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Schnyder SK, Molina JJ, Yamamoto R, Turner MS. Understanding Nash epidemics. Proc Natl Acad Sci U S A 2025; 122:e2409362122. [PMID: 40014574 PMCID: PMC11892628 DOI: 10.1073/pnas.2409362122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Accepted: 01/17/2025] [Indexed: 03/01/2025] Open
Abstract
Faced with a dangerous epidemic humans will spontaneously social distance to reduce their risk of infection at a socioeconomic cost. Compartmentalized epidemic models have been extended to include this endogenous decision making: Individuals choose their behavior to optimize a utility function, self-consistently giving rise to population behavior. Here, we study the properties of the resulting Nash equilibria, in which no member of the population can gain an advantage by unilaterally adopting different behavior. We leverage an analytic solution that yields fully time-dependent rational population behavior to obtain, 1) a simple relationship between rational social distancing behavior and the current number of infections; 2) scaling results for how the infection peak and number of total cases depend on the cost of contracting the disease; 3) characteristic infection costs that divide regimes of strong and weak behavioral response; 4) a closed form expression for the value of the utility. We discuss how these analytic results provide a deep and intuitive understanding of the disease dynamics, useful for both individuals and policymakers. In particular, the relationship between social distancing and infections represents a heuristic that could be communicated to the population to encourage, or "bootstrap," rational behavior.
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Affiliation(s)
- Simon K. Schnyder
- Institute of Industrial Science, The University of Tokyo, Tokyo153-8505, Japan
| | - John J. Molina
- Department of Chemical Engineering, Kyoto University, Kyoto615-8510, Japan
| | - Ryoichi Yamamoto
- Department of Chemical Engineering, Kyoto University, Kyoto615-8510, Japan
| | - Matthew S. Turner
- Department of Physics, University of Warwick, CoventryCV4 7AL, United Kingdom
- Institute for Global Pandemic Planning, University of Warwick, CoventryCV4 7AL, United Kingdom
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2
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Rysava K, Thompson RN. Projections of health outcomes in the USA in 2050. Lancet 2024; 404:2246-2247. [PMID: 39645373 DOI: 10.1016/s0140-6736(24)02428-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2024] [Accepted: 10/31/2024] [Indexed: 12/09/2024]
Affiliation(s)
- Kristyna Rysava
- Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK
| | - Robin N Thompson
- Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK.
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3
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Goenka A, Liu L. Economic Epidemiology: A Framework to Study Interactions of Epidemics and the Economy. APPLIED HEALTH ECONOMICS AND HEALTH POLICY 2024; 22:767-769. [PMID: 39141026 DOI: 10.1007/s40258-024-00907-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/29/2024] [Indexed: 08/15/2024]
Affiliation(s)
- Aditya Goenka
- Department of Economics, University of Birmingham, Birmingham, UK.
| | - Lin Liu
- Management School, University of Liverpool, Liverpool, UK
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4
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d'Onofrio A, Iannelli M, Marinoschi G, Manfredi P. Multiple pandemic waves vs multi-period/multi-phasic epidemics: Global shape of the COVID-19 pandemic. J Theor Biol 2024; 593:111881. [PMID: 38972568 DOI: 10.1016/j.jtbi.2024.111881] [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: 03/14/2023] [Revised: 09/29/2023] [Accepted: 06/14/2024] [Indexed: 07/09/2024]
Abstract
The overall course of the COVID-19 pandemic in Western countries has been characterized by complex sequences of phases. In the period before the arrival of vaccines, these phases were mainly due to the alternation between the strengthening/lifting of social distancing measures, with the aim to balance the protection of health and that of the society as a whole. After the arrival of vaccines, this multi-phasic character was further emphasized by the complicated deployment of vaccination campaigns and the onset of virus' variants. To cope with this multi-phasic character, we propose a theoretical approach to the modeling of overall pandemic courses, that we term multi-period/multi-phasic, based on a specific definition of phase. This allows a unified and parsimonious representation of complex epidemic courses even when vaccination and virus' variants are considered, through sequences of weak ergodic renewal equations that become fully ergodic when appropriate conditions are met. Specific hypotheses on epidemiological and intervention parameters allow reduction to simple models. The framework suggest a simple, theory driven, approach to data explanation that allows an accurate reproduction of the overall course of the COVID-19 epidemic in Italy since its beginning (February 2020) up to omicron onset, confirming the validity of the concept.
