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Garcia-Bernardo J, Hedde-von Westernhagen C, Emery T, van Hoek AJ. Assessing COVID-19 transmission through school and family networks using population-level registry data from the Netherlands. Sci Rep 2024; 14:31248. [PMID: 39732754 DOI: 10.1038/s41598-024-82646-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2024] [Accepted: 12/06/2024] [Indexed: 12/30/2024] Open
Abstract
Understanding the impact of different types of social interactions is key to improving epidemic models. Here, we use extensive registry data-including PCR test results and population-level networks-to investigate the impact of school, family, and other social contacts on SARS-CoV-2 transmission in the Netherlands (June 2020-October 2021). We isolate and compare different contexts of potential SARS-CoV-2 transmission by matching pairs of students based on their attendance at the same or different primary school (in 2020) and secondary school (in 2021) and their geographic proximity. We then calculate the probability of temporally associated infections-i.e. the probability of both students testing positive within a 14-day period. Our results highlight the relative importance of household and family transmission in the spread of SARS-CoV-2 compared to school settings. The probability of temporally associated infections for siblings and parent-child pairs living in the same household ranged from 22.6-23.2%. Interestingly, a high probability (4.7-7.9%) was found even when family members lived in different households, underscoring the persistent risk of transmission within family networks. In contrast, the probability of temporally associated infections was 0.52% for pairs of students living nearby but not attending the same primary or secondary school, 0.66% for pairs attending different secondary schools but having attended the same primary school, and 1.65% for pairs attending the same secondary school. It is worth noting, however, that even small increases in school-related infection probabilities can trigger large-scale outbreaks due to the dense network of interactions in these settings. Finally, we used multilevel regression analyses to examine how individual, school, and geographic factors contribute to transmission risk. We found that the largest differences in transmission probabilities were due to unobserved individual (60%) and school-level (35%) factors. Only a small proportion (3%) could be attributed to geographic proximity of students or to school size, denomination, or the median income of the school area.
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Affiliation(s)
- Javier Garcia-Bernardo
- ODISSEI Social Data Science (SoDa) Team & Department of Methodology and Statistics, Utrecht University, Utrecht, Netherlands.
- Centre for Complex Systems Studies, Utrecht University, Utrecht, Netherlands.
| | | | - Tom Emery
- ODISSEI & Department of Public Administration and Sociology, Erasmus University, Rotterdam, Netherlands
| | - Albert Jan van Hoek
- Centre for Infectious Diseases Control, National Institute for Public Health and the Environment, Bilthoven, Netherlands
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2
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Demongeot J, Magal P. Data-driven mathematical modeling approaches for COVID-19: A survey. Phys Life Rev 2024; 50:166-208. [PMID: 39142261 DOI: 10.1016/j.plrev.2024.08.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2024] [Accepted: 08/02/2024] [Indexed: 08/16/2024]
Abstract
In this review, we successively present the methods for phenomenological modeling of the evolution of reported and unreported cases of COVID-19, both in the exponential phase of growth and then in a complete epidemic wave. After the case of an isolated wave, we present the modeling of several successive waves separated by endemic stationary periods. Then, we treat the case of multi-compartmental models without or with age structure. Eventually, we review the literature, based on 260 articles selected in 11 sections, ranging from the medical survey of hospital cases to forecasting the dynamics of new cases in the general population. This review favors the phenomenological approach over the mechanistic approach in the choice of references and provides simulations of the evolution of the number of observed cases of COVID-19 for 10 states (California, China, France, India, Israel, Japan, New York, Peru, Spain and United Kingdom).
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Affiliation(s)
- Jacques Demongeot
- Université Grenoble Alpes, AGEIS EA7407, La Tronche, F-38700, France.
| | - Pierre Magal
- Department of Mathematics, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai, 519087, China; Univ. Bordeaux, IMB, UMR 5251, Talence, F-33400, France; CNRS, IMB, UMR 5251, Talence, F-33400, France
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3
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Jang G, Kim J, Lee Y, Son C, Ko KT, Lee H. Analysis of the impact of COVID-19 variants and vaccination on the time-varying reproduction number: statistical methods. Front Public Health 2024; 12:1353441. [PMID: 39022412 PMCID: PMC11253806 DOI: 10.3389/fpubh.2024.1353441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 06/07/2024] [Indexed: 07/20/2024] Open
Abstract
Introduction The COVID-19 pandemic has profoundly impacted global health systems, requiring the monitoring of infection waves and strategies to control transmission. Estimating the time-varying reproduction number is crucial for understanding the epidemic and guiding interventions. Methods Probability distributions of serial interval are estimated for Pre-Delta and Delta periods. We conducted a comparative analysis of time-varying reproduction numbers, taking into account population immunity and variant differences. We incorporated the regional heterogeneity and age distribution of the population, as well as the evolving variants and vaccination rates over time. COVID-19 transmission dynamics were analyzed with variants and vaccination. Results The reproduction number is computed with and without considering variant-based immunity. In addition, values of reproduction number significantly differed by variants, emphasizing immunity's importance. Enhanced vaccination efforts and stringent control measures were effective in reducing the transmission of the Delta variant. Conversely, Pre-Delta variant appeared less influenced by immunity levels, due to lower vaccination rates. Furthermore, during the Pre-Delta period, there was a significant difference between the region-specific and the non-region-specific reproduction numbers, with particularly distinct pattern differences observed in Gangwon, Gyeongbuk, and Jeju in Korea. Discussion This research elucidates the dynamics of COVID-19 transmission concerning the dominance of the Delta variant, the efficacy of vaccinations, and the influence of immunity levels. It highlights the necessity for targeted interventions and extensive vaccination coverage. This study makes a significant contribution to the understanding of disease transmission mechanisms and informs public health strategies.
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Affiliation(s)
- Geunsoo Jang
- Nonlinear Dynamics and Mathematical Application Center, Kyungpook National University, Daegu, Republic of Korea
| | - Jihyeon Kim
- Department of Statistics, Kyungpook National University, Daegu, Republic of Korea
| | - Yeonsu Lee
- Department of Statistics, Kyungpook National University, Daegu, Republic of Korea
| | - Changdae Son
- Department of Statistics, Kyungpook National University, Daegu, Republic of Korea
| | - Kyeong Tae Ko
- Department of Statistics, Kyungpook National University, Daegu, Republic of Korea
| | - Hyojung Lee
- Department of Statistics, Kyungpook National University, Daegu, Republic of Korea
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Terebuh PD, Albert JM, Curtis JW, Stange KC, Hrusch S, Brennan K, Miracle JE, Yar W, Ganesh PR, Gullett HL, Rose J. Association of School Instructional Mode with Community COVID-19 Incidence during August-December 2020 in Cuyahoga County, Ohio. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2024; 21:569. [PMID: 38791784 PMCID: PMC11121418 DOI: 10.3390/ijerph21050569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 04/23/2024] [Accepted: 04/26/2024] [Indexed: 05/26/2024]
Abstract
Remote and hybrid modes of instruction were employed as alternatives to in-person instruction as part of early mitigation efforts in response to the COVID-19 pandemic. We investigated the impact of a public school district's instructional mode on cumulative incidence and transmission in the surrounding community by employing a generalized estimating equations approach to estimate the association with weekly COVID-19 case counts by zip code in Cuyahoga County, Ohio, from August to December 2020. Remote instruction only (RI) was employed by 7 of 20 school districts; 13 used some non-remote instruction (NRI) (2-15 weeks). Weekly incidence increased in all zip codes from August to peak in late fall before declining. The zip code cumulative incidence within NRI school districts was higher than in those offering only RI (risk ratio = 1.12, p = 0.01; risk difference = 519 per 100,000, 95% confidence interval (123-519)). The mean effect for NRI on emergent cases 2 weeks after mode exposure, controlling for Social Vulnerability Index (SVI), was significant only for high SVI zip codes 1.30, p < 0.001. NRI may be associated with increased community COVID-19 incidence, particularly in communities with high SVI. Vulnerable communities may need more resources to open schools safely.
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Affiliation(s)
- Pauline D. Terebuh
- University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
- School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA; (J.M.A.); (J.W.C.); (H.L.G.)
| | - Jeffrey M. Albert
- School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA; (J.M.A.); (J.W.C.); (H.L.G.)
| | - Jacqueline W. Curtis
- School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA; (J.M.A.); (J.W.C.); (H.L.G.)
| | - Kurt C. Stange
- University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
- School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA; (J.M.A.); (J.W.C.); (H.L.G.)
- Neighborhood Family Practice, Cleveland, OH 44102, USA
| | | | - Kevin Brennan
- Cuyahoga County Board of Health, Parma, OH 44130, USA
| | - Jill E. Miracle
- University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
- School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA; (J.M.A.); (J.W.C.); (H.L.G.)
| | - Wail Yar
- University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
- School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA; (J.M.A.); (J.W.C.); (H.L.G.)
| | - Prakash R. Ganesh
- University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
- School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA; (J.M.A.); (J.W.C.); (H.L.G.)
- Neighborhood Family Practice, Cleveland, OH 44102, USA
- Cuyahoga County Board of Health, Parma, OH 44130, USA
| | - Heidi L. Gullett
- School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA; (J.M.A.); (J.W.C.); (H.L.G.)
- Cuyahoga County Board of Health, Parma, OH 44130, USA
| | - Johnie Rose
- School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA; (J.M.A.); (J.W.C.); (H.L.G.)
- Cuyahoga County Board of Health, Parma, OH 44130, USA
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5
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Ma Z, Rennert L. An epidemiological modeling framework to inform institutional-level response to infectious disease outbreaks: a Covid-19 case study. Sci Rep 2024; 14:7221. [PMID: 38538693 PMCID: PMC10973339 DOI: 10.1038/s41598-024-57488-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 03/19/2024] [Indexed: 04/04/2024] Open
Abstract
Institutions have an enhanced ability to implement tailored mitigation measures during infectious disease outbreaks. However, macro-level predictive models are inefficient for guiding institutional decision-making due to uncertainty in local-level model input parameters. We present an institutional-level modeling toolkit used to inform prediction, resource procurement and allocation, and policy implementation at Clemson University throughout the Covid-19 pandemic. Through incorporating real-time estimation of disease surveillance and epidemiological measures based on institutional data, we argue this approach helps minimize uncertainties in input parameters presented in the broader literature and increases prediction accuracy. We demonstrate this through case studies at Clemson and other university settings during the Omicron BA.1 and BA.4/BA.5 variant surges. The input parameters of our toolkit are easily adaptable to other institutional settings during future health emergencies. This methodological approach has potential to improve public health response through increasing the capability of institutions to make data-informed decisions that better prioritize the health and safety of their communities while minimizing operational disruptions.
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Affiliation(s)
- Zichen Ma
- Department of Mathematics, Colgate University, Hamilton, NY, USA
- Center for Public Health Modeling and Response, Department of Public Health Sciences, Clemson University, 517 Edwards Hall, Clemson, SC, 29634, USA
| | - Lior Rennert
- Center for Public Health Modeling and Response, Department of Public Health Sciences, Clemson University, 517 Edwards Hall, Clemson, SC, 29634, USA.
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6
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Boldea O, Alipoor A, Pei S, Shaman J, Rozhnova G. Age-specific transmission dynamics of SARS-CoV-2 during the first 2 years of the pandemic. PNAS NEXUS 2024; 3:pgae024. [PMID: 38312225 PMCID: PMC10837015 DOI: 10.1093/pnasnexus/pgae024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 01/09/2024] [Indexed: 02/06/2024]
Abstract
During its first 2 years, the SARS-CoV-2 pandemic manifested as multiple waves shaped by complex interactions between variants of concern, non-pharmaceutical interventions, and the immunological landscape of the population. Understanding how the age-specific epidemiology of SARS-CoV-2 has evolved throughout the pandemic is crucial for informing policy decisions. In this article, we aimed to develop an inference-based modeling approach to reconstruct the burden of true infections and hospital admissions in children, adolescents, and adults over the seven waves of four variants (wild-type, Alpha, Delta, and Omicron BA.1) during the first 2 years of the pandemic, using the Netherlands as the motivating example. We find that reported cases are a considerable underestimate and a generally poor predictor of true infection burden, especially because case reporting differs by age. The contribution of children and adolescents to total infection and hospitalization burden increased with successive variants and was largest during the Omicron BA.1 period. However, the ratio of hospitalizations to infections decreased with each subsequent variant in all age categories. Before the Delta period, almost all infections were primary infections occurring in naive individuals. During the Delta and Omicron BA.1 periods, primary infections were common in children but relatively rare in adults who experienced either reinfections or breakthrough infections. Our approach can be used to understand age-specific epidemiology through successive waves in other countries where random community surveys uncovering true SARS-CoV-2 dynamics are absent but basic surveillance and statistics data are available.
