: 05/23/2023]
Abstract
Recent evidence suggests that snail predators may aid efforts to control the human parasitic disease schistosomiasis by eating aquatic snail species that serve as intermediate hosts of the parasite. Potential synergies between schistosomiasis control and aquaculture of giant prawns are evaluated using an integrated bio-economic-epidemiologic model. Combinations of stocking density and aquaculture cycle length that maximize cumulative, discounted profit are identified for two prawn species in sub-Saharan Africa: the endemic, non-domesticated Macrobrachium vollenhovenii, and the non-native, domesticated Macrobrachium rosenbergii. At profit maximizing densities, both M. rosenbergii and M. vollenhovenii may substantially reduce intermediate host snail populations and aid schistosomiasis control efforts. Control strategies drawing on both prawn aquaculture to reduce intermediate host snail populations and mass drug administration to treat infected individuals are found to be superior to either strategy alone. Integrated aquaculture-based interventions can be a win-win strategy in terms of health and sustainable development in schistosomiasis endemic regions of the world.
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Affiliation(s)
- Christopher M. Hoover
- Division of Environmental Health Sciences, University of California, Berkeley School of Public Health, Berkeley, CA 94720 USA
| | - Susanne H. Sokolow
- Department of Biology, Hopkins Marine Station, Stanford University, Pacific Grove, CA 93950 USA
- Woods Institute for the Environment and Center for Innovation in Global Health, Stanford University, Stanford, CA 94305 USA
| | - Jonas Kemp
- Program in Human Biology, Stanford University, Stanford, CA 94305 USA
| | - James N. Sanchirico
- Department of Environmental Science and Policy, University of California, Davis, Davis, CA 95616 USA
| | - Andrea J. Lund
- Emmett Interdisciplinary Program in Environment and Resources, School of Earth, Energy and Environmental Sciences, Stanford University, Stanford, CA 94305 USA
| | - Isabel J. Jones
- Department of Biology, Hopkins Marine Station, Stanford University, Pacific Grove, CA 93950 USA
| | - Tyler Higginson
- Middlebury Institute of International Studies at Monterey, Monterey, CA 93940 USA
| | - Gilles Riveau
- Biomedical Research Center EPLS, Saint Louis, Senegal
| | - Amit Savaya
- Department of Life Sciences and the National Institute for Biotechnology in the Negev, Ben Gurion University of the Negev, Beer Sheva, Israel
| | - Shawn Coyle
- Kentucky State University, Aquaculture Division, Aquaculture Research Center, Frankfort, KY 40601 USA
| | - Chelsea L. Wood
- University of Washington, School of Aquatic and Fishery Sciences, Seattle, WA 98195 USA
| | - Fiorenza Micheli
- Hopkins Marine Station and Center for Ocean Solutions, Stanford University, Pacific Grove, CA 93950 USA
| | - Renato Casagrandi
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy
| | - Lorenzo Mari
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy
| | - Marino Gatto
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy
| | - Andrea Rinaldo
- Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Switzerland
| | - Javier Perez-Saez
- Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Switzerland
| | - Jason R. Rohr
- Department of Biological Sciences, Eck Institute of Global Health, Environmental Change Initiative University of Notre Damea, Notre Dame, IN, 46556 USA
- Department of Integrative Biology, University of South Florida, Tampa, FL, 33620 USA
| | - Amir Sagi
- Department of Life Sciences and the National Institute for Biotechnology in the Negev, Ben Gurion University of the Negev, Beer Sheva, Israel
| | - Justin V. Remais
- Division of Environmental Health Sciences, University of California, Berkeley School of Public Health, Berkeley, CA 94720 USA
| | - Giulio A. De Leo
- Department of Biology, Hopkins Marine Station, Stanford University, Pacific Grove, CA 93950 USA
- Woods Institute for the Environment and Center for Innovation in Global Health, Stanford University, Stanford, CA 94305 USA
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28