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Affiliation(s)
- Alberto d'Onofrio
- Dipartimento di Matematica e Geoscienze, Universitá di Trieste, Via Alfonso Valerio 12, Edificio H2bis, 34127 Trieste, Italy.
| | - Mimmo Iannelli
- Mathematics Department, University of Trento, Via Sommarive 14, 38123 Trento, Italy.
| | - Gabriela Marinoschi
- Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics, Romanian Academy, Bucharest, Romania.
| | - Piero Manfredi
- Department of Economics and Management, University of Pisa, Via Ridolfi 10, 56124 Pisa, Italy.
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5
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Keeling MJ, Dyson L. A retrospective assessment of forecasting the peak of the SARS-CoV-2 Omicron BA.1 wave in England. PLoS Comput Biol 2024; 20:e1012452. [PMID: 39312582 PMCID: PMC11449292 DOI: 10.1371/journal.pcbi.1012452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 10/03/2024] [Accepted: 09/03/2024] [Indexed: 09/25/2024] Open
Abstract
We discuss the invasion of the Omicron BA.1 variant into England as a paradigm for real-time model fitting and projection. Here we use a mixture of simple SIR-type models, analysis of the early data and a more complex age-structure model fit to the outbreak to understand the dynamics. In particular, we highlight that early data shows that the invading Omicron variant had a substantial growth advantage over the resident Delta variant. However, early data does not allow us to reliably infer other key epidemiological parameters-such as generation time and severity-which influence the expected peak hospital numbers. With more complete epidemic data from January 2022 are we able to capture the true scale of the epidemic in terms of both infections and hospital admissions, driven by different infection characteristics of Omicron compared to Delta and a substantial shift in estimated precautionary behaviour during December. This work highlights the challenges of real time forecasting, in a rapidly changing environment with limited information on the variant's epidemiological characteristics.
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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, United Kingdom
- Joint UNIversities Pandemic and Epidemiological Research, https://maths.org/juniper/
| | - Louise Dyson
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, CV4 7AL, United Kingdom
- Joint UNIversities Pandemic and Epidemiological Research, https://maths.org/juniper/
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6
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Lees JA, Russell TW, Shaw LP, Hellewell J. Recent approaches in computational modelling for controlling pathogen threats. Life Sci Alliance 2024; 7:e202402666. [PMID: 38906676 PMCID: PMC11192964 DOI: 10.26508/lsa.202402666] [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: 02/19/2024] [Revised: 06/11/2024] [Accepted: 06/13/2024] [Indexed: 06/23/2024] Open
Abstract
In this review, we assess the status of computational modelling of pathogens. We focus on three disparate but interlinked research areas that produce models with very different spatial and temporal scope. First, we examine antimicrobial resistance (AMR). Many mechanisms of AMR are not well understood. As a result, it is hard to measure the current incidence of AMR, predict the future incidence, and design strategies to preserve existing antibiotic effectiveness. Next, we look at how to choose the finite number of bacterial strains that can be included in a vaccine. To do this, we need to understand what happens to vaccine and non-vaccine strains after vaccination programmes. Finally, we look at within-host modelling of antibody dynamics. The SARS-CoV-2 pandemic produced huge amounts of antibody data, prompting improvements in this area of modelling. We finish by discussing the challenges that persist in understanding these complex biological systems.
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Affiliation(s)
- John A Lees
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Timothy W Russell
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Liam P Shaw
- Department of Biology, University of Oxford, Oxford, UK
- Department of Biosciences, University of Durham, Durham, UK
| | - Joel Hellewell
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
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7
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Cori A, Kucharski A. Inference of epidemic dynamics in the COVID-19 era and beyond. Epidemics 2024; 48:100784. [PMID: 39167954 DOI: 10.1016/j.epidem.2024.100784] [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: 03/22/2024] [Revised: 06/25/2024] [Accepted: 07/11/2024] [Indexed: 08/23/2024] Open
Abstract
The COVID-19 pandemic demonstrated the key role that epidemiology and modelling play in analysing infectious threats and supporting decision making in real-time. Motivated by the unprecedented volume and breadth of data generated during the pandemic, we review modern opportunities for analysis to address questions that emerge during a major modern epidemic. Following the broad chronology of insights required - from understanding initial dynamics to retrospective evaluation of interventions, we describe the theoretical foundations of each approach and the underlying intuition. Through a series of case studies, we illustrate real life applications, and discuss implications for future work.