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Affiliation(s)
- Otilia Boldea
- Department of Econometrics and OR, Tilburg School of Economics and Management, Tilburg University, Tilburg 5037 AB, The Netherlands
| | - Amir Alipoor
- Department of Econometrics and OR, Tilburg School of Economics and Management, Tilburg University, Tilburg 5037 AB, The Netherlands
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
- Columbia Climate School, Columbia University, New York, NY 10025, USA
| | - Ganna Rozhnova
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht 3584 CX, The Netherlands
- Center for Complex Systems Studies (CCSS), Utrecht University, Utrecht 3584 CE, The Netherlands
- Faculdade de Ciências, Universidade de Lisboa, Lisbon PT1749-016, Portugal
- BioISI—Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisbon PT1749-016, Portugal
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7
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Chan LYH, Rø G, Midtbø JE, Di Ruscio F, Watle SSV, Juvet LK, Littmann J, Aavitsland P, Nygård KM, Berg AS, Bukholm G, Kristoffersen AB, Engø-Monsen K, Engebretsen S, Swanson D, Palomares ADL, Lindstrøm JC, Frigessi A, de Blasio BF. Modeling geographic vaccination strategies for COVID-19 in Norway. PLoS Comput Biol 2024; 20:e1011426. [PMID: 38295111 PMCID: PMC10861074 DOI: 10.1371/journal.pcbi.1011426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 02/12/2024] [Accepted: 01/08/2024] [Indexed: 02/02/2024] Open
Abstract
Vaccination was a key intervention in controlling the COVID-19 pandemic globally. In early 2021, Norway faced significant regional variations in COVID-19 incidence and prevalence, with large differences in population density, necessitating efficient vaccine allocation to reduce infections and severe outcomes. This study explored alternative vaccination strategies to minimize health outcomes (infections, hospitalizations, ICU admissions, deaths) by varying regions prioritized, extra doses prioritized, and implementation start time. Using two models (individual-based and meta-population), we simulated COVID-19 transmission during the primary vaccination period in Norway, covering the first 7 months of 2021. We investigated alternative strategies to allocate more vaccine doses to regions with a higher force of infection. We also examined the robustness of our results and highlighted potential structural differences between the two models. Our findings suggest that early vaccine prioritization could reduce COVID-19 related health outcomes by 8% to 20% compared to a baseline strategy without geographic prioritization. For minimizing infections, hospitalizations, or ICU admissions, the best strategy was to initially allocate all available vaccine doses to fewer high-risk municipalities, comprising approximately one-fourth of the population. For minimizing deaths, a moderate level of geographic prioritization, with approximately one-third of the population receiving doubled doses, gave the best outcomes by balancing the trade-off between vaccinating younger people in high-risk areas and older people in low-risk areas. The actual strategy implemented in Norway was a two-step moderate level aimed at maintaining the balance and ensuring ethical considerations and public trust. However, it did not offer significant advantages over the baseline strategy without geographic prioritization. Earlier implementation of geographic prioritization could have more effectively addressed the main wave of infections, substantially reducing the national burden of the pandemic.
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Affiliation(s)
- Louis Yat Hin Chan
- Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway
| | - Gunnar Rø
- Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway
| | - Jørgen Eriksson Midtbø
- Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway
| | - Francesco Di Ruscio
- Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway
| | | | - Lene Kristine Juvet
- Department of Infection Control and Vaccines, Norwegian Institute of Public Health, Oslo, Norway
| | - Jasper Littmann
- Division of Infection Control, Norwegian Institute of Public Health, Oslo, Norway
- Bergen Centre for Ethics and Priority Setting (BCEPS), University of Bergen, Bergen, Norway
| | - Preben Aavitsland
- Division of Infection Control, Norwegian Institute of Public Health, Oslo, Norway
- Pandemic Centre, University of Bergen, Bergen, Norway
| | - Karin Maria Nygård
- Department of Infectious Diseases and Preparedness, Norwegian Institute of Public Health, Oslo, Norway
| | - Are Stuwitz Berg
- Department of Infection Control and Vaccines, Norwegian Institute of Public Health, Oslo, Norway
| | - Geir Bukholm
- Division of Infection Control, Norwegian Institute of Public Health, Oslo, Norway
- Faculty of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Ås, Norway
| | | | | | | | - David Swanson
- Department of Biostatistics, MD Anderson Cancer Center, University of Texas, Houston, Texas, United States of America
| | | | | | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Birgitte Freiesleben de Blasio
- Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo and Oslo University Hospital, Oslo, Norway
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8
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van Boven M, van Dorp CH, Westerhof I, Jaddoe V, Heuvelman V, Duijts L, Fourie E, Sluiter-Post J, van Houten MA, Badoux P, Euser S, Herpers B, Eggink D, de Hoog M, Boom T, Wildenbeest J, Bont L, Rozhnova G, Bonten MJ, Kretzschmar ME, Bruijning-Verhagen P. Estimation of introduction and transmission rates of SARS-CoV-2 in a prospective household study. PLoS Comput Biol 2024; 20:e1011832. [PMID: 38285727 PMCID: PMC10852262 DOI: 10.1371/journal.pcbi.1011832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 02/08/2024] [Accepted: 01/16/2024] [Indexed: 01/31/2024] Open
Abstract
Household studies provide an efficient means to study transmission of infectious diseases, enabling estimation of susceptibility and infectivity by person-type. A main inclusion criterion in such studies is usually the presence of an infected person. This precludes estimation of the hazards of pathogen introduction into the household. Here we estimate age- and time-dependent household introduction hazards together with within household transmission rates using data from a prospective household-based study in the Netherlands. A total of 307 households containing 1,209 persons were included from August 2020 until March 2021. Follow-up of households took place between August 2020 and August 2021 with maximal follow-up per household mostly limited to 161 days. Almost 1 out of 5 households (59/307) had evidence of an introduction of SARS-CoV-2. We estimate introduction hazards and within-household transmission rates in our study population with penalized splines and stochastic epidemic models, respectively. The estimated hazard of introduction of SARS-CoV-2 in the households was lower for children (0-12 years) than for adults (relative hazard: 0.62; 95%CrI: 0.34-1.0). Estimated introduction hazards peaked in mid October 2020, mid December 2020, and mid April 2021, preceding peaks in hospital admissions by 1-2 weeks. Best fitting transmission models included increased infectivity of children relative to adults and adolescents, such that the estimated child-to-child transmission probability (0.62; 95%CrI: 0.40-0.81) was considerably higher than the adult-to-adult transmission probability (0.12; 95%CrI: 0.057-0.19). Scenario analyses indicate that vaccination of adults can strongly reduce household infection attack rates and that adding adolescent vaccination offers limited added benefit.
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Affiliation(s)
- Michiel van Boven
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Center for Complex Systems Studies (CCSS), Utrecht University, Utrecht, The Netherlands
| | - Christiaan H. van Dorp
- Department of Pathology & Cell Biology, Columbia University Irving Medical Center, New York, United States of America
| | - Ilse Westerhof
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | | | | | | | | | | | | | - Paul Badoux
- Regional Public Health Laboratory Kennemerland, Haarlem, the Netherlands
| | - Sjoerd Euser
- Regional Public Health Laboratory Kennemerland, Haarlem, the Netherlands
| | - Bjorn Herpers
- Regional Public Health Laboratory Kennemerland, Haarlem, the Netherlands
| | - Dirk Eggink
- National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - Marieke de Hoog
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Trisja Boom
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Joanne Wildenbeest
- Department of Paediatric Infectious Diseases and Immunology, Wilhelmina Children’s hospital, University Medical Center Utrecht, the Netherlands
| | - Louis Bont
- Department of Paediatric Infectious Diseases and Immunology, Wilhelmina Children’s hospital, University Medical Center Utrecht, the Netherlands
| | - Ganna Rozhnova
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Center for Complex Systems Studies (CCSS), Utrecht University, Utrecht, The Netherlands
- BioISI—Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal
| | - Marc J. Bonten
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Mirjam E. Kretzschmar
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Center for Complex Systems Studies (CCSS), Utrecht University, Utrecht, The Netherlands
| | - Patricia Bruijning-Verhagen
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
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9
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Brackx F, De Smedt B, Molenberghs G. A dashboard for the evaluation of the effect of school closures on wellbeing of children and parents. Arch Public Health 2023; 81:178. [PMID: 37789384 PMCID: PMC10548715 DOI: 10.1186/s13690-023-01114-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 05/18/2023] [Indexed: 10/05/2023] Open
Abstract
BACKGROUND We present a dashboard for the evaluation of the impact of school closures on children and parents during the first wave of the COVID pandemic in 2020 on the various components of wellbeing. METHODS Starting from an explorative literature search by a team of experts from diverse fields (e.g., epidemiology, virology, psychology, education, sociology), we developed a dashboard that allows for the quick evaluation of the general effect of school closures on various indicators of well-being in different groups and for the quality of the available research, at a time where a crisis is ongoing. RESULTS It is concluded that there is evidence that the school closures reduced the transmission of COVID in the first wave in springtime 2020. Nevertheless, a multitude of studies show that the school closures also had a negative impact on different components of wellbeing such as academic achievement, time spent on learning and mental health. Furthermore, the school closures affected not only the children and adolescents, but also the parents that were forced to provide more childcare and help with schoolwork. Longitudinal studies on large representative samples with repeated assessments of wellbeing are necessary to understand the long-term effects of the school closures. CONCLUSIONS The dashboard provides a first visual overview of the effects of school closures on wellbeing, and can serve as the basis for a future more systematic review and meta-analysis of the effects of school closures on wellbeing. It can be considered as a paradigm for rapid obtention of scientific evidence, during a quickly unfolding crisis, also in view of underpinning policy advice.
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Affiliation(s)
| | - Bert De Smedt
- Faculty of Psychology and Educational Sciences, KU Leuven, Louvain, Belgium
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10
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Ziarelli G, Dede' L, Parolini N, Verani M, Quarteroni A. Optimized numerical solutions of SIRDVW multiage model controlling SARS-CoV-2 vaccine roll out: An application to the Italian scenario. Infect Dis Model 2023; 8:672-703. [PMID: 37346476 PMCID: PMC10240908 DOI: 10.1016/j.idm.2023.05.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 05/15/2023] [Accepted: 05/29/2023] [Indexed: 06/23/2023] Open
Abstract
In the context of SARS-CoV-2 pandemic, mathematical modelling has played a fundamental role for making forecasts, simulating scenarios and evaluating the impact of preventive political, social and pharmaceutical measures. Optimal control theory represents a useful mathematical tool to plan the vaccination campaign aimed at eradicating the pandemic as fast as possible. The aim of this work is to explore the optimal prioritisation order for planning vaccination campaigns able to achieve specific goals, as the reduction of the amount of infected, deceased and hospitalized in a given time frame, among age classes. For this purpose, we introduce an age stratified SIR-like epidemic compartmental model settled in an abstract framework for modelling two-doses vaccination campaigns and conceived with the description of COVID19 disease. Compared to other recent works, our model incorporates all stages of the COVID-19 disease, including death or recovery, without accounting for additional specific compartments that would increase computational complexity and that are not relevant for our purposes. Moreover, we introduce an optimal control framework where the model is the state problem while the vaccine doses administered are the control variables. An extensive campaign of numerical tests, featured in the Italian scenario and calibrated on available data from Dipartimento di Protezione Civile Italiana, proves that the presented framework can be a valuable tool to support the planning of vaccination campaigns. Indeed, in each considered scenario, our optimization framework guarantees noticeable improvements in terms of reducing deceased, infected or hospitalized individuals with respect to the baseline vaccination policy.
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Affiliation(s)
| | - Luca Dede'
- MOX, Department of Mathematics, Politecnico di Milano, Milan, Italy
| | - Nicola Parolini
- MOX, Department of Mathematics, Politecnico di Milano, Milan, Italy
| | - Marco Verani
- MOX, Department of Mathematics, Politecnico di Milano, Milan, Italy
| | - Alfio Quarteroni
- MOX, Department of Mathematics, Politecnico di Milano, Milan, Italy
- Institute of Mathematics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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11
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Rennert L, Ma Z. An epidemiological modeling framework to inform institutional-level response to infectious disease outbreaks: A Covid-19 case study. RESEARCH SQUARE 2023:rs.3.rs-3116880. [PMID: 37503237 PMCID: PMC10371141 DOI: 10.21203/rs.3.rs-3116880/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Institutions have an enhanced ability to implement tailored mitigation measures during infectious disease outbreaks. However, macro-level predictive models are inefficient for guiding institutional decision-making due to uncertainty in local-level model input parameters. We present an institutional-level modeling toolkit used to inform prediction, resource procurement and allocation, and policy implementation at Clemson University throughout the Covid-19 pandemic. Through incorporating real-time estimation of disease surveillance and epidemiological measures based on institutional data, we argue this approach helps minimize uncertainties in input parameters presented in the broader literature and increases prediction accuracy. We demonstrate this through case studies at Clemson and other university settings during the Omicron BA.1 and BA.4/BA.5 variant surges. The input parameters of our toolkit are easily adaptable to other institutional settings during future health emergencies. This methodological approach has potential to improve public health response through increasing the capability of institutions to make data-informed decisions that better prioritize the health and safety of their communities while minimizing operational disruptions.
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Rouzine IM, Rozhnova G. Evolutionary implications of SARS-CoV-2 vaccination for the future design of vaccination strategies. COMMUNICATIONS MEDICINE 2023; 3:86. [PMID: 37336956 PMCID: PMC10279745 DOI: 10.1038/s43856-023-00320-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 06/07/2023] [Indexed: 06/21/2023] Open
Abstract
Once the first SARS-CoV-2 vaccine became available, mass vaccination was the main pillar of the public health response to the COVID-19 pandemic. It was very effective in reducing hospitalizations and deaths. Here, we discuss the possibility that mass vaccination might accelerate SARS-CoV-2 evolution in antibody-binding regions compared to natural infection at the population level. Using the evidence of strong genetic variation in antibody-binding regions and taking advantage of the similarity between the envelope proteins of SARS-CoV-2 and influenza, we assume that immune selection pressure acting on these regions of the two viruses is similar. We discuss the consequences of this assumption for SARS-CoV-2 evolution in light of mathematical models developed previously for influenza. We further outline the implications of this phenomenon, if our assumptions are confirmed, for the future design of SARS-CoV-2 vaccination strategies.
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Affiliation(s)
- Igor M Rouzine
- Immunogenetics, Sechenov Institute of Evolutionary Physiology and Biochemistry of Russian Academy of Sciences, Saint-Petersburg, Russia.
| | - Ganna Rozhnova
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
- BioISI - Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal.
- Center for Complex Systems Studies (CCSS), Utrecht University, Utrecht, The Netherlands.