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Giezendanner J, Pasetto D, Perez-Saez J, Cerrato C, Viterbi R, Terzago S, Palazzi E, Rinaldo A. Earth and field observations underpin metapopulation dynamics in complex landscapes: Near-term study on carabids. Proc Natl Acad Sci U S A 2020; 117:12877-12884. [PMID: 32461358 PMCID: PMC7293626 DOI: 10.1073/pnas.1919580117] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Understanding risks to biodiversity requires predictions of the spatial distribution of species adapting to changing ecosystems and, to that end, Earth observations integrating field surveys prove essential as they provide key numbers for assessing landscape-wide biodiversity scenarios. Here, we develop, and apply to a relevant case study, a method suited to merge Earth/field observations with spatially explicit stochastic metapopulation models to study the near-term ecological dynamics of target species in complex terrains. Our framework incorporates the use of species distribution models for a reasoned estimation of the initial presence of the target species and accounts for imperfect and incomplete detection of the species presence in the study area. It also uses a metapopulation fitness function derived from Earth observation data subsuming the ecological niche of the target species. This framework is applied to contrast occupancy of two species of carabids (Pterostichus flavofemoratus, Carabus depressus) observed in the context of a large ecological monitoring program carried out within the Gran Paradiso National Park (GPNP, Italy). Results suggest that the proposed framework may indeed exploit the hallmarks of spatially explicit ecological approaches and of remote Earth observations. The model reproduces well the observed in situ data. Moreover, it projects in the near term the two species' presence both in space and in time, highlighting the features of the metapopulation dynamics of colonization and extinction, and their expected trends within verifiable timeframes.
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Affiliation(s)
- Jonathan Giezendanner
- Laboratory of Ecohydrology, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Damiano Pasetto
- Laboratory of Ecohydrology, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Javier Perez-Saez
- Laboratory of Ecohydrology, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | | | | | - Silvia Terzago
- National Research Council (CNR), Institute of Atmospheric Sciences and Climate, 10133 Torino, Italy
| | - Elisa Palazzi
- National Research Council (CNR), Institute of Atmospheric Sciences and Climate, 10133 Torino, Italy
| | - Andrea Rinaldo
- Laboratory of Ecohydrology, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland;
- Dipartimento di Ingegneria Civile Edile e Ambientale (DICEA), Università di Padova, 35131 Padova, Italy
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29
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Lemaitre JC, Perez-Saez J, Azman AS, Rinaldo A, Fellay J. Assessing the impact of non-pharmaceutical interventions on SARS-CoV-2 transmission in Switzerland. Swiss Med Wkly 2020; 150:w20295. [PMID: 32472939 DOI: 10.4414/smw.2020.20295] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Following the rapid dissemination of COVID-19 cases in Switzerland, large-scale non-pharmaceutical interventions (NPIs) were implemented by the cantons and the federal government between 28 February and 20 March 2020. Estimates of the impact of these interventions on SARS-CoV-2 transmission are critical for decision making in this and future outbreaks. We here aim to assess the impact of these NPIs on disease transmission by estimating changes in the basic reproduction number (R0) at national and cantonal levels in relation to the timing of these NPIs. We estimated the time-varying R0 nationally and in eleven cantons by fitting a stochastic transmission model explicitly simulating within-hospital dynamics. We used individual-level data from more than 1000 hospitalised patients in Switzerland and public daily reports of hospitalisations and deaths. We estimated the national R0 to be 2.8 (95% confidence interval 2.1–3.8) at the beginning of the epidemic. Starting from around 7 March, we found a strong reduction in time-varying R0 with a 86% median decrease (95% quantile range [QR] 79–90%) to a value of 0.40 (95% QR 0.3–0.58) in the period of 29 March to 5 April. At the cantonal level, R0 decreased over the course of the epidemic between 53% and 92%. Reductions in time-varying R0 were synchronous with changes in mobility patterns as estimated through smartphone activity, which started before the official implementation of NPIs. We inferred that most of the reduction of transmission is attributable to behavioural changes as opposed to natural immunity, the latter accounting for only about 4% of the total reduction in effective transmission. As Switzerland considers relaxing some of the restrictions of social mixing, current estimates of time-varying R0 well below one are promising. However, as of 24 April 2020, at least 96% (95% QR 95.7–96.4%) of the Swiss population remains susceptible to SARS-CoV-2. These results warrant a cautious relaxation of social distance practices and close monitoring of changes in both the basic and effective reproduction numbers.