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Affiliation(s)
- Anne Cori
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, United Kingdom.
| | - Adam Kucharski
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, United Kingdom.
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8
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Bouros I, Hill EM, Keeling MJ, Moore S, Thompson RN. Prioritising older individuals for COVID-19 booster vaccination leads to optimal public health outcomes in a range of socio-economic settings. PLoS Comput Biol 2024; 20:e1012309. [PMID: 39116038 PMCID: PMC11309497 DOI: 10.1371/journal.pcbi.1012309] [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: 03/08/2024] [Accepted: 07/09/2024] [Indexed: 08/10/2024] Open
Abstract
The rapid development of vaccines against SARS-CoV-2 altered the course of the COVID-19 pandemic. In most countries, vaccinations were initially targeted at high-risk populations, including older individuals and healthcare workers. Now, despite substantial infection- and vaccine-induced immunity in host populations worldwide, waning immunity and the emergence of novel variants continue to cause significant waves of infection and disease. Policy makers must determine how to deploy booster vaccinations, particularly when constraints in vaccine supply, delivery and cost mean that booster vaccines cannot be administered to everyone. A key question is therefore whether older individuals should again be prioritised for vaccination, or whether alternative strategies (e.g. offering booster vaccines to the individuals who have most contacts with others and therefore drive infection) can instead offer indirect protection to older individuals. Here, we use mathematical modelling to address this question, considering SARS-CoV-2 transmission in a range of countries with different socio-economic backgrounds. We show that the population structures of different countries can have a pronounced effect on the impact of booster vaccination, even when identical booster vaccination targeting strategies are adopted. However, under the assumed transmission model, prioritising older individuals for booster vaccination consistently leads to the most favourable public health outcomes in every setting considered. This remains true for a range of assumptions about booster vaccine supply and timing, and for different assumed policy objectives of booster vaccination.
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Affiliation(s)
- Ioana Bouros
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Edward M. Hill
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, United Kingdom
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
| | - Matt J. Keeling
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, United Kingdom
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
| | - Sam Moore
- Lancaster Medical School, Lancaster University, Lancaster, United Kingdom
| | - Robin N. Thompson
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
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9
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Holm RH, Rempala GA, Choi B, Brick JM, Amraotkar AR, Keith RJ, Rouchka EC, Chariker JH, Palmer KE, Smith T, Bhatnagar A. Dynamic SARS-CoV-2 surveillance model combining seroprevalence and wastewater concentrations for post-vaccine disease burden estimates. COMMUNICATIONS MEDICINE 2024; 4:70. [PMID: 38594350 PMCID: PMC11004132 DOI: 10.1038/s43856-024-00494-y] [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: 06/13/2023] [Accepted: 03/28/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND Despite wide scale assessments, it remains unclear how large-scale severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination affected the wastewater concentration of the virus or the overall disease burden as measured by hospitalization rates. METHODS We used weekly SARS-CoV-2 wastewater concentration with a stratified random sampling of seroprevalence, and linked vaccination and hospitalization data, from April 2021-August 2021 in Jefferson County, Kentucky (USA). Our susceptible ( S ), vaccinated ( V ), variant-specific infected (I 1 andI 2 ), recovered ( R ), and seropositive ( T ) model ( S V I 2 R T ) tracked prevalence longitudinally. This was related to wastewater concentration. RESULTS Here we show the 64% county vaccination rate translate into about a 61% decrease in SARS-CoV-2 incidence. The estimated effect of SARS-CoV-2 Delta variant emergence is a 24-fold increase of infection counts, which correspond to an over 9-fold increase in wastewater concentration. Hospitalization burden and wastewater concentration have the strongest correlation (r = 0.95) at 1 week lag. CONCLUSIONS Our study underscores the importance of continuing environmental surveillance post-vaccine and provides a proof-of-concept for environmental epidemiology monitoring of infectious disease for future pandemic preparedness.