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13
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van Boven M, van Dorp CH, Westerhof I, Jaddoe V, Heuvelman V, Duijts L, Fourie E, Sluiter-Post J, van Houten MA, Badoux P, Euser S, Herpers B, Eggink D, de Hoog M, Boom T, Wildenbeest J, Bont L, Rozhnov G, Bonten MJ, Kretzschmar ME, Bruijning-Verhagen P. Estimation of introduction and transmission rates of SARS-CoV-2 in a prospective household study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.02.23290879. [PMID: 37333399 PMCID: PMC10275010 DOI: 10.1101/2023.06.02.23290879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Household studies provide an efficient means to study transmission of infectious diseases, enabling estimation of individual susceptibility and infectivity. A main inclusion criterion in such studies is often the presence of an infected person. This precludes estimation of the hazards of pathogen introduction into the household. Here we use data from a prospective household-based study to estimate SARS-CoV-2 age- and time-dependent household introduction hazards together with within household transmission rates in the Netherlands from August 2020 to August 2021. Introduction hazards and within-household transmission rates are estimated with penalized splines and stochastic epidemic models, respectively. The estimated hazard of introduction of SARS-CoV-2 in the households was lower for children (0-12 years) than for adults (relative hazard: 0.62; 95%CrI: 0.34-1.0). Estimated introduction hazards peaked in mid October 2020, mid December 2020, and mid April 2021, preceding peaks in hospital admissions by 1-2 weeks. The best fitting transmission models include increased infectivity of children relative to adults and adolescents, such that the estimated child-to-child transmission probability (0.62; 95%CrI: 0.40-0.81) was considerably higher than the adult-to-adult transmission probability (0.12; 95%CrI: 0.057-0.19). Scenario analyses show that vaccination of adults could have strongly reduced infection attack rates in households and that adding adolescent vaccination would have offered limited added benefit.
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Affiliation(s)
- Michiel van Boven
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Center for Complex Systems Studies (CCSS), Utrecht University, Utrecht, The Netherlands
| | - Christiaan H van Dorp
- Department of Pathology & Cell Biology, Columbia University Irving Medical Center, New York, United States
| | - Ilse Westerhof
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | | | | | | | | | | | | | - Paul Badoux
- Regional Public Health Laboratory Kennemerland, Haarlem, the Netherlands
| | - Sjoerd Euser
- Regional Public Health Laboratory Kennemerland, Haarlem, the Netherlands
| | - Bjorn Herpers
- Regional Public Health Laboratory Kennemerland, Haarlem, the Netherlands
| | - Dirk Eggink
- National Institute for Public Health, and the Environment, Bilthoven, the Netherlands
| | - Marieke de Hoog
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Trisja Boom
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Joanne Wildenbeest
- Department of Paediatric Infectious Diseases and Immunology, Wilhelmina Children's hospital, University Medical Center Utrecht, the Netherlands
| | - Louis Bont
- Department of Paediatric Infectious Diseases and Immunology, Wilhelmina Children's hospital, University Medical Center Utrecht, the Netherlands
| | - Ganna Rozhnov
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Center for Complex Systems Studies (CCSS), Utrecht University, Utrecht, The Netherlands
- BioISI-Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal
| | - Marc J Bonten
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Mirjam E Kretzschmar
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Center for Complex Systems Studies (CCSS), Utrecht University, Utrecht, The Netherlands
| | - Patricia Bruijning-Verhagen
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
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14
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Colosi E, Bassignana G, Barrat A, Lina B, Vanhems P, Bielicki J, Colizza V. Minimising school disruption under high incidence conditions due to the Omicron variant in France, Switzerland, Italy, in January 2022. Euro Surveill 2023; 28:2200192. [PMID: 36729116 PMCID: PMC9896604 DOI: 10.2807/1560-7917.es.2023.28.5.2200192] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 11/21/2022] [Indexed: 02/03/2023] Open
Abstract
BackgroundAs record cases of Omicron variant were registered in Europe in early 2022, schools remained a vulnerable setting undergoing large disruption.AimThrough mathematical modelling, we compared school protocols of reactive screening, regular screening, and reactive class closure implemented in France, in Baselland (Switzerland), and in Italy, respectively, and assessed them in terms of case prevention, testing resource demand, and schooldays lost.MethodsWe used a stochastic agent-based model of SARS-CoV-2 transmission in schools accounting for within- and across-class contacts from empirical contact data. We parameterised it to the Omicron BA.1 variant to reproduce the French Omicron wave in January 2022. We simulated the three protocols to assess their costs and effectiveness for varying peak incidence rates in the range experienced by European countries.ResultsWe estimated that at the high incidence rates registered in France during the Omicron BA.1 wave in January 2022, the reactive screening protocol applied in France required higher test resources compared with the weekly screening applied in Baselland (0.50 vs 0.45 tests per student-week), but achieved considerably lower control (8% vs 21% reduction of peak incidence). The reactive class closure implemented in Italy was predicted to be very costly, leading to > 20% student-days lost.ConclusionsAt high incidence conditions, reactive screening protocols generate a large and unplanned demand in testing resources, for marginal control of school transmissions. Comparable or lower resources could be more efficiently used through weekly screening. Our findings can help define incidence levels triggering school protocols and optimise their cost-effectiveness.
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Affiliation(s)
- Elisabetta Colosi
- Sorbonne Université, INSERM, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
| | - Giulia Bassignana
- Sorbonne Université, INSERM, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
| | - Alain Barrat
- Aix Marseille Univ, Université de Toulon, CNRS, CPT, Turing Center for Living Systems, Marseille, France
| | - Bruno Lina
- Centre International de Recherche en Infectiologie (CIRI), Virpath Laboratory, INSERM U1111, CNRS-UMR 5308, École Normale Supérieure de Lyon, Université Claude Bernard Lyon, Lyon University, Lyon, France
- National Reference Center for Respiratory Viruses, Department of Virology, Infective Agents Institute, Croix-Rousse Hospital, Hospices Civils de Lyon, Lyon, France
| | - Philippe Vanhems
- Centre International de Recherche en Infectiologie (CIRI), Public Health, Epidemiology and Evolutionary Ecology of Infectious Diseases (PHE3ID) - Inserm - U1111 - UCBL Lyon 1 - CNRS -UMR5308 - ENS de Lyon, Lyon, France
- Service d'Hygiène, Épidémiologie, Infectiovigilance et Prévention, Hospices Civils de Lyon, Lyon, France
| | - Julia Bielicki
- Paediatric Infectious Diseases, University of Basel Children's Hospital, Basel, Switzerland
| | - Vittoria Colizza
- Sorbonne Université, INSERM, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
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15
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Cassidy T, Gillich P, Humphries AR, van Dorp CH. Numerical methods and hypoexponential approximations for gamma distributed delay differential equations. IMA JOURNAL OF APPLIED MATHEMATICS 2022; 87:1043-1089. [PMID: 36691452 PMCID: PMC9850366 DOI: 10.1093/imamat/hxac027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 07/30/2022] [Accepted: 09/20/2022] [Indexed: 06/17/2023]
Abstract
Gamma distributed delay differential equations (DDEs) arise naturally in many modelling applications. However, appropriate numerical methods for generic gamma distributed DDEs have not previously been implemented. Modellers have therefore resorted to approximating the gamma distribution with an Erlang distribution and using the linear chain technique to derive an equivalent system of ordinary differential equations (ODEs). In this work, we address the lack of appropriate numerical tools for gamma distributed DDEs in two ways. First, we develop a functional continuous Runge-Kutta (FCRK) method to numerically integrate the gamma distributed DDE without resorting to Erlang approximation. We prove the fourth-order convergence of the FCRK method and perform numerical tests to demonstrate the accuracy of the new numerical method. Nevertheless, FCRK methods for infinite delay DDEs are not widely available in existing scientific software packages. As an alternative approach to solving gamma distributed DDEs, we also derive a hypoexponential approximation of the gamma distributed DDE. This hypoexponential approach is a more accurate approximation of the true gamma distributed DDE than the common Erlang approximation but, like the Erlang approximation, can be formulated as a system of ODEs and solved numerically using standard ODE software. Using our FCRK method to provide reference solutions, we show that the common Erlang approximation may produce solutions that are qualitatively different from the underlying gamma distributed DDE. However, the proposed hypoexponential approximations do not have this limitation. Finally, we apply our hypoexponential approximations to perform statistical inference on synthetic epidemiological data to illustrate the utility of the hypoexponential approximation.
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Affiliation(s)
- Tyler Cassidy
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - Peter Gillich
- Department of Mathematics and Statistics, McGill University, Montreal, Quebec 3A 0G4, Canada
| | - Antony R Humphries
- Departments of Mathematics and Statistics, and Physiology, McGill University, Montreal, Quebec 3A 0G4, Canada
| | - Christiaan H van Dorp
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
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16
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Teslya A, Rozhnova G, Pham TM, van Wees DA, Nunner H, Godijk NG, Bootsma M, Kretzschmar ME. The importance of sustained compliance with physical distancing during COVID-19 vaccination rollout. COMMUNICATIONS MEDICINE 2022; 2:146. [PMID: 36402924 PMCID: PMC9675824 DOI: 10.1038/s43856-022-00207-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 10/26/2022] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND Increasing vaccination coverage against SARS-CoV-2 enabled relaxation of lockdowns in many countries in Europe. As the vaccination rollouts progressed, the public health authorities were seeking recommendations on the continuation of physical distancing measures during ongoing vaccination rollouts. Compliance with these measures was declining while more transmissible virus variants have emerged. METHODS We used a SARS-CoV-2 transmission model to investigate the feedback between compliance, infection incidence, and vaccination coverage. We quantified our findings in terms of cumulative number of new hospitalisations three and six months after the start of vaccination. RESULTS Our results suggest that the combination of fast waning compliance in non-vaccinated individuals, low compliance in vaccinated individuals, low vaccine efficacy against infection and more transmissible virus variants may result in a higher cumulative number of new hospitalisations than in a situation without vaccination. These adverse effects can be alleviated by deploying behavioural interventions that should preferably target both vaccinated and non-vaccinated individuals. The choice of the most appropriate intervention depends on vaccination rate and vaccine efficacy against infection. CONCLUSIONS Supplementary behavioural interventions aiming to boost compliance to physical distancing measures can improve the outcome of vaccination programmes, until vaccination coverage is sufficiently high. For optimal results, these interventions should be selected based on the vaccine efficacy against infection and expected vaccination rate. While we considered the dynamics of SARS-CoV-2, the qualitative effects of the interplay between infectious disease spread and behavior on the outcomes of a vaccination programme can be used as guidance in a future similar pandemic.
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Affiliation(s)
- Alexandra Teslya
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
| | - Ganna Rozhnova
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
- BioISI-Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal.
- Centre for Complex System Studies (CCSS), Utrecht University, Utrecht, The Netherlands.
| | - Thi Mui Pham
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Daphne A van Wees
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Hendrik Nunner
- Centre for Complex System Studies (CCSS), Utrecht University, Utrecht, The Netherlands
- Department of Sociology / ICS, Utrecht University, Utrecht, The Netherlands
| | - Noortje G Godijk
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Martin Bootsma
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of Mathematics, Faculty of Sciences, Utrecht University, Utrecht, The Netherlands
| | - Mirjam E Kretzschmar
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Centre for Complex System Studies (CCSS), Utrecht University, Utrecht, The Netherlands
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17
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Modelling the impact of school reopening and contact tracing strategies on Covid-19 dynamics in different epidemiologic settings in Brazil. GLOBAL EPIDEMIOLOGY 2022; 4:100094. [DOI: 10.1016/j.gloepi.2022.100094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 10/21/2022] [Accepted: 11/08/2022] [Indexed: 11/13/2022] Open
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18
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Aouissi HA, Kechebar MSA, Ababsa M, Roufayel R, Neji B, Petrisor AI, Hamimes A, Epelboin L, Ohmagari N. The Importance of Behavioral and Native Factors on COVID-19 Infection and Severity: Insights from a Preliminary Cross-Sectional Study. Healthcare (Basel) 2022; 10:1341. [PMID: 35885867 PMCID: PMC9323463 DOI: 10.3390/healthcare10071341] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 07/13/2022] [Accepted: 07/15/2022] [Indexed: 12/12/2022] Open
Abstract
The COVID-19 pandemic has had a major impact on a global scale. Understanding the innate and lifestyle-related factors influencing the rate and severity of COVID-19 is important for making evidence-based recommendations. This cross-sectional study aims at establishing a potential relationship between human characteristics and vulnerability/resistance to SARS-CoV-2. We hypothesize that the impact of the virus is not the same due to cultural and ethnic differences. A cross-sectional study was performed using an online questionnaire. The methodology included the development of a multi-language survey, expert evaluation, and data analysis. Data were collected using a 13-item pre-tested questionnaire based on a literature review between 9 December 2020 and 21 July 2021. Data were statistically analyzed using logistic regression. For a total of 1125 respondents, 332 (29.5%) were COVID-19 positive; among them, 130 (11.5%) required home-based treatment, and 14 (1.2%) intensive care. The significant and most influential factors on infection included age, physical activity, and health status (p < 0.05), i.e., better physical activity and better health status significantly reduced the possibility of infection, while older age significantly increased it. The severity of infection was negatively associated with the acceptance (adherence and respect) of preventive measures and positively associated with tobacco (p < 0.05), i.e., smoking regularly significantly increases the severity of COVID-19 infection. This suggests the importance of behavioral factors compared to innate ones. Apparently, individual behavior is mainly responsible for the spread of the virus. Therefore, adopting a healthy lifestyle and scrupulously observing preventive measures, including vaccination, would greatly limit the probability of infection and prevent the development of severe COVID-19.
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Affiliation(s)
- Hani Amir Aouissi
- Scientific and Technical Research Center on Arid Regions (CRSTRA), Biskra 07000, Algeria; (M.S.A.K.); (M.A.)