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Affiliation(s)
- Joseph C Lemaitre
- Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Switzerland
| | - Javier Perez-Saez
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Andrew S Azman
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Andrea Rinaldo
- Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Switzerland / Dipartimento di Ingegneria Civile Edile ed Ambientale, Università di Padova, Padua, Italy
| | - Jacques Fellay
- School of Life Sciences, École Polytechnique Fédérale de Lausanne, Switzerland / Precision Medicine Unit, Lausanne University Hospital and University of Lausanne, Switzerland
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Maier T, Wheeler NJ, Namigai EKO, Tycko J, Grewelle RE, Woldeamanuel Y, Klohe K, Perez-Saez J, Sokolow SH, De Leo GA, Yoshino TP, Zamanian M, Reinhard-Rupp J. Gene drives for schistosomiasis transmission control. PLoS Negl Trop Dis 2019; 13:e0007833. [PMID: 31856157 PMCID: PMC6922350 DOI: 10.1371/journal.pntd.0007833] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Schistosomiasis is one of the most important and widespread neglected tropical diseases (NTD), with over 200 million people infected in more than 70 countries; the disease has nearly 800 million people at risk in endemic areas. Although mass drug administration is a cost-effective approach to reduce occurrence, extent, and severity of the disease, it does not provide protection to subsequent reinfection. Interventions that target the parasites’ intermediate snail hosts are a crucial part of the integrated strategy required to move toward disease elimination. The recent revolution in gene drive technology naturally leads to questions about whether gene drives could be used to efficiently spread schistosome resistance traits in a population of snails and whether gene drives have the potential to contribute to reduced disease transmission in the long run. Responsible implementation of gene drives will require solutions to complex challenges spanning multiple disciplines, from biology to policy. This Review Article presents collected perspectives from practitioners of global health, genome engineering, epidemiology, and snail/schistosome biology and outlines strategies for responsible gene drive technology development, impact measurements of gene drives for schistosomiasis control, and gene drive governance. Success in this arena is a function of many factors, including gene-editing specificity and efficiency, the level of resistance conferred by the gene drive, how fast gene drives may spread in a metapopulation over a complex landscape, ecological sustainability, social equity, and, ultimately, the reduction of infection prevalence in humans. With combined efforts from across the broad global health community, gene drives for schistosomiasis control could fortify our defenses against this devastating disease in the future.