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Grants
- P20 GM103436 NIGMS NIH HHS
- P30 ES030283 NIEHS NIH HHS
- This study was supported by Centers for Disease Control and Prevention (75D30121C10273), Louisville Metro Government, James Graham Brown Foundation, Owsley Brown II Family Foundation, Welch Family, Jewish Heritage Fund for Excellence, the National Institutes of Health, (P20GM103436), the Rockefeller Foundation, the National Sciences Foundation (DMS-2027001), and the Basic Science Research Program National Research Foundation of Korea (NRF) (RS-2023-00245056).
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Affiliation(s)
- Rochelle H Holm
- Christina Lee Brown Envirome Institute, School of Medicine, University of Louisville, Louisville, KY, 40202, USA
| | - Grzegorz A Rempala
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, 43210, USA
| | - Boseung Choi
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, 43210, USA
- Division of Big Data Science, Korea University, Sejong, South Korea
- Biomedical Mathematics Group, Institute for Basic Science, Daejeon, South Korea
| | | | - Alok R Amraotkar
- Christina Lee Brown Envirome Institute, School of Medicine, University of Louisville, Louisville, KY, 40202, USA
| | - Rachel J Keith
- Christina Lee Brown Envirome Institute, School of Medicine, University of Louisville, Louisville, KY, 40202, USA
| | - Eric C Rouchka
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Louisville, Louisville, KY, 40202, USA
- KY INBRE Bioinformatics Core, University of Louisville, Louisville, KY, 40202, USA
| | - Julia H Chariker
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Louisville, Louisville, KY, 40202, USA
- KY INBRE Bioinformatics Core, University of Louisville, Louisville, KY, 40202, USA
| | - Kenneth E Palmer
- Center for Predictive Medicine for Biodefense and Emerging Infectious Diseases, University of Louisville, Louisville, KY, 40202, USA
- Department of Pharmacology and Toxicology, School of Medicine, University of Louisville, Louisville, KY, 40202, USA
| | - Ted Smith
- Christina Lee Brown Envirome Institute, School of Medicine, University of Louisville, Louisville, KY, 40202, USA
| | - Aruni Bhatnagar
- Christina Lee Brown Envirome Institute, School of Medicine, University of Louisville, Louisville, KY, 40202, USA.
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10
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Loo SL, Howerton E, Contamin L, Smith CP, Borchering RK, Mullany LC, Bents S, Carcelen E, Jung SM, Bogich T, van Panhuis WG, Kerr J, Espino J, Yan K, Hochheiser H, Runge MC, Shea K, Lessler J, Viboud C, Truelove S. The US COVID-19 and Influenza Scenario Modeling Hubs: Delivering long-term projections to guide policy. Epidemics 2024; 46:100738. [PMID: 38184954 DOI: 10.1016/j.epidem.2023.100738] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 10/02/2023] [Accepted: 12/18/2023] [Indexed: 01/09/2024] Open
Abstract
Between December 2020 and April 2023, the COVID-19 Scenario Modeling Hub (SMH) generated operational multi-month projections of COVID-19 burden in the US to guide pandemic planning and decision-making in the context of high uncertainty. This effort was born out of an attempt to coordinate, synthesize and effectively use the unprecedented amount of predictive modeling that emerged throughout the COVID-19 pandemic. Here we describe the history of this massive collective research effort, the process of convening and maintaining an open modeling hub active over multiple years, and attempt to provide a blueprint for future efforts. We detail the process of generating 17 rounds of scenarios and projections at different stages of the COVID-19 pandemic, and disseminating results to the public health community and lay public. We also highlight how SMH was expanded to generate influenza projections during the 2022-23 season. We identify key impacts of SMH results on public health and draw lessons to improve future collaborative modeling efforts, research on scenario projections, and the interface between models and policy.
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Affiliation(s)
- Sara L Loo
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; International Vaccine Access Center, Johns Hopkins, Baltimore, MD, USA.