- Laboratoire de Recherche et d’Etude en Aménagement et Urbanisme (LREAU), Université des Sciences et de la Technologie (USTHB), Algiers 16000, Algeria
- Environmental Research Center (CRE), Badji-Mokhtar Annaba University, Annaba 23000, Algeria
| | - Mohamed Seif Allah Kechebar
- Scientific and Technical Research Center on Arid Regions (CRSTRA), Biskra 07000, Algeria; (M.S.A.K.); (M.A.)
| | - Mostefa Ababsa
- Scientific and Technical Research Center on Arid Regions (CRSTRA), Biskra 07000, Algeria; (M.S.A.K.); (M.A.)
| | - Rabih Roufayel
- College of Engineering and Technology, American University of the Middle East, Kuwait;
| | - Bilel Neji
- College of Engineering and Technology, American University of the Middle East, Kuwait;
| | - Alexandru-Ionut Petrisor
- Doctoral School of Urban Planning, Ion Mincu University of Architecture and Urbanism, 010014 Bucharest, Romania;
- National Institute for Research and Development in Tourism, 50741 Bucharest, Romania
- National Institute for Research and Development in Constructions, Urbanism and Sustainable Spatial Development URBAN-INCERC, 021652 Bucharest, Romania
| | - Ahmed Hamimes
- Faculty of Medicine, University Salah Boubnider of Constantine 3, Constantine 25000, Algeria;
| | - Loïc Epelboin
- Infectious and Tropical Diseases Department, Centre Hospitalier de Cayenne Andrée Rosemon, 97306 Cayenne, France;
- Centre d’Investigation Clinique (CIC INSERM 1424), Centre Hospitalier de Cayenne Andrée Rosemon, 97306 Cayenne, France
| | - Norio Ohmagari
- Disease Control and Prevention Center, National Center for Global Health and Medicine, Tokyo 162-8655, Japan;
- AMR Clinical Reference Center, National Center for Global Health and Medicine, Tokyo 162-8655, Japan
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19
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Colosi E, Bassignana G, Contreras DA, Poirier C, Boëlle PY, Cauchemez S, Yazdanpanah Y, Lina B, Fontanet A, Barrat A, Colizza V. Screening and vaccination against COVID-19 to minimise school closure: a modelling study. THE LANCET. INFECTIOUS DISEASES 2022; 22:977-989. [PMID: 35378075 PMCID: PMC8975262 DOI: 10.1016/s1473-3099(22)00138-4] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 01/26/2022] [Accepted: 02/15/2022] [Indexed: 12/18/2022]
Abstract
BACKGROUND Schools were closed extensively in 2020-21 to counter SARS-CoV-2 spread, impacting students' education and wellbeing. With highly contagious variants expanding in Europe, safe options to maintain schools open are urgently needed. By estimating school-specific transmissibility, our study evaluates costs and benefits of different protocols for SARS-CoV-2 control at school. METHODS We developed an agent-based model of SARS-CoV-2 transmission in schools. We used empirical contact data in a primary and a secondary school and data from pilot screenings in 683 schools during the alpha variant (B.1.1.7) wave in March-June, 2021, in France. We fitted the model to observed school prevalence to estimate the school-specific effective reproductive number for the alpha (Ralpha) and delta (B.1.617.2; Rdelta) variants and performed a cost-benefit analysis examining different intervention protocols. FINDINGS We estimated Ralpha to be 1·40 (95% CI 1·35-1·45) in the primary school and 1·46 (1·41-1·51) in the secondary school during the spring wave, higher than the time-varying reproductive number estimated from community surveillance. Considering the delta variant and vaccination coverage in Europe as of mid-September, 2021, we estimated Rdelta to be 1·66 (1·60-1·71) in primary schools and 1·10 (1·06-1·14) in secondary schools. Under these conditions, weekly testing of 75% of unvaccinated students (PCR tests on saliva samples in primary schools and lateral flow tests in secondary schools), in addition to symptom-based testing, would reduce cases by 34% (95% CI 32-36) in primary schools and 36% (35-39) in secondary schools compared with symptom-based testing alone. Insufficient adherence was recorded in pilot screening (median ≤53%). Regular testing would also reduce student-days lost up to 80% compared with reactive class closures. Moderate vaccination coverage in students would still benefit from regular testing for additional control-ie, weekly testing 75% of unvaccinated students would reduce cases compared with symptom-based testing only, by 23% in primary schools when 50% of children are vaccinated. INTERPRETATION The COVID-19 pandemic will probably continue to pose a risk to the safe and normal functioning of schools. Extending vaccination coverage in students, complemented by regular testing with good adherence, are essential steps to keep schools open when highly transmissible variants are circulating. FUNDING EU Framework Programme for Research and Innovation Horizon 2020, Horizon Europe Framework Programme, Agence Nationale de la Recherche, ANRS-Maladies Infectieuses Émergentes.
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Affiliation(s)
- Elisabetta Colosi
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
| | - Giulia Bassignana
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
| | - Diego Andrés Contreras
- Aix-Marseille Univ, Université de Toulon, CNRS, Centre de Physique Théorique, Turing Center for Living Systems, Marseille, France
| | - Canelle Poirier
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
| | - Pierre-Yves Boëlle
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
| | - Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France
| | - Yazdan Yazdanpanah
- Infection, Antimicrobials, Modelling, Evolution, INSERM, Université de Paris, Paris, France; Bichat Claude Bernard Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Bruno Lina
- National Reference Center for Respiratory Viruses, Department of Virology, Infective Agents Institute, Croix-Rousse Hospital, Hospices Civils de Lyon, Lyon, France; Virpath Laboratory, International Center of Research in Infectiology, INSERM U1111, CNRS-UMR 5308, École Normale Supérieure de Lyon, Université Claude Bernard Lyon, Lyon University, Lyon, France
| | - Arnaud Fontanet
- Emerging Diseases Epidemiology Unit, Institut Pasteur, Paris, France; PACRI Unit, Conservatoire National des Arts et Metiers, Paris, France
| | - Alain Barrat
- Aix-Marseille Univ, Université de Toulon, CNRS, Centre de Physique Théorique, Turing Center for Living Systems, Marseille, France; Tokyo Tech World Research Hub Initiative, Tokyo Institute of Technology, Tokyo, Japan
| | - Vittoria Colizza
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France; Tokyo Tech World Research Hub Initiative, Tokyo Institute of Technology, Tokyo, Japan.
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20
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Berec L, Smyčka J, Levínský R, Hromádková E, Šoltés M, Šlerka J, Tuček V, Trnka J, Šmíd M, Zajíček M, Diviák T, Neruda R, Vidnerová P. Delays, Masks, the Elderly, and Schools: First Covid-19 Wave in the Czech Republic. Bull Math Biol 2022; 84:75. [PMID: 35726074 PMCID: PMC9208712 DOI: 10.1007/s11538-022-01031-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 05/16/2022] [Indexed: 11/29/2022]
Abstract
Running across the globe for nearly 2 years, the Covid-19 pandemic keeps demonstrating its strength. Despite a lot of understanding, uncertainty regarding the efficiency of interventions still persists. We developed an age-structured epidemic model parameterized with epidemiological and sociological data for the first Covid-19 wave in the Czech Republic and found that (1) starting the spring 2020 lockdown 4 days earlier might prevent half of the confirmed cases by the end of lockdown period, (2) personal protective measures such as face masks appear more effective than just a realized reduction in social contacts, (3) the strategy of sheltering just the elderly is not at all effective, and (4) leaving schools open is a risky strategy. Despite vaccination programs, evidence-based choice and timing of non-pharmaceutical interventions remains an effective weapon against the Covid-19 pandemic.
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Affiliation(s)
- Luděk Berec
- Department of Mathematics, Centre for Mathematical Biology, Faculty of Science, University of South Bohemia, Branišovská 1760, 37005, České Budějovice, Czech Republic. .,Czech Academy of Sciences, Biology Centre, Institute of Entomology, Branišovská 31, 37005, České Budějovice, Czech Republic. .,Centre for Modelling of Biological and Social Processes, Na břehu 497/15, 19000, Prague 9, Czech Republic.
| | - Jan Smyčka
- Center for Theoretical Studies, Husova 4, 11000, Prague 1, Czech Republic
| | - René Levínský
- Centre for Modelling of Biological and Social Processes, Na břehu 497/15, 19000, Prague 9, Czech Republic.,CERGE-EI, Politických vězňů 7, 11121, Prague 1, Czech Republic
| | - Eva Hromádková
- CERGE-EI, Politických vězňů 7, 11121, Prague 1, Czech Republic
| | - Michal Šoltés
- CERGE-EI, Politických vězňů 7, 11121, Prague 1, Czech Republic
| | - Josef Šlerka
- Centre for Modelling of Biological and Social Processes, Na břehu 497/15, 19000, Prague 9, Czech Republic.,New Media Studies, Faculty of Arts, Charles University, Na Příkopě 29, 11000, Prague 1, Czech Republic
| | - Vít Tuček
- Centre for Modelling of Biological and Social Processes, Na břehu 497/15, 19000, Prague 9, Czech Republic.,Department of Mathematics, University of Zagreb, Bijenička 30, 10000, Zagreb, Croatia
| | - Jan Trnka
- Department of Biochemistry, Cell and Molecular Biology, Third Faculty of Medicine, Charles University, Ruská 87, 100 00, Prague 10, Czech Republic
| | - Martin Šmíd
- Centre for Modelling of Biological and Social Processes, Na břehu 497/15, 19000, Prague 9, Czech Republic.,Czech Academy of Sciences, Institute of Information Theory and Automation, Pod Vodárenskou věží 4, 18200, Prague 8, Czech Republic
| | - Milan Zajíček
- Centre for Modelling of Biological and Social Processes, Na břehu 497/15, 19000, Prague 9, Czech Republic.,Czech Academy of Sciences, Institute of Information Theory and Automation, Pod Vodárenskou věží 4, 18200, Prague 8, Czech Republic
| | - Tomáš Diviák
- Centre for Modelling of Biological and Social Processes, Na břehu 497/15, 19000, Prague 9, Czech Republic.,Department of Criminology, School of Social Sciences, University of Manchester, Oxford Rd, Manchester, UK
| | - Roman Neruda
- Centre for Modelling of Biological and Social Processes, Na břehu 497/15, 19000, Prague 9, Czech Republic.,Czech Academy of Sciences, Institute of Computer Science, Pod Vodárenskou věží 2, 18200, Prague 8, Czech Republic
| | - Petra Vidnerová
- Centre for Modelling of Biological and Social Processes, Na břehu 497/15, 19000, Prague 9, Czech Republic.,Czech Academy of Sciences, Institute of Computer Science, Pod Vodárenskou věží 2, 18200, Prague 8, Czech Republic
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21
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Zuo C, Meng Z, Zhu F, Zheng Y, Ling Y. Assessing Vaccination Prioritization Strategies for COVID-19 in South Africa Based on Age-Specific Compartment Model. Front Public Health 2022; 10:876551. [PMID: 35784231 PMCID: PMC9240634 DOI: 10.3389/fpubh.2022.876551] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 05/11/2022] [Indexed: 02/05/2023] Open
Abstract
The vaccines are considered to be important for the prevention and control of coronavirus disease 2019 (COVID-19). However, considering the limited vaccine supply within an extended period of time in many countries where COVID-19 vaccine booster shot are taken and new vaccines are developed to suppress the mutation of virus, designing an effective vaccination strategy is extremely important to reduce the number of deaths and infections. Then, the simulations were implemented to study the relative reduction in morbidity and mortality of vaccine allocation strategies by using the proposed model and actual South Africa's epidemiological data. Our results indicated that in light of South Africa's demographics, vaccinating older age groups (>60 years) largely reduced the cumulative deaths and the "0-20 first" strategy was the most effective way to reduce confirmed cases. In addition, "21-30 first" and "31-40 first" strategies have also had a positive effect. Partial vaccination resulted in lower numbers of infections and deaths under different control measures compared with full vaccination in low-income countries. In addition, we analyzed the sensitivity of daily testing volume and infection rate, which are critical to optimize vaccine allocation. However, comprehensive reduction in infections was mainly affected by the vaccine proportion of the target age group. An increase in the proportion of vaccines given priority to "0-20" groups always had a favorable effect, and the prioritizing vaccine allocation among the "60+" age group with 60% of the total amount of vaccine consistently resulted in the greatest reduction in deaths. Meanwhile, we observed a significant distinction in the effect of COVID-19 vaccine allocation policies under varying priority strategies on relative reductions in the effective reproduction number. Our results could help evaluate to control measures performance and the improvement of vaccine allocation strategy for COVID-19 epidemic.
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Affiliation(s)
- Chao Zuo
- School of Management Engineering and E-Commerce, Zhejiang Gongshang University, Hangzhou, China
| | | | | | | | - Yuting Ling
- School of Management Engineering and E-Commerce, Zhejiang Gongshang University, Hangzhou, China
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22
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Keeling MJ, Moore SE. An assessment of the vaccination of school-aged children in England against SARS-CoV-2. BMC Med 2022; 20:196. [PMID: 35581585 PMCID: PMC9113775 DOI: 10.1186/s12916-022-02379-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 04/20/2022] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Children and young persons are known to have a high number of close interactions, often within the school environment, which can facilitate rapid spread of infection; yet for SARS-CoV-2, it is the elderly and vulnerable that suffer the greatest health burden. Vaccination, initially targeting the elderly and vulnerable before later expanding to the entire adult population, has been transformative in the control of SARS-CoV-2 in England. However, early concerns over adverse events and the lower risk associated with infection in younger individuals means that the expansion of the vaccine programme to those under 18 years of age needs to be rigorously and quantitatively assessed. METHODS Here, using a bespoke mathematical model matched to case and hospital data for England, we consider the potential impact of vaccinating 12-17 and 5-11-year-olds. This analysis is reported from an early model (generated in June 2021) that formed part of the evidence base for the decisions in England, and a later model (from November 2021) that benefits from a richer understanding of vaccine efficacy, greater knowledge of the Delta variant wave and uses data on the rate of vaccine administration. For both models, we consider the population wide impact of childhood vaccination as well as the specific impact on the age groups targeted for vaccination. RESULTS Projections from June suggested that an expansion of the vaccine programme to those 12-17 years old could generate substantial reductions in infection, hospital admission and deaths in the entire population, depending on population behaviour following the relaxation of control measures. The benefits within the 12-17-year-old cohort were less marked, saving between 660 and 1100 (95% PI (prediction interval) 280-2300) hospital admissions and between 22 and 38 (95% PI 9-91) deaths depending on assumed population behaviour. For the more recent model, the benefits within this age group are reduced, saving on average 630 (95% PI 300-1300) hospital admissions and 11 (95% PI 5-28) deaths for 80% vaccine uptake, while the benefits to the wider population represent a reduction of 8-10% in hospital admissions and deaths. The vaccination of 5-11-year-olds is projected to have a far smaller impact, in part due to the later roll-out of vaccines to this age group. CONCLUSIONS Vaccination of 12-170-year-olds and 5-11-year-olds is projected to generate a reduction in infection, hospital admission and deaths for both the age groups involved and the population in general. For any decision involving childhood vaccination, these benefits needs to be balanced against potential adverse events from the vaccine, the operational constraints on delivery and the potential for diverting resources from other public health campaigns.