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Affiliation(s)
- Theresa Maier
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom
| | - Nicolas James Wheeler
- Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Global Health Institute of Merck (KGaA), Eysins, Switzerland
| | | | - Josh Tycko
- Department of Genetics, Stanford University School of Medicine, Stanford, California, United States of America
| | - Richard Ernest Grewelle
- Hopkins Marine Station, School of Humanities and Sciences, Stanford University, Pacific Grove, California, United States of America
| | - Yimtubezinash Woldeamanuel
- Department of Microbiology, Immunology & Parasitology, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
| | | | - Javier Perez-Saez
- Laboratory of Ecohydrology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Susanne H. Sokolow
- Woods Institute for the Environment, Stanford University, Stanford, California, United States of America
- Marine Science Institute, University of California, Santa Barbara, California, United States of America
| | - Giulio A. De Leo
- Hopkins Marine Station, School of Humanities and Sciences, Stanford University, Pacific Grove, California, United States of America
| | - Timothy P. Yoshino
- Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Mostafa Zamanian
- Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
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Perez-Saez J, Mande T, Zongo D, Rinaldo A. Comparative analysis of time-based and quadrat sampling in seasonal population dynamics of intermediate hosts of human schistosomes. PLoS Negl Trop Dis 2019; 13:e0007938. [PMID: 31860653 PMCID: PMC6957212 DOI: 10.1371/journal.pntd.0007938] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 01/13/2020] [Accepted: 11/20/2019] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Despite their importance for designing and evaluating schistosomiasis control trials, little attention in the literature has been dedicated to sampling protocols for the parasite's snail intermediate hosts since their first development. We propose a comparative analysis of time-based and quadrat sampling protocols to quantify the seasonal variations in the abundance of these aquatic snail species of medical importance. METHODOLOGY/PRINCIPAL FINDINGS Snail populations were monitored during 42 consecutive months in three types of habitats (ephemeral pond, ephemeral river and permanent stream) in two sites covering different climatic zones in Burkina Faso. We employed both a widely used time-based protocol of 30min of systematic collection at a weekly interval, and a quadrat protocol of 8 replicates per sample at a monthly interval. The correspondence between the two protocols was evaluated using an ensemble of statistical models including linear and saturating-type functional forms as well as allowing for count zero-inflation. The quadrat protocol yielded on average a relative standard error of 40%, for a mean snail density of 16.7 snails/m2 and index of dispersion of 1.51. Both protocols yielded similar seasonal patterns in snail abundance, confirming the asynchrony between permanent and ephemeral habitats with respect to the country's seasonal rainfall patterns. Formal model comparison of the link between time vs. quadrat counts showed strong support of saturation for the latter and measurement zero-inflation, providing important evidence for the presence of density feedbacks in the snail's population dynamics, as well as for spatial clustering. CONCLUSIONS/SIGNIFICANCE In addition to the agreement with the time-based method, quadrat sampling provided insight into snail population dynamics and comparable density estimates across sites. The re-evaluation of these "traditional" sampling protocols, as well as the correspondence between their outputs, is of practical importance for the design and evaluation of schistosomiasis control trials.
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Affiliation(s)
- Javier Perez-Saez
- Laboratory of Ecohydrology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Théophile Mande
- Laboratory of Ecohydrology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Dramane Zongo
- Départemente Biomédical et Santé publique, Institut de Recherche en Sciences de la Santé, Ouagadougou, Burkina Faso
| | - Andrea Rinaldo
- Laboratory of Ecohydrology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Dipartimento ICEA, Università di Padova, Padova, Italy
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Perez-Saez J, Mande T, Rinaldo A. Space and time predictions of schistosomiasis snail host population dynamics across hydrologic regimes in Burkina Faso. Geospat Health 2019; 14. [PMID: 31724380 DOI: 10.4081/gh.2019.796] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 10/08/2019] [Indexed: 06/10/2023]
Abstract
The ecology of the aquatic snails that serve as obligatory intermediate hosts of human schistosomiasis is driven by climatic and hydrological factors which result in specific spatial patterns of occurrence and abundance. These patterns in turn affect, jointly with other determinants, the geography of the disease and the timing of transmission windows, with direct implications for the success of control and elimination programmes in the endemic countries. We address the spatial distribution of the intermediate hosts and their seasonal population dynamics within a predictive ecohydrological framework developed at the national scale for Burkina Faso, West Africa. The approach blends river network-wide information on hydrological ephemerality which conditions snail habitat suitability together with ensembles of discrete time ecological models forced by remotely sensed estimates of temperature and precipitation. The models were validated against up to four years of monthly snail abundance data. Simulations of model ensembles accounting for the uncertainty in remotely sensed products adequately reproduce observed snail demographic fluctuations observed in the field across habitat types, and produce national scale predictions by accounting for spatial patterns of hydrological conditions in the country. Geospatial estimates of seasonal snail abundance underpin large-scale, spatially explicit predictions of schistosomiasis incidence. This work can therefore contribute to the development of disease control and elimination programmes.
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Affiliation(s)
- Javier Perez-Saez
- Laboratory of Ecohydrology, Federal Polytechnic School of Lausanne, Lausanne.
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Lemaitre J, Pasetto D, Perez-Saez J, Sciarra C, Wamala JF, Rinaldo A. Rainfall as a driver of epidemic cholera: Comparative model assessments of the effect of intra-seasonal precipitation events. Acta Trop 2019; 190:235-243. [PMID: 30465744 DOI: 10.1016/j.actatropica.2018.11.013] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Revised: 11/04/2018] [Accepted: 11/14/2018] [Indexed: 01/18/2023]
Abstract
The correlation between cholera epidemics and climatic drivers, in particular seasonal tropical rainfall, has been studied in a variety of contexts owing to its documented relevance. Several mechanistic models of cholera transmission have included rainfall as a driver by focusing on two possible transmission pathways: either by increasing exposure to contaminated water (e.g. due to worsening sanitary conditions during water excess), or water contamination by freshly excreted bacteria (e.g. due to washout of open-air defecation sites or overflows). Our study assesses the explanatory power of these different modeling structures by formal model comparison using deterministic and stochastic models of the type susceptible-infected-recovered-bacteria (SIRB). The incorporation of rainfall effects is generalized using a nonlinear function that can increase or decrease the relative importance of the large precipitation events. Our modelling framework is tested against the daily epidemiological data collected during the 2015 cholera outbreak within the urban context of Juba, South Sudan. This epidemic is characterized by a particular intra-seasonal double peak on the incidence in apparent relation with particularly strong rainfall events. Our results show that rainfall-based models in both their deterministic and stochastic formulations outperform models that do not account for rainfall. In fact, classical SIRB models are not able to reproduce the second epidemiological peak, thus suggesting that it was rainfall-driven. Moreover we found stronger support across model types for rainfall acting on increased exposure rather than on exacerbated water contamination. Although these results are context-specific, they stress the importance of a systematic and comprehensive appraisal of transmission pathways and their environmental forcings when embarking in the modelling of epidemic cholera.
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Affiliation(s)
- Joseph Lemaitre
- Laboratory of Ecohydrology, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.
| | - Damiano Pasetto
- Laboratory of Ecohydrology, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.
| | - Javier Perez-Saez
- Laboratory of Ecohydrology, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.
| | - Carla Sciarra
- Dipartimento di Ingegneria dell'Ambiente, del Territorio e delle Infrastrutture, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino, Italy.
| | | | - Andrea Rinaldo
- Laboratory of Ecohydrology, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland; Dipartimento ICEA, Università di Padova, 35100 Padova, Italy.
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Perez-Saez J, King AA, Rinaldo A, Yunus M, Faruque ASG, Pascual M. Climate-driven endemic cholera is modulated by human mobility in a megacity. Adv Water Resour 2017; 108:367-376. [PMID: 29081572 PMCID: PMC5654324 DOI: 10.1016/j.advwatres.2016.11.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Although a differential sensitivity of cholera dynamics to climate variability has been reported in the spatially heterogeneous megacity of Dhaka, Bangladesh, the specific patterns of spread of the resulting risk within the city remain unclear. We build on an established probabilistic spatial model to investigate the importance and role of human mobility in modulating spatial cholera transmission. Mobility fluxes were inferred using a straightforward and generalizable methodology that relies on mapping population density based on a high resolution urban footprint product, and a parameter-free human mobility model. In accordance with previous findings, we highlight the higher sensitivity to the El Niño Southern Oscillation (ENSO) in the highly populated urban center than in the more rural periphery. More significantly, our results show that cholera risk is largely transmitted from the climate-sensitive core to the periphery of the city, with implications for the planning of control efforts. In addition, including human mobility improves the outbreak prediction performance of the model with an 11 month lead. The interplay between climatic and human mobility factors in cholera transmission is discussed from the perspective of the rapid growth of megacities across the developing world.