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Affiliation(s)
- Matt J Keeling
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK.
| | - Sam E Moore
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK
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23
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One-Size-Fits-All Policies Are Unacceptable: A Sustainable Management and Decision-Making Model for Schools in the Post-COVID-19 Era. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19105913. [PMID: 35627450 PMCID: PMC9140660 DOI: 10.3390/ijerph19105913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 05/10/2022] [Accepted: 05/11/2022] [Indexed: 01/27/2023]
Abstract
This paper proposes a sustainable management and decision-making model for COVID-19 control in schools, which makes improvements to current policies and strategies. It is not a case study of any specific school or country. The term one-size-fits-all has two meanings: being blind to the pandemic, and conducting inflexible and harsh policies. The former strategy leads to more casualties and does potential harm to children. Conversely, under long-lasting strict policies, people feel exhausted. Therefore, some administrators pretend that they are working hard for COVID-19 control, and people pretend to follow pandemic control rules. The proposed model helps to alleviate these problems and improve management efficiency. A customized queue model is introduced to control social gatherings. An indoor–outdoor tracking system is established. Based on tracing data, we can assess people’s infection risk, and allocate medical resources more effectively in case of emergency. We consider both social and technical feasibility. Test results demonstrate the improvements and effectiveness of the model. In conclusion, the model has patched up certain one-size-fits-all strategies to balance pandemic control and normal life.
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24
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Musa SS, Tariq A, Yuan L, Haozhen W, He D. Infection fatality rate and infection attack rate of COVID-19 in South American countries. Infect Dis Poverty 2022. [PMID: 35382879 DOI: 10.21203/rs.3.rs-1126392/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023] Open
Abstract
BACKGROUND The ongoing COVID-19 pandemic hit South America badly with multiple waves. Different COVID-19 variants have been storming across the region, leading to more severe infections and deaths even in places with high vaccination coverage. This study aims to assess the spatiotemporal variability of the COVID-19 pandemic and estimate the infection fatality rate (IFR), infection attack rate (IAR) and reproduction number ([Formula: see text]) for twelve most affected South American countries. METHODS We fit a susceptible-exposed-infectious-recovered (SEIR)-based model with a time-varying transmission rate to the reported COVID-19 deaths for the twelve South American countries with the highest mortalities. Most of the epidemiological datasets analysed in this work are retrieved from the disease surveillance systems by the World Health Organization, Johns Hopkins Coronavirus Resource Center and Our World in Data. We investigate the COVID-19 mortalities in these countries, which could represent the situation for the overall South American region. We employ COVID-19 dynamic model with-and-without vaccination considering time-varying flexible transmission rate to estimate IFR, IAR and [Formula: see text] of COVID-19 for the South American countries. RESULTS We simulate the model in each scenario under suitable parameter settings and yield biologically reasonable estimates for IFR (varies between 0.303% and 0.723%), IAR (varies between 0.03 and 0.784) and [Formula: see text] (varies between 0.7 and 2.5) for the 12 South American countries. We observe that the severity, dynamical patterns of deaths and time-varying transmission rates among the countries are highly heterogeneous. Further analysis of the model with the effect of vaccination highlights that increasing the vaccination rate could help suppress the pandemic in South America. CONCLUSIONS This study reveals possible reasons for the two waves of COVID-19 outbreaks in South America. We observed reductions in the transmission rate corresponding to each wave plausibly due to improvement in nonpharmaceutical interventions measures and human protective behavioral reaction to recent deaths. Thus, strategies coupling social distancing and vaccination could substantially suppress the mortality rate of COVID-19 in South America.
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Affiliation(s)
- Salihu Sabiu Musa
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
- Department of Mathematics, Kano University of Science and Technology, Wudil, Nigeria
| | - Amna Tariq
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Liu Yuan
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Wei Haozhen
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Daihai He
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China.
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25
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Musa SS, Tariq A, Yuan L, Haozhen W, He D. Infection fatality rate and infection attack rate of COVID-19 in South American countries. Infect Dis Poverty 2022; 11:40. [PMID: 35382879 PMCID: PMC8983329 DOI: 10.1186/s40249-022-00961-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 03/14/2022] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND The ongoing COVID-19 pandemic hit South America badly with multiple waves. Different COVID-19 variants have been storming across the region, leading to more severe infections and deaths even in places with high vaccination coverage. This study aims to assess the spatiotemporal variability of the COVID-19 pandemic and estimate the infection fatality rate (IFR), infection attack rate (IAR) and reproduction number ([Formula: see text]) for twelve most affected South American countries. METHODS We fit a susceptible-exposed-infectious-recovered (SEIR)-based model with a time-varying transmission rate to the reported COVID-19 deaths for the twelve South American countries with the highest mortalities. Most of the epidemiological datasets analysed in this work are retrieved from the disease surveillance systems by the World Health Organization, Johns Hopkins Coronavirus Resource Center and Our World in Data. We investigate the COVID-19 mortalities in these countries, which could represent the situation for the overall South American region. We employ COVID-19 dynamic model with-and-without vaccination considering time-varying flexible transmission rate to estimate IFR, IAR and [Formula: see text] of COVID-19 for the South American countries. RESULTS We simulate the model in each scenario under suitable parameter settings and yield biologically reasonable estimates for IFR (varies between 0.303% and 0.723%), IAR (varies between 0.03 and 0.784) and [Formula: see text] (varies between 0.7 and 2.5) for the 12 South American countries. We observe that the severity, dynamical patterns of deaths and time-varying transmission rates among the countries are highly heterogeneous. Further analysis of the model with the effect of vaccination highlights that increasing the vaccination rate could help suppress the pandemic in South America. CONCLUSIONS This study reveals possible reasons for the two waves of COVID-19 outbreaks in South America. We observed reductions in the transmission rate corresponding to each wave plausibly due to improvement in nonpharmaceutical interventions measures and human protective behavioral reaction to recent deaths. Thus, strategies coupling social distancing and vaccination could substantially suppress the mortality rate of COVID-19 in South America.
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Affiliation(s)
- Salihu Sabiu Musa
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
- Department of Mathematics, Kano University of Science and Technology, Wudil, Nigeria
| | - Amna Tariq
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA USA
| | - Liu Yuan
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Wei Haozhen
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Daihai He
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
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26
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Ghosh AK, Venkatraman S, Reshetnyak E, Rajan M, An A, Chae JK, Unruh MA, Abramson D, DiMaggio C, Hupert N. Association between city-wide lockdown and COVID-19 hospitalization rates in multigenerational households in New York City. PLoS One 2022; 17:e0266127. [PMID: 35353857 PMCID: PMC8967012 DOI: 10.1371/journal.pone.0266127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 03/15/2022] [Indexed: 11/18/2022] Open
Abstract
Background
City-wide lockdowns and school closures have demonstrably impacted COVID-19 transmission. However, simulation studies have suggested an increased risk of COVID-19 related morbidity for older individuals inoculated by house-bound children. This study examines whether the March 2020 lockdown in New York City (NYC) was associated with higher COVID-19 hospitalization rates in neighborhoods with larger proportions of multigenerational households.
Methods
We obtained daily age-segmented COVID-19 hospitalization counts in each of 166 ZIP code tabulation areas (ZCTAs) in NYC. Using Bayesian Poisson regression models that account for spatiotemporal dependencies between ZCTAs, as well as socioeconomic risk factors, we conducted a difference-in-differences study amongst ZCTA-level hospitalization rates from February 23 to May 2, 2020. We compared ZCTAs in the lowest quartile of multigenerational housing to other quartiles before and after the lockdown.
Findings
Among individuals over 55 years, the lockdown was associated with higher COVID-19 hospitalization rates in ZCTAs with more multigenerational households. The greatest difference occurred three weeks after lockdown: Q2 vs. Q1: 54% increase (95% Bayesian credible intervals: 22–96%); Q3 vs. Q1: 48% (17–89%); Q4 vs. Q1: 66% (30–211%). After accounting for pandemic-related population shifts, a significant difference was observed only in Q4 ZCTAs: 37% (7–76%).
Interpretation
By increasing house-bound mixing across older and younger age groups, city-wide lockdown mandates imposed during the growth of COVID-19 cases may have inadvertently, but transiently, contributed to increased transmission in multigenerational households.
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Affiliation(s)
- Arnab K. Ghosh
- Department of Medicine, Weill Cornell Medical College, Cornell University, New York, New York, United States of America
- * E-mail:
| | - Sara Venkatraman
- Department of Statistics and Data Science, Cornell University, Ithaca, New York, United States of America
| | - Evgeniya Reshetnyak
- Department of Medicine, Weill Cornell Medical College, Cornell University, New York, New York, United States of America
| | - Mangala Rajan
- Department of Medicine, Weill Cornell Medical College, Cornell University, New York, New York, United States of America
| | - Anjile An
- Department of Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, New York, United States of America
| | - John K. Chae
- Department of Medicine, Weill Cornell Medical College, Cornell University, New York, New York, United States of America
| | - Mark A. Unruh
- Department of Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, New York, United States of America
| | - David Abramson
- School of Global Public Health, New York University, New York, New York, United States of America
| | - Charles DiMaggio
- Department of Surgery, New York University School of Medicine, New York, New York, United States of America
| | - Nathaniel Hupert
- Department of Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, New York, United States of America
- Cornell Center for Disease and Disaster Preparedness, Weill Cornell Medical College, Cornell University, New York, New York, United States of America
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27
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Kretzschmar ME, Ashby B, Fearon E, Overton CE, Panovska-Griffiths J, Pellis L, Quaife M, Rozhnova G, Scarabel F, Stage HB, Swallow B, Thompson RN, Tildesley MJ, Villela D. Challenges for modelling interventions for future pandemics. Epidemics 2022; 38:100546. [PMID: 35183834 PMCID: PMC8830929 DOI: 10.1016/j.epidem.2022.100546] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 02/04/2022] [Accepted: 02/09/2022] [Indexed: 12/16/2022] Open
Abstract
Mathematical modelling and statistical inference provide a framework to evaluate different non-pharmaceutical and pharmaceutical interventions for the control of epidemics that has been widely used during the COVID-19 pandemic. In this paper, lessons learned from this and previous epidemics are used to highlight the challenges for future pandemic control. We consider the availability and use of data, as well as the need for correct parameterisation and calibration for different model frameworks. We discuss challenges that arise in describing and distinguishing between different interventions, within different modelling structures, and allowing both within and between host dynamics. We also highlight challenges in modelling the health economic and political aspects of interventions. Given the diversity of these challenges, a broad variety of interdisciplinary expertise is needed to address them, combining mathematical knowledge with biological and social insights, and including health economics and communication skills. Addressing these challenges for the future requires strong cross-disciplinary collaboration together with close communication between scientists and policy makers.
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Affiliation(s)
- Mirjam E Kretzschmar
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
| | - Ben Ashby
- Department of Mathematical Sciences, University of Bath, Bath BA2 7AY, UK
| | - Elizabeth Fearon
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, UK
| | - Christopher E Overton
- Department of Mathematics, University of Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK; Clinical Data Science Unit, Manchester University NHS Foundation Trust, UK
| | - Jasmina Panovska-Griffiths
- The Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK; The Queen's College, University of Oxford, Oxford, UK
| | - Lorenzo Pellis
- Department of Mathematics, University of Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK; The Alan Turing Institute, London, UK
| | - Matthew Quaife
- TB Modelling Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, UK
| | - Ganna Rozhnova
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands; BioISI-Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal
| | - Francesca Scarabel
- Department of Mathematics, University of Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK; CDLab - Computational Dynamics Laboratory, Department of Mathematics, Computer Science and Physics, University of Udine, Italy
| | - Helena B Stage
- Department of Mathematics, University of Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK; University of Potsdam, Germany; Humboldt University of Berlin, Germany
| | - Ben Swallow
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK; Scottish Covid-19 Response Consortium, UK
| | - Robin N Thompson
- Joint UNIversities Pandemic and Epidemiological Research, UK; Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK; Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry CV4 7AL, UK
| | - Michael J Tildesley
- Joint UNIversities Pandemic and Epidemiological Research, UK; Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK; Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry CV4 7AL, UK
| | - Daniel Villela
- Program of Scientific Computing, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
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28
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Quantifying pupil-to-pupil SARS-CoV-2 transmission and the impact of lateral flow testing in English secondary schools. Nat Commun 2022; 13:1106. [PMID: 35232987 PMCID: PMC8888696 DOI: 10.1038/s41467-022-28731-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 02/04/2022] [Indexed: 12/24/2022] Open
Abstract
A range of measures have been implemented to control within-school SARS-CoV-2 transmission in England, including the self-isolation of close contacts and twice weekly mass testing of secondary school pupils using lateral flow device tests (LFTs). Despite reducing transmission, isolating close contacts can lead to high levels of absences, negatively impacting pupils. To quantify pupil-to-pupil SARS-CoV-2 transmission and the impact of implemented control measures, we fit a stochastic individual-based model of secondary school infection to both swab testing data and secondary school absences data from England, and then simulate outbreaks from 31st August 2020 until 23rd May 2021. We find that the pupil-to-pupil reproduction number, Rschool, has remained below 1 on average across the study period, and that twice weekly mass testing using LFTs has helped to control pupil-to-pupil transmission. We also explore the potential benefits of alternative containment strategies, finding that a strategy of repeat testing of close contacts rather than isolation, alongside mass testing, substantially reduces absences with only a marginal increase in pupil-to-pupil transmission. Twice weekly mass testing using lateral flow tests has helped to control pupil-to-pupil transmission in English secondary schools. Here, the authors show that repeat testing of contacts alongside mass testing could greatly reduce absences with only a marginal increase in transmission, compared to isolating contacts.