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Affiliation(s)
- Javier Perez-Saez
- Laboratory of Ecohydrology, Ecole Polytechnique Fédérale de Lausanne, CH-1015, Switzerland
| | - Aaron A King
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Andrea Rinaldo
- Laboratory of Ecohydrology, Ecole Polytechnique Fédérale de Lausanne, CH-1015, Switzerland
| | - Mohammad Yunus
- International Centre for Diarrheal Disease Research, Dhaka 1000, Bangladesh
| | - Abu S G Faruque
- International Centre for Diarrheal Disease Research, Dhaka 1000, Bangladesh
| | - Mercedes Pascual
- Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA
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Mari L, Ciddio M, Casagrandi R, Perez-Saez J, Bertuzzo E, Rinaldo A, Sokolow SH, De Leo GA, Gatto M. Heterogeneity in schistosomiasis transmission dynamics. J Theor Biol 2017; 432:87-99. [PMID: 28823529 PMCID: PMC5595357 DOI: 10.1016/j.jtbi.2017.08.015] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Revised: 06/30/2017] [Accepted: 08/15/2017] [Indexed: 01/30/2023]
Abstract
Transmission dynamics of schistosomiasis presents multiple heterogeneity sources. A comprehensive framework for heterogeneous disease transmission is proposed. Heterogeneous multigroup communities can be more prone to parasite transmission. Presence of multiple water sources can hinder parasite transmission. Spatial and temporal heterogeneities can have nontrivial implications for endemicity.
Simple models of disease propagation often disregard the effects of transmission heterogeneity on the ecological and epidemiological dynamics associated with host-parasite interactions. However, for some diseases like schistosomiasis, a widespread parasitic infection caused by Schistosoma worms, accounting for heterogeneity is crucial to both characterize long-term dynamics and evaluate opportunities for disease control. Elaborating on the classic Macdonald model for macroparasite transmission, we analyze families of models including explicit descriptions of heterogeneity related to differential transmission risk within a community, water contact patterns, the distribution of the snail host population, human mobility, and the seasonal fluctuations of the environment. Through simple numerical examples, we show that heterogeneous multigroup communities may be more prone to schistosomiasis than homogeneous ones, that the availability of multiple water sources can hinder parasite transmission, and that both spatial and temporal heterogeneities may have nontrivial implications for disease endemicity. Finally, we discuss the implications of heterogeneity for disease control. Although focused on schistosomiasis, results from this study may apply as well to other parasitic infections with complex transmission cycles, such as cysticercosis, dracunculiasis and fasciolosis.
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Affiliation(s)
- Lorenzo Mari
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy.
| | - Manuela Ciddio
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy
| | - Renato Casagrandi
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy
| | - Javier Perez-Saez
- Laboratory of Ecohydrology, Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Enrico Bertuzzo
- Dipartimento di Scienze Ambientali, Informatica e Statistica, Università Ca' Foscari Venezia, 30170 Venezia Mestre, Italy
| | - Andrea Rinaldo
- Laboratory of Ecohydrology, Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland; Dipartimento ICEA, Università di Padova, 35131 Padova, Italy
| | - Susanne H Sokolow
- Hopkins Marine Station, Stanford University, Pacific Grove, CA 93950, USA; Marine Science Institute, University of California, Santa Barbara, CA 93106, USA
| | - Giulio A De Leo
- Hopkins Marine Station, Stanford University, Pacific Grove, CA 93950, USA
| | - Marino Gatto
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy
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Perez-Saez J, Mari L, Bertuzzo E, Casagrandi R, Sokolow SH, De Leo GA, Mande T, Ceperley N, Froehlich JM, Sou M, Karambiri H, Yacouba H, Maiga A, Gatto M, Rinaldo A. A Theoretical Analysis of the Geography of Schistosomiasis in Burkina Faso Highlights the Roles of Human Mobility and Water Resources Development in Disease Transmission. PLoS Negl Trop Dis 2015; 9:e0004127. [PMID: 26513655 PMCID: PMC4625963 DOI: 10.1371/journal.pntd.0004127] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2015] [Accepted: 09/08/2015] [Indexed: 12/28/2022] Open
Abstract
We study the geography of schistosomiasis across Burkina Faso by means of a spatially explicit model of water-based disease dynamics. The model quantitatively addresses the geographic stratification of disease burden in a novel framework by explicitly accounting for drivers and controls of the disease, including spatial information on the distributions of population and infrastructure, jointly with a general description of human mobility and climatic/ecological drivers. Spatial patterns of disease are analysed by the extraction and the mapping of suitable eigenvectors of the Jacobian matrix subsuming the stability of the disease-free equilibrium. The relevance of the work lies in the novel mapping of disease burden, a byproduct of the parametrization induced by regional upscaling, by model-guided field validations and in the predictive scenarios allowed by exploiting the range of possible parameters and processes. Human mobility is found to be a primary control at regional scales both for pathogen invasion success and the overall distribution of disease burden. The effects of water resources development highlighted by systematic reviews are accounted for by the average distances of human settlements from water bodies that are habitats for the parasite’s intermediate host. Our results confirm the empirical findings about the role of water resources development on disease spread into regions previously nearly disease-free also by inspection of empirical prevalence patterns. We conclude that while the model still needs refinements based on field and epidemiological evidence, the proposed framework provides a powerful tool for large-scale public health planning and schistosomiasis management. Dynamical models of schistosomiasis infections, even spatially explicit ones, have so far only addressed spatial scales encompassing at best a few villages and the disease transmission impacts of related short-range human mobility. Here, we build from existing models of disease dynamics and spread, including a proxy of the ecology of the intermediate host of the parasite, and from generalized reproduction numbers of SIR-type systems developed for epidemics of waterborne disease, to set up large-scale projections of spatial patterns of the disease at whole country level. We ground our study in Burkina Faso in sub-Saharan Africa, and its model of social and economic development including the infrastructure built to exploit water resources, especially irrigation schemes, which have been empirically linked to enhanced disease burden. We make extensive use of remotely sensed and field data, and capitalize on ecohydrological insight. We suggest that reliable nationwide patterns of disease burden can be projected in relation to the key roles of human mobility and water resources development subsuming exposure, and claim that the case at hand provides an insightful example towards the integration of development and environmental thinking not confined to ad-hoc indicators of human development.
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Affiliation(s)
- Javier Perez-Saez
- Laboratory of Ecohydrology, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Lorenzo Mari
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy
| | - Enrico Bertuzzo
- Laboratory of Ecohydrology, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Renato Casagrandi
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy
| | - Susanne H. Sokolow
- Hopkins Marine Station, Stanford University, Pacific Grove, California, United States of America
- Marine Science Institute, University of California Santa Barbara, California, United States of America
| | - Giulio A. De Leo
- Hopkins Marine Station, Stanford University, Pacific Grove, California, United States of America
- Woods Institute for the Environment, Stanford University, California, United States of America
| | - Theophile Mande
- Laboratory of Ecohydrology, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Natalie Ceperley
- Laboratory of Ecohydrology, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Jean-Marc Froehlich
- Laboratory of Ecohydrology, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Mariam Sou
- Institute International d’Ingénierie de l’Eau et de l’Environment, Ouagadougou, Burkina Faso
| | - Harouna Karambiri
- Institute International d’Ingénierie de l’Eau et de l’Environment, Ouagadougou, Burkina Faso
| | - Hamma Yacouba
- Institute International d’Ingénierie de l’Eau et de l’Environment, Ouagadougou, Burkina Faso
| | - Amadou Maiga
- Institute International d’Ingénierie de l’Eau et de l’Environment, Ouagadougou, Burkina Faso
| | - Marino Gatto
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy
| | - Andrea Rinaldo
- Laboratory of Ecohydrology, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Dipartimento ICEA, Università di Padova, Padova, Italy
- * E-mail:
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