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29
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Gandolfi A, Aspri A, Beretta E, Jamshad K, Jiang M. A new threshold reveals the uncertainty about the effect of school opening on diffusion of Covid-19. Sci Rep 2022; 12:3012. [PMID: 35194065 PMCID: PMC8863853 DOI: 10.1038/s41598-022-06540-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 02/02/2022] [Indexed: 12/11/2022] Open
Abstract
Studies on the effects of school openings or closures during the Covid-19 pandemic seem to reach contrasting conclusions even in similar contexts. We aim at clarifying this controversy. A mathematical analysis of compartmental models with subpopulations has been conducted, starting from the SIR model, and progressively adding features modeling outbreaks or upsurge of variants, lockdowns, and vaccinations. We find that in all cases, the in-school transmission rates only affect the overall course of the pandemic above a certain context dependent threshold. We provide rigorous proofs and computations of the thresdhold through linearization. We then confirm our theoretical findings through simulations and the review of data-driven studies that exhibit an often unnoticed phase transition. Specific implications are: awareness about the threshold could inform choice of data collection, analysis and release, such as in-school transmission rates, and clarify the reason for divergent conclusions in similar studies; schools may remain open at any stage of the Covid-19 pandemic, including variants upsurge, given suitable containment rules; these rules would be extremely strict and hardly sustainable if only adults are vaccinated, making a compelling argument for vaccinating children whenever possible.
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Affiliation(s)
- Alberto Gandolfi
- Division of Science, New York University Abu Dhabi, Abu Dhabi, 129188, UAE.
| | | | - Elena Beretta
- Division of Science, New York University Abu Dhabi, Abu Dhabi, 129188, UAE
| | - Khola Jamshad
- Division of Science, New York University Abu Dhabi, Abu Dhabi, 129188, UAE
| | - Muyan Jiang
- Division of Science, New York University Abu Dhabi, Abu Dhabi, 129188, UAE
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30
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Krishnaratne S, Littlecott H, Sell K, Burns J, Rabe JE, Stratil JM, Litwin T, Kreutz C, Coenen M, Geffert K, Boger AH, Movsisyan A, Kratzer S, Klinger C, Wabnitz K, Strahwald B, Verboom B, Rehfuess E, Biallas RL, Jung-Sievers C, Voss S, Pfadenhauer LM. Measures implemented in the school setting to contain the COVID-19 pandemic. Cochrane Database Syst Rev 2022; 1:CD015029. [PMID: 35037252 PMCID: PMC8762709 DOI: 10.1002/14651858.cd015029] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
BACKGROUND In response to the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the impact of coronavirus disease 2019 (COVID-19), governments have implemented a variety of measures to control the spread of the virus and the associated disease. Among these, have been measures to control the pandemic in primary and secondary school settings. OBJECTIVES To assess the effectiveness of measures implemented in the school setting to safely reopen schools, or keep schools open, or both, during the COVID-19 pandemic, with particular focus on the different types of measures implemented in school settings and the outcomes used to measure their impacts on transmission-related outcomes, healthcare utilisation outcomes, other health outcomes as well as societal, economic, and ecological outcomes. SEARCH METHODS: We searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, and the Educational Resources Information Center, as well as COVID-19-specific databases, including the Cochrane COVID-19 Study Register and the WHO COVID-19 Global literature on coronavirus disease (indexing preprints) on 9 December 2020. We conducted backward-citation searches with existing reviews. SELECTION CRITERIA We considered experimental (i.e. randomised controlled trials; RCTs), quasi-experimental, observational and modelling studies assessing the effects of measures implemented in the school setting to safely reopen schools, or keep schools open, or both, during the COVID-19 pandemic. Outcome categories were (i) transmission-related outcomes (e.g. number or proportion of cases); (ii) healthcare utilisation outcomes (e.g. number or proportion of hospitalisations); (iii) other health outcomes (e.g. physical, social and mental health); and (iv) societal, economic and ecological outcomes (e.g. costs, human resources and education). We considered studies that included any population at risk of becoming infected with SARS-CoV-2 and/or developing COVID-19 disease including students, teachers, other school staff, or members of the wider community. DATA COLLECTION AND ANALYSIS: Two review authors independently screened titles, abstracts and full texts. One review author extracted data and critically appraised each study. One additional review author validated the extracted data. To critically appraise included studies, we used the ROBINS-I tool for quasi-experimental and observational studies, the QUADAS-2 tool for observational screening studies, and a bespoke tool for modelling studies. We synthesised findings narratively. Three review authors made an initial assessment of the certainty of evidence with GRADE, and several review authors discussed and agreed on the ratings. MAIN RESULTS We included 38 unique studies in the analysis, comprising 33 modelling studies, three observational studies, one quasi-experimental and one experimental study with modelling components. Measures fell into four broad categories: (i) measures reducing the opportunity for contacts; (ii) measures making contacts safer; (iii) surveillance and response measures; and (iv) multicomponent measures. As comparators, we encountered the operation of schools with no measures in place, less intense measures in place, single versus multicomponent measures in place, or closure of schools. Across all intervention categories and all study designs, very low- to low-certainty evidence ratings limit our confidence in the findings. Concerns with the quality of modelling studies related to potentially inappropriate assumptions about the model structure and input parameters, and an inadequate assessment of model uncertainty. Concerns with risk of bias in observational studies related to deviations from intended interventions or missing data. Across all categories, few studies reported on implementation or described how measures were implemented. Where we describe effects as 'positive', the direction of the point estimate of the effect favours the intervention(s); 'negative' effects do not favour the intervention. We found 23 modelling studies assessing measures reducing the opportunity for contacts (i.e. alternating attendance, reduced class size). Most of these studies assessed transmission and healthcare utilisation outcomes, and all of these studies showed a reduction in transmission (e.g. a reduction in the number or proportion of cases, reproduction number) and healthcare utilisation (i.e. fewer hospitalisations) and mixed or negative effects on societal, economic and ecological outcomes (i.e. fewer number of days spent in school). We identified 11 modelling studies and two observational studies assessing measures making contacts safer (i.e. mask wearing, cleaning, handwashing, ventilation). Five studies assessed the impact of combined measures to make contacts safer. They assessed transmission-related, healthcare utilisation, other health, and societal, economic and ecological outcomes. Most of these studies showed a reduction in transmission, and a reduction in hospitalisations; however, studies showed mixed or negative effects on societal, economic and ecological outcomes (i.e. fewer number of days spent in school). We identified 13 modelling studies and one observational study assessing surveillance and response measures, including testing and isolation, and symptomatic screening and isolation. Twelve studies focused on mass testing and isolation measures, while two looked specifically at symptom-based screening and isolation. Outcomes included transmission, healthcare utilisation, other health, and societal, economic and ecological outcomes. Most of these studies showed effects in favour of the intervention in terms of reductions in transmission and hospitalisations, however some showed mixed or negative effects on societal, economic and ecological outcomes (e.g. fewer number of days spent in school). We found three studies that reported outcomes relating to multicomponent measures, where it was not possible to disaggregate the effects of each individual intervention, including one modelling, one observational and one quasi-experimental study. These studies employed interventions, such as physical distancing, modification of school activities, testing, and exemption of high-risk students, using measures such as hand hygiene and mask wearing. Most of these studies showed a reduction in transmission, however some showed mixed or no effects. As the majority of studies included in the review were modelling studies, there was a lack of empirical, real-world data, which meant that there were very little data on the actual implementation of interventions. AUTHORS' CONCLUSIONS Our review suggests that a broad range of measures implemented in the school setting can have positive impacts on the transmission of SARS-CoV-2, and on healthcare utilisation outcomes related to COVID-19. The certainty of the evidence for most intervention-outcome combinations is very low, and the true effects of these measures are likely to be substantially different from those reported here. Measures implemented in the school setting may limit the number or proportion of cases and deaths, and may delay the progression of the pandemic. However, they may also lead to negative unintended consequences, such as fewer days spent in school (beyond those intended by the intervention). Further, most studies assessed the effects of a combination of interventions, which could not be disentangled to estimate their specific effects. Studies assessing measures to reduce contacts and to make contacts safer consistently predicted positive effects on transmission and healthcare utilisation, but may reduce the number of days students spent at school. Studies assessing surveillance and response measures predicted reductions in hospitalisations and school days missed due to infection or quarantine, however, there was mixed evidence on resources needed for surveillance. Evidence on multicomponent measures was mixed, mostly due to comparators. The magnitude of effects depends on multiple factors. New studies published since the original search date might heavily influence the overall conclusions and interpretation of findings for this review.
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Affiliation(s)
- Shari Krishnaratne
- Institute for Medical Information Processing, Biometry and Epidemiology - IBE, Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
- Department of Disease Control, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Hannah Littlecott
- Institute for Medical Information Processing, Biometry and Epidemiology - IBE, Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
- DECIPHer, School of Social Sciences, Cardiff University, Cardiff, UK
| | - Kerstin Sell
- Institute for Medical Information Processing, Biometry and Epidemiology - IBE, Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Jacob Burns
- Institute for Medical Information Processing, Biometry and Epidemiology - IBE, Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Julia E Rabe
- Institute for Medical Information Processing, Biometry and Epidemiology - IBE, Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Jan M Stratil
- Institute for Medical Information Processing, Biometry and Epidemiology - IBE, Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Tim Litwin
- Institute of Medical Biometry and Statistics (IMBI), Freiburg Center for Data Analytics and Modeling (FDM), Faculty of Medicine and Medical Center, Albert-Ludwig-University, Freiburg, Germany
| | - Clemens Kreutz
- Institute of Medical Biometry and Statistics (IMBI), Freiburg Center for Data Analytics and Modeling (FDM), Faculty of Medicine and Medical Center, Albert-Ludwig-University, Freiburg, Germany
| | - Michaela Coenen
- Institute for Medical Information Processing, Biometry and Epidemiology - IBE, Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Karin Geffert
- Institute for Medical Information Processing, Biometry and Epidemiology - IBE, Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Anna Helen Boger
- Institute of Medical Biometry and Statistics (IMBI), Freiburg Center for Data Analytics and Modeling (FDM), Faculty of Medicine and Medical Center, Albert-Ludwig-University, Freiburg, Germany
| | - Ani Movsisyan
- Institute for Medical Information Processing, Biometry and Epidemiology - IBE, Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Suzie Kratzer
- Institute for Medical Information Processing, Biometry and Epidemiology - IBE, Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Carmen Klinger
- Institute for Medical Information Processing, Biometry and Epidemiology - IBE, Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Katharina Wabnitz
- Institute for Medical Information Processing, Biometry and Epidemiology - IBE, Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Brigitte Strahwald
- Institute for Medical Information Processing, Biometry and Epidemiology - IBE, Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Ben Verboom
- Institute for Medical Information Processing, Biometry and Epidemiology - IBE, Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Eva Rehfuess
- Institute for Medical Information Processing, Biometry and Epidemiology - IBE, Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Renke L Biallas
- Institute for Medical Information Processing, Biometry and Epidemiology - IBE, Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Caroline Jung-Sievers
- Institute for Medical Information Processing, Biometry and Epidemiology - IBE, Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Stephan Voss
- Institute for Medical Information Processing, Biometry and Epidemiology - IBE, Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Lisa M Pfadenhauer
- Institute for Medical Information Processing, Biometry and Epidemiology - IBE, Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
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Liu QH, Zhang J, Peng C, Litvinova M, Huang S, Poletti P, Trentini F, Guzzetta G, Marziano V, Zhou T, Viboud C, Bento AI, Lv J, Vespignani A, Merler S, Yu H, Ajelli M. Model-based evaluation of alternative reactive class closure strategies against COVID-19. Nat Commun 2022; 13:322. [PMID: 35031600 PMCID: PMC8760266 DOI: 10.1038/s41467-021-27939-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 12/17/2021] [Indexed: 01/10/2023] Open
Abstract
There are contrasting results concerning the effect of reactive school closure on SARS-CoV-2 transmission. To shed light on this controversy, we developed a data-driven computational model of SARS-CoV-2 transmission. We found that by reactively closing classes based on syndromic surveillance, SARS-CoV-2 infections are reduced by no more than 17.3% (95%CI: 8.0-26.8%), due to the low probability of timely identification of infections in the young population. We thus investigated an alternative triggering mechanism based on repeated screening of students using antigen tests. Depending on the contribution of schools to transmission, this strategy can greatly reduce COVID-19 burden even when school contribution to transmission and immunity in the population is low. Moving forward, the adoption of antigen-based screenings in schools could be instrumental to limit COVID-19 burden while vaccines continue to be rolled out.
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Affiliation(s)
- Quan-Hui Liu
- College of Computer Science, Sichuan University, Chengdu, China
| | - Juanjuan Zhang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China
- Department of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China
| | - Cheng Peng
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Maria Litvinova
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Shudong Huang
- College of Computer Science, Sichuan University, Chengdu, China
| | - Piero Poletti
- Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy
| | - Filippo Trentini
- Dondena Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milan, Italy
| | - Giorgio Guzzetta
- Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy
| | | | - Tao Zhou
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China
| | - Cecile Viboud
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Ana I Bento
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Jiancheng Lv
- College of Computer Science, Sichuan University, Chengdu, China
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA
| | - Stefano Merler
- Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy
| | - Hongjie Yu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China.
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China.
- Department of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
| | - Marco Ajelli
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.
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POZZOBON ALLANPB, PETRY ANAC, ZILBERBERG CARLA, BARROS CINTIAMDE, NEPOMUCENO-SILVA JOSÉL, FEITOSA NATÁLIAM, GOMES NETO LUPISR, RODRIGUES BRUNOC, BRINDEIRO RODRIGOM, NOCCHI KEITYJAQUELINEC, MURY FLAVIAB, SOUZA-MENEZES JACKSONDE, SILVA MANUELALDA, MEDEIROS MARCIOJOSÉDE, GESTINARI RAQUELS, ALVARENGA ALESSANDRASDE, SILVA CARINAA, SANTOS DANIELEGDOS, SILVESTRE DIEGOHENRIQUE, SOUSA GRAZIELEFDE, ALMEIDA JANIMAYRIFDE, SILVA JHENIFERNDA, BRANDÃO LAYZAM, DRUMMOND LEANDROO, CARPES RAPHAELM, SANTOS RENATACDOS, PORTAL TAYNANM, TANURI AMILCAR, NUNES-DA-FONSECA RODRIGO. Schools reopening and the COVID-19 pandemic: a case study from Macaé, Rio de Janeiro, Brazil. AN ACAD BRAS CIENC 2022; 94:e20211361. [DOI: 10.1590/0001-3765202220211361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 01/16/2022] [Indexed: 12/23/2022] Open
Affiliation(s)
| | - ANA C. PETRY
- Universidade Federal do Rio de Janeiro (UFRJ), Brazil
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Nian F, Shi Y, Cao J. COVID-19 Propagation Model Based on Economic Development and Interventions. WIRELESS PERSONAL COMMUNICATIONS 2022; 122:2355-2365. [PMID: 34421225 PMCID: PMC8371428 DOI: 10.1007/s11277-021-08998-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/08/2021] [Indexed: 05/03/2023]
Abstract
In order to understand the influencing factors affecting the COVID-19 propagation, and analyze the development trend of the epidemic situation in the world, COVID-19 propagation model to simulate the COVID-19 propagation in the population is proposed in this paper. First of all, this paper analyzes the economic factors and interventions affecting the COVID-19 propagation in various different countries. Then, the touch number for COVID-19 High-risk Population Dynamic Network in this paper was redefined, and it predicts and analyzes the development trend of the epidemic situation in different countries. The simulation data and the published confirmed data by the world health organization could fit well, which also verified the reliability of the model. Finally, this paper also analyzes the impact of public awareness of prevention on the control of the epidemic. The analysis shows that increasing the awareness of prevention, timely and early adoption of protective measures such as wearing masks, and reducing travel can greatly reduce the risk of infection and the outbreak scale.
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Affiliation(s)
- Fuzhong Nian
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050 China
| | - Yayong Shi
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050 China
| | - Jun Cao
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050 China
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Sombetzki M, Lücker P, Ehmke M, Bock S, Littmann M, Reisinger EC, Hoffmann W, Kästner A. Impact of Changes in Infection Control Measures on the Dynamics of COVID-19 Infections in Schools and Pre-schools. Front Public Health 2021; 9:780039. [PMID: 34988054 PMCID: PMC8720754 DOI: 10.3389/fpubh.2021.780039] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 11/23/2021] [Indexed: 11/25/2022] Open
Abstract
Introduction: With the increased emergence of SARS-CoV-2 variants, the impact on schools and preschools remains a matter of debate. To ensure that schools and preschools are kept open safely, the identification of factors influencing the extent of outbreaks is of importance. Aim: To monitor dynamics of COVID-19 infections in schools and preschools and identify factors influencing the extent of outbreaks. Methods: In this prospective observational study we analyzed routine surveillance data of Mecklenburg-Western Pomerania, Germany, from calendar week (CW) 32, 2020 to CW19, 2021 regarding SARS-CoV-2 infection events in schools and preschools considering changes in infection control measures over time. A multivariate linear regression model was fitted to evaluate factors influencing the number of students, teachers and staff tested positive following index cases in schools and preschools. Due to an existing multicollinearity in the common multivariate regression model between the variables "face mask obligation for children" and "face mask obligation for adults", two further separate regression models were set up (Multivariate Model Adults and Multivariate Model Children). Results: We observed a significant increase in secondary cases in preschools in the first quarter of 2021 (CW8 to CW15, 2021), and simultaneously a decrease in secondary cases in schools. In multivariate regression analysis, the strongest predictor of the extent of the outbreaks was the teacher/ caregiver mask obligation (B = -1.9; 95% CI: -2.9 to -1.0; p < 0.001). Furthermore, adult index cases (adult only or child+adult combinations) increased the likelihood of secondary cases (B = 1.3; 95% CI: 0.9 to 1.8; p < 0.001). The face mask obligation for children also showed a significant reduction in the number of secondary cases (B = -0.6; 95% CI: -0.9 to -0.2; p = 0.004. Conclusion: The present study indicates that outbreak events at schools and preschools are effectively contained by an obligation for adults and children to wear face masks.
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Affiliation(s)
- Martina Sombetzki
- Department of Tropical Medicine and Infectious Diseases, University Medical Center Rostock, Rostock, Germany
| | - Petra Lücker
- Department for Epidemiology of Health Care and Community Health, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Manja Ehmke
- Department of Tropical Medicine and Infectious Diseases, University Medical Center Rostock, Rostock, Germany
| | - Sabrina Bock
- Landesamt für Gesundheit und Soziales Mecklenburg-Vorpommern State Office for Health and Social Affairs, Rostock, Germany
| | - Martina Littmann
- Landesamt für Gesundheit und Soziales Mecklenburg-Vorpommern State Office for Health and Social Affairs, Rostock, Germany
| | - Emil C. Reisinger
- Department of Tropical Medicine and Infectious Diseases, University Medical Center Rostock, Rostock, Germany
| | - Wolfgang Hoffmann
- Department for Epidemiology of Health Care and Community Health, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Anika Kästner
- Department for Epidemiology of Health Care and Community Health, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
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Ganegoda NC, Wijaya KP, Páez Chávez J, Aldila D, Erandi KKWH, Amadi M. Reassessment of contact restrictions and testing campaigns against COVID-19 via spatio-temporal modeling. NONLINEAR DYNAMICS 2021; 107:3085-3109. [PMID: 34955605 PMCID: PMC8686823 DOI: 10.1007/s11071-021-07111-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 11/28/2021] [Indexed: 06/14/2023]
Abstract
Since the earliest outbreak of COVID-19, the disease continues to obstruct life normalcy in many parts of the world. The present work proposes a mathematical framework to improve non-pharmaceutical interventions during the new normal before vaccination settles herd immunity. The considered approach is built from the viewpoint of decision makers in developing countries where resources to tackle the disease from both a medical and an economic perspective are scarce. Spatial auto-correlation analysis via global Moran's index and Moran's scatter is presented to help modulate decisions on hierarchical-based priority for healthcare capacity and interventions (including possible vaccination), finding a route for the corresponding deployment as well as landmarks for appropriate border controls. These clustering tools are applied to sample data from Sri Lanka to classify the 26 Regional Director of Health Services (RDHS) divisions into four clusters by introducing convenient classification criteria. A metapopulation model is then used to evaluate the intra- and inter-cluster contact restrictions as well as testing campaigns under the absence of confounding factors. Furthermore, we investigate the role of the basic reproduction number to determine the long-term trend of the regressing solution around disease-free and endemic equilibria. This includes an analytical bifurcation study around the basic reproduction number using Brouwer Degree Theory and asymptotic expansions as well as related numerical investigations based on path-following techniques. We also introduce the notion of average policy effect to assess the effectivity of contact restrictions and testing campaigns based on the proposed model's transient behavior within a fixed time window of interest.
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Affiliation(s)
| | | | - Joseph Páez Chávez
- Center for Applied Dynamical Systems and Computational Methods (CADSCOM), Faculty of Natural Sciences and Mathematics, Escuela Superior Politécnica del Litoral, P.O. Box 09-01-5863, Guayaquil, Ecuador
- Center for Dynamics, Department of Mathematics, TU Dresden, D–01062 Dresden, Germany
| | - Dipo Aldila
- Department of Mathematics, University of Indonesia, Depok, 16424 Indonesia
| | | | - Miracle Amadi
- Department of Mathematics and Physics, Lappeenranta University of Technology, FI–53851 Lappeenranta, Finland
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Dorp CHV, Goldberg EE, Hengartner N, Ke R, Romero-Severson EO. Estimating the strength of selection for new SARS-CoV-2 variants. Nat Commun 2021; 12:7239. [PMID: 34907182 PMCID: PMC8671537 DOI: 10.1038/s41467-021-27369-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 11/10/2021] [Indexed: 01/15/2023] Open
Abstract
Controlling the SARS-CoV-2 pandemic becomes increasingly challenging as the virus adapts to human hosts through the continual emergence of more transmissible variants. Simply observing that a variant is increasing in frequency is relatively straightforward, but more sophisticated methodology is needed to determine whether a new variant is a global threat and the magnitude of its selective advantage. We present two models for quantifying the strength of selection for new and emerging variants of SARS-CoV-2 relative to the background of contemporaneous variants. These methods range from a detailed model of dynamics within one country to a broad analysis across all countries, and they include alternative explanations such as migration and drift. We find evidence for strong selection favoring the D614G spike mutation and B.1.1.7 (Alpha), weaker selection favoring B.1.351 (Beta), and no advantage of R.1 after it spreads beyond Japan. Cutting back data to earlier time horizons reveals that uncertainty is large very soon after emergence, but that estimates of selection stabilize after several weeks. Our results also show substantial heterogeneity among countries, demonstrating the need for a truly global perspective on the molecular epidemiology of SARS-CoV-2.
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Affiliation(s)
- Christiaan H van Dorp
- Theoretical Biology and Biophysics (T-6), Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Emma E Goldberg
- Theoretical Biology and Biophysics (T-6), Los Alamos National Laboratory, Los Alamos, NM, USA
- New Mexico Consortium, Los Alamos, NM, USA
| | - Nick Hengartner
- Theoretical Biology and Biophysics (T-6), Los Alamos National Laboratory, Los Alamos, NM, USA
- New Mexico Consortium, Los Alamos, NM, USA
| | - Ruian Ke
- Theoretical Biology and Biophysics (T-6), Los Alamos National Laboratory, Los Alamos, NM, USA
- New Mexico Consortium, Los Alamos, NM, USA
| | - Ethan O Romero-Severson
- Theoretical Biology and Biophysics (T-6), Los Alamos National Laboratory, Los Alamos, NM, USA.
- New Mexico Consortium, Los Alamos, NM, USA.
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Campos M, Sempere JM, Galán JC, Moya A, Llorens C, de-Los-Angeles C, Baquero-Artigao F, Cantón R, Baquero F. Simulating the impact of non-pharmaceutical interventions limiting transmission in COVID-19 epidemics using a membrane computing model. ACTA ACUST UNITED AC 2021; 2:uqab011. [PMID: 34642663 PMCID: PMC8499911 DOI: 10.1093/femsml/uqab011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 09/03/2021] [Indexed: 01/08/2023]
Abstract
Epidemics caused by microbial organisms are part of the natural phenomena of increasing biological complexity. The heterogeneity and constant variability of hosts, in terms of age, immunological status, family structure, lifestyle, work activities, social and leisure habits, daily division of time and other demographic characteristics make it extremely difficult to predict the evolution of epidemics. Such prediction is, however, critical for implementing intervention measures in due time and with appropriate intensity. General conclusions should be precluded, given that local parameters dominate the flow of local epidemics. Membrane computing models allows us to reproduce the objects (viruses and hosts) and their interactions (stochastic but also with defined probabilities) with an unprecedented level of detail. Our LOIMOS model helps reproduce the demographics and social aspects of a hypothetical town of 10 320 inhabitants in an average European country where COVID-19 is imported from the outside. The above-mentioned characteristics of hosts and their lifestyle are minutely considered. For the data in the Hospital and the ICU we took advantage of the observations at the Nursery Intensive Care Unit of the Consortium University General Hospital, Valencia, Spain (included as author). The dynamics of the epidemics are reproduced and include the effects on viral transmission of innate and acquired immunity at various ages. The model predicts the consequences of delaying the adoption of non-pharmaceutical interventions (between 15 and 45 days after the first reported cases) and the effect of those interventions on infection and mortality rates (reducing transmission by 20, 50 and 80%) in immunological response groups. The lockdown for the elderly population as a single intervention appears to be effective. This modeling exercise exemplifies the application of membrane computing for designing appropriate multilateral interventions in epidemic situations.
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Affiliation(s)
- M Campos
- Department of Microbiology, Ramón y Cajal University Hospital, M-607, km 9,1 28034 Madrid, Spain
| | - J M Sempere
- Valencian Research Institute for Artificial Intelligence (VRAIN), Universitat Politècnica de Valencia, Camí de Vera s/n, 46022 Valencia, Spain
| | - J C Galán
- Department of Microbiology, Ramón y Cajal University Hospital, M-607, km 9,1 28034 Madrid, Spain
| | - A Moya
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública, M-607, km 9,1. 28034 Madrid, Spain
| | - C Llorens
- Biotechvana, Valencia, CEEI Building, Valencia Technological Park., C. agustín Escardino 9, 46980, Paterna, Valencia, Spain
| | - C de-Los-Angeles
- Nursery Unit, Intensive Care Unit and Pain Therapy, Consortium University General Hospital (CHGUV)., Av. Tres Cruces 2, 46014 Valencia, Spain
| | - F Baquero-Artigao
- Department of Infectious Diseases and Tropical Pediatrics, La Paz University Hospital., Av. Monforte de Lemos 2D, 28029 Madrid, Spain
| | - R Cantón
- Department of Microbiology, Ramón y Cajal University Hospital, M-607, km 9,1 28034 Madrid, Spain
| | - F Baquero
- Department of Microbiology, Ramón y Cajal University Hospital, M-607, km 9,1 28034 Madrid, Spain
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van Dorp CH, Goldberg EE, Hengartner N, Ke R, Romero-Severson EO. Estimating the strength of selection for new SARS-CoV-2 variants. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.03.29.21254233. [PMID: 33821289 PMCID: PMC8020992 DOI: 10.1101/2021.03.29.21254233] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Controlling the SARS-CoV-2 pandemic becomes increasingly challenging as the virus adapts to human hosts through the continual emergence of more transmissible variants. Simply observing that a variant is increasing in frequency is relatively straightforward, but more sophisticated methodology is needed to determine whether a new variant is a global threat and the magnitude of its selective advantage. We present three methods for quantifying the strength of selection for new and emerging variants of SARS-CoV-2 relative to the background of contemporaneous variants. These methods range from a detailed model of dynamics within one country to a broad analysis across all countries, and they include alternative explanations such as migration and drift. We find evidence for strong selection favoring the D614G spike mutation and B.1.1.7 (Alpha), weaker selection favoring B.1.351 (Beta), and no advantage of R.1 after it spreads beyond Japan. Cutting back data to earlier time horizons reveals large uncertainty very soon after emergence, but that estimates of selection stabilize after several weeks. Our results also show substantial heterogeneity among countries, demonstrating the need for a truly global perspective on the molecular epidemiology of SARS-CoV-2.
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Affiliation(s)
- Christiaan H. van Dorp
- Theoretical Biology and Biophysics (T-6), Los Alamos National Laboratory, Los Alamos NM, USA
| | - Emma E. Goldberg
- Theoretical Biology and Biophysics (T-6), Los Alamos National Laboratory, Los Alamos NM, USA
- New Mexico Consortium, Los Alamos NM, USA
| | - Nick Hengartner
- Theoretical Biology and Biophysics (T-6), Los Alamos National Laboratory, Los Alamos NM, USA
- New Mexico Consortium, Los Alamos NM, USA
| | - Ruian Ke
- Theoretical Biology and Biophysics (T-6), Los Alamos National Laboratory, Los Alamos NM, USA
- New Mexico Consortium, Los Alamos NM, USA
| | - Ethan O. Romero-Severson
- Theoretical Biology and Biophysics (T-6), Los Alamos National Laboratory, Los Alamos NM, USA
- New Mexico Consortium, Los Alamos NM, USA
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39
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Barcellini L, Forlanini F, Sangiorgio A, Gambacorta G, Alberti L, Meta A, Gaia P, Amendola A, Tanzi E, Massa V, Borghi E, Fabiano V, Zuccotti GV. Does school reopening affect SARS-CoV-2 seroprevalence among school-age children in Milan? PLoS One 2021; 16:e0257046. [PMID: 34473776 PMCID: PMC8412254 DOI: 10.1371/journal.pone.0257046] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 08/21/2021] [Indexed: 11/18/2022] Open
Abstract
The benefits of schools' closure, used as a containment strategy by many European countries, must be carefully considered against the adverse effects of child wellbeing. In this study, we assessed SARS-CoV-2 seroprevalence, which better estimates the real extent of the infection unraveling asymptomatic cases, among schoolchildren aged 3 to 18 in Milan, using dried blood spot, a safe and extremely viable methods for children, and then compared it between September 2020 and January 2021. Secondly, we evaluated the seroconversion rate and compared it between students attending schools in presence and those switched to distance-learning, using a logistic regression model, both as univariate and multivariate, adjusting for age and biological-sex. Among 1109 pupils, we found a seroprevalence of 2.8% in September before school reopening, while in January 2021, the seropositive rate was 12.5%, reflecting the general growth rate of infections during the second pandemic wave. The overall seroconversion rate was 10%, with no differences based on biological-sex and age groups; we observed no seroreversion. When considered age groups, the seroconversion rate was 10.5% (95%Confidence Interval, 2.9-24.8) among children attending preschools, 10.6% (95%Confidence Interval, 8.2-13.4) for primary schools, 9.9% (95%Confidence Interval, 6.8-13.8) for secondary schools, and 7.8% (95%Confidence Interval, 4-13.2) among high-school students. Interestingly, no differences in seroconversion rate were found between students who attended school compared to those who started remote learning in the first days of November. Furthermore, most patients (61%) reported that the contact occurred within the household. We reported a low seroconversion rate among school children in Milan, with no differences between those who attended from September 2020 to January 2021 compared to those who switched to remote learning in the first days of November. Our data suggest that schools do not amplify SARS-CoV-2 transmission, but rather reflect the level of the transmission in the community.
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Affiliation(s)
- Lucia Barcellini
- Department of Paediatrics, Children Hospital V. Buzzi, University of Milan, Milan, Italy
| | - Federica Forlanini
- Department of Paediatrics, Children Hospital V. Buzzi, University of Milan, Milan, Italy
| | - Arianna Sangiorgio
- Department of Paediatrics, Children Hospital V. Buzzi, University of Milan, Milan, Italy
| | - Greta Gambacorta
- Department of Paediatrics, Children Hospital V. Buzzi, University of Milan, Milan, Italy
| | - Luisella Alberti
- Newborn Screening Laboratory, Regional Reference Center for Metabolic Screening, Buzzi Children Hospital, Milan, Italy
| | - Andrea Meta
- Newborn Screening Laboratory, Regional Reference Center for Metabolic Screening, Buzzi Children Hospital, Milan, Italy
| | - Paola Gaia
- Newborn Screening Laboratory, Regional Reference Center for Metabolic Screening, Buzzi Children Hospital, Milan, Italy
| | - Antonella Amendola
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
| | - Elisabetta Tanzi
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
| | - Valentina Massa
- Department of Health Sciences, University of Milan, Milan, Italy
| | - Elisa Borghi
- Department of Health Sciences, University of Milan, Milan, Italy
| | - Valentina Fabiano
- Department of Paediatrics, Children Hospital V. Buzzi, University of Milan, Milan, Italy
| | - Gian Vincenzo Zuccotti
- Department of Paediatrics, Children Hospital V. Buzzi, University of Milan, Milan, Italy
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40
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Lakha F, King A, Swinkels K, Lee ACK. Are schools drivers of COVID-19 infections-an analysis of outbreaks in Colorado, USA in 2020. J Public Health (Oxf) 2021; 44:e26-e35. [PMID: 34179987 DOI: 10.1093/pubmed/fdab213] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 05/19/2021] [Accepted: 06/04/2021] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND The impact of school closures/reopening on transmission of SARS-CoV-2 in the wider community remains contested. METHODS Outbreak data from Colorado, USA (2020), alongside data on implemented public health measures were analyzed. RESULTS There were three waves (n = 3169 outbreaks; 61 650 individuals). The first was led by healthcare settings, the second leisure/entertainment and the third workplaces followed by other settings where the trajectory was equally distributed amongst essential workplaces, non-essential workplaces, schools and non-essential healthcare.Non-acute healthcare, essential and non-essential workplace experienced more outbreaks compared to education, entertainment, large-group-living and social gatherings.Schools experienced 11% of identified outbreaks, yet involved just 4% of total cases. Conversely, adult-education outbreaks (2%) had disproportionately more cases (9%). CONCLUSION Our findings suggest schools were not the key driver of the latest wave in infections. School re-opening coinciding with returning to work may have accounted for the parallel rise in outbreaks in those settings suggesting contact-points outside school being more likely to seed in-school outbreaks than contact points within school as the wave of outbreaks in all other settings occurred either prior to or simultaneously with the schools wave.School re-opening is a priority but requires mitigation measures to do so safely including staggering opening of different settings whilst maintaining low levels of community transmission.
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Affiliation(s)
- F Lakha
- Communicable Diseases Policy and Research Group, Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - A King
- Independent Researcher, London, UK
| | | | - A C K Lee
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK
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41
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Viana J, van Dorp CH, Nunes A, Gomes MC, van Boven M, Kretzschmar ME, Veldhoen M, Rozhnova G. Controlling the pandemic during the SARS-CoV-2 vaccination rollout. Nat Commun 2021; 12:3674. [PMID: 34135335 PMCID: PMC8209021 DOI: 10.1038/s41467-021-23938-8] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 05/24/2021] [Indexed: 02/07/2023] Open
Abstract
There is a consensus that mass vaccination against SARS-CoV-2 will ultimately end the COVID-19 pandemic. However, it is not clear when and which control measures can be relaxed during the rollout of vaccination programmes. We investigate relaxation scenarios using an age-structured transmission model that has been fitted to age-specific seroprevalence data, hospital admissions, and projected vaccination coverage for Portugal. Our analyses suggest that the pressing need to restart socioeconomic activities could lead to new pandemic waves, and that substantial control efforts prove necessary throughout 2021. Using knowledge on control measures introduced in 2020, we anticipate that relaxing measures completely or to the extent as in autumn 2020 could launch a wave starting in April 2021. Additional waves could be prevented altogether if measures are relaxed as in summer 2020 or in a step-wise manner throughout 2021. We discuss at which point the control of COVID-19 would be achieved for each scenario.
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Affiliation(s)
- João Viana
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal
| | - Christiaan H van Dorp
- Theoretical Biology and Biophysics (T-6), Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Ana Nunes
- Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal
- BioISI-Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal
| | - Manuel C Gomes
- Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal
| | - Michiel van Boven
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Mirjam E Kretzschmar
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Marc Veldhoen
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
| | - Ganna Rozhnova
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
- BioISI-Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal.
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Liu QH, Zhang J, Peng C, Litvinova M, Huang S, Poletti P, Trentini F, Guzzetta G, Marziano V, Zhou T, Viboud C, Bento AI, Lv J, Vespignani A, Merler S, Yu H, Ajelli M. Model-based evaluation of alternative reactive class closure strategies against COVID-19. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.04.18.21255683. [PMID: 33907769 PMCID: PMC8077629 DOI: 10.1101/2021.04.18.21255683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
There are contrasting results concerning the effect of reactive school closure on SARS-CoV-2 transmission. To shed light on this controversy, here we develop a data-driven computational model of SARS-CoV-2 transmission to investigate mechanistically the effect on COVID-19 outbreaks of school closure strategies based on syndromic surveillance and antigen screening of students. We found that by reactively closing classes based on syndromic surveillance, SARS-CoV-2 infections are reduced by no more than 13.1% (95%CI: 8.6%-20.2 %), due to the low probability of timely symptomatic case identification among the young population. We thus investigated an alternative triggering mechanism based on repeated screening of students using antigen tests. Should population-level social distancing measures unrelated to schools enable maintaining the reproduction number ( R ) at 1.3 or lower, an antigen-based screening strategy is estimated to fully prevent COVID-19 outbreaks in the general population. Depending on the contribution of schools to transmission, this strategy can either prevent COVID-19 outbreaks for R up to 1.9 or to at least greatly reduce outbreak size in very conservative scenarios about school contribution to transmission. Moving forward, the adoption of antigen-based screenings in schools could be instrumental to limit COVID-19 burden while vaccines continue to roll out through 2021, especially in light of possible continued emergence of SARS-CoV-2 variants.
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Affiliation(s)
- Quan-Hui Liu
- College of Computer Science, Sichuan University, Chengdu, China
| | - Juanjuan Zhang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Cheng Peng
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Maria Litvinova
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Shudong Huang
- College of Computer Science, Sichuan University, Chengdu, China
| | - Piero Poletti
- Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy
| | - Filippo Trentini
- Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy
| | - Giorgio Guzzetta
- Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy
| | | | - Tao Zhou
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China
- Tianfu Complexity Science Research Center, Chengdu, China
| | - Cecile Viboud
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Ana I. Bento
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Jiancheng Lv
- College of Computer Science, Sichuan University, Chengdu, China
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA
- ISI Foundation, Turin, Italy
| | - Stefano Merler
- Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy
| | - Hongjie Yu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China
- Department of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China
| | - Marco Ajelli
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA
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43
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Phillips B, Browne DT, Anand M, Bauch CT. Model-based projections for COVID-19 outbreak size and student-days lost to closure in Ontario childcare centres and primary schools. Sci Rep 2021; 11:6402. [PMID: 33737555 PMCID: PMC7973423 DOI: 10.1038/s41598-021-85302-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Accepted: 02/26/2021] [Indexed: 12/24/2022] Open
Abstract
There is a pressing need for evidence-based scrutiny of plans to re-open childcare centres during the COVID-19 pandemic. Here we developed an agent-based model of SARS-CoV-2 transmission within a childcare centre and households. Scenarios varied the student-to-educator ratio (15:2, 8:2, 7:3), family clustering (siblings together versus random assignment) and time spent in class. We also evaluated a primary school setting (with student-educator ratios 30:1, 15:1 and 8:1), including cohorts that alternate weekly. In the childcare centre setting, grouping siblings significantly reduced outbreak size and student-days lost. We identify an intensification cascade specific to classroom outbreaks of respiratory viruses with presymptomatic infection. In both childcare and primary school settings, each doubling of class size from 8 to 15 to 30 more than doubled the outbreak size and student-days lost (increases by factors of 2-5, depending on the scenario. Proposals for childcare and primary school reopening could be enhanced for safety by switching to smaller class sizes and grouping siblings.
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Affiliation(s)
- Brendon Phillips
- Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada
- Department of Psychology, University of Waterloo, Waterloo, ON, Canada
| | - Dillon T Browne
- Department of Psychology, University of Waterloo, Waterloo, ON, Canada
| | - Madhur Anand
- School of Environmental Sciences, University of Guelph, Guelph, ON, Canada
| | - Chris T Bauch
- Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada.
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44
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Rozhnova G, van Dorp CH, Bruijning-Verhagen P, Bootsma MCJ, van de Wijgert JHHM, Bonten MJM, Kretzschmar ME. Model-based evaluation of school- and non-school-related measures to control the COVID-19 pandemic. Nat Commun 2021; 12:1614. [PMID: 33712603 PMCID: PMC7955041 DOI: 10.1038/s41467-021-21899-6] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 02/17/2021] [Indexed: 12/16/2022] Open
Abstract
The role of school-based contacts in the epidemiology of SARS-CoV-2 is incompletely understood. We use an age-structured transmission model fitted to age-specific seroprevalence and hospital admission data to assess the effects of school-based measures at different time points during the COVID-19 pandemic in the Netherlands. Our analyses suggest that the impact of measures reducing school-based contacts depends on the remaining opportunities to reduce non-school-based contacts. If opportunities to reduce the effective reproduction number (Re) with non-school-based measures are exhausted or undesired and Re is still close to 1, the additional benefit of school-based measures may be considerable, particularly among older school children. As two examples, we demonstrate that keeping schools closed after the summer holidays in 2020, in the absence of other measures, would not have prevented the second pandemic wave in autumn 2020 but closing schools in November 2020 could have reduced Re below 1, with unchanged non-school-based contacts.
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Affiliation(s)
- Ganna Rozhnova
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
- BioISI-Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal.
| | - Christiaan H van Dorp
- Theoretical Biology and Biophysics (T-6), Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Patricia Bruijning-Verhagen
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Martin C J Bootsma
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Mathematical Institute, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Janneke H H M van de Wijgert
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- The Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
| | - Marc J M Bonten
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of Medical Microbiology, University Medical Center Utrecht, Utrecht University, The Netherlands
| | - Mirjam E Kretzschmar
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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