| | - Emily Howerton
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
| | - Lucie Contamin
- Public Health Dynamics Lab, University of Pittsburgh, Pittsburgh, PA, USA
| | - Claire P Smith
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Rebecca K Borchering
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
| | - Luke C Mullany
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA
| | - Samantha Bents
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Erica Carcelen
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; International Vaccine Access Center, Johns Hopkins, Baltimore, MD, USA
| | - Sung-Mok Jung
- UNC Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tiffany Bogich
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
| | - Willem G van Panhuis
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jessica Kerr
- Public Health Dynamics Lab, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jessi Espino
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA
| | - Katie Yan
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
| | - Harry Hochheiser
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA
| | - Michael C Runge
- Eastern Ecological Science Center at the Patuxent Research Refuge, US Geological Survey, Laurel, MD, USA
| | - Katriona Shea
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; UNC Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Shaun Truelove
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; International Vaccine Access Center, Johns Hopkins, Baltimore, MD, USA
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11
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Miyazawa S, Wong TS, Ito G, Iwamoto R, Watanabe K, van Boven M, Wallinga J, Miura F. Wastewater-based reproduction numbers and projections of COVID-19 cases in three areas in Japan, November 2021 to December 2022. Euro Surveill 2024; 29:2300277. [PMID: 38390648 PMCID: PMC10899819 DOI: 10.2807/1560-7917.es.2024.29.8.2300277] [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: 05/22/2023] [Accepted: 12/20/2023] [Indexed: 02/24/2024] Open
Abstract
BackgroundWastewater surveillance has expanded globally as a means to monitor spread of infectious diseases. An inherent challenge is substantial noise and bias in wastewater data because of the sampling and quantification process, limiting the applicability of wastewater surveillance as a monitoring tool.AimTo present an analytical framework for capturing the growth trend of circulating infections from wastewater data and conducting scenario analyses to guide policy decisions.MethodsWe developed a mathematical model for translating the observed SARS-CoV-2 viral load in wastewater into effective reproduction numbers. We used an extended Kalman filter to infer underlying transmissions by smoothing out observational noise. We also illustrated the impact of different countermeasures such as expanded vaccinations and non-pharmaceutical interventions on the projected number of cases using three study areas in Japan during 2021-22 as an example.ResultsObserved notified cases were matched with the range of cases estimated by our approach with wastewater data only, across different study areas and virus quantification methods, especially when the disease prevalence was high. Estimated reproduction numbers derived from wastewater data were consistent with notification-based reproduction numbers. Our projections showed that a 10-20% increase in vaccination coverage or a 10% reduction in contact rate may suffice to initiate a declining trend in study areas.ConclusionOur study demonstrates how wastewater data can be used to track reproduction numbers and perform scenario modelling to inform policy decisions. The proposed framework complements conventional clinical surveillance, especially when reliable and timely epidemiological data are not available.
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Affiliation(s)
- Shogo Miyazawa
- Data Science Department, Shionogi and Co, Ltd, Osaka, Japan
| | - Ting Sam Wong
- SHIMADZU Corporation, Kyoto, Japan
- AdvanSentinel Inc., Osaka, Japan
| | - Genta Ito
- Data Science Department, Shionogi and Co, Ltd, Osaka, Japan
| | - Ryo Iwamoto
- Integrated Disease Care Division, Shionogi and Co, Ltd, Osaka, Japan
- Data Science Department, Shionogi and Co, Ltd, Osaka, Japan
| | - Kozo Watanabe
- Center for Marine Environmental Studies (CMES), Ehime University, Ehime, Japan
| | - Michiel van Boven
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
| | - Jacco Wallinga
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
| | - Fuminari Miura
- Center for Marine Environmental Studies (CMES), Ehime University, Ehime, Japan
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12
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Sherratt K, Carnegie AC, Kucharski A, Cori A, Pearson CAB, Jarvis CI, Overton C, Weston D, Hill EM, Knock E, Fearon E, Nightingale E, Hellewell J, Edmunds WJ, Villabona Arenas J, Prem K, Pi L, Baguelin M, Kendall M, Ferguson N, Davies N, Eggo RM, van Elsland S, Russell T, Funk S, Liu Y, Abbott S. Improving modelling for epidemic responses: reflections from members of the UK infectious disease modelling community on their experiences during the COVID-19 pandemic. Wellcome Open Res 2024; 9:12. [PMID: 38784437 PMCID: PMC11112301 DOI: 10.12688/wellcomeopenres.19601.1] [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] [Accepted: 12/10/2023] [Indexed: 05/25/2024] Open
Abstract
Background The COVID-19 pandemic both relied and placed significant burdens on the experts involved from research and public health sectors. The sustained high pressure of a pandemic on responders, such as healthcare workers, can lead to lasting psychological impacts including acute stress disorder, post-traumatic stress disorder, burnout, and moral injury, which can impact individual wellbeing and productivity. Methods As members of the infectious disease modelling community, we convened a reflective workshop to understand the professional and personal impacts of response work on our community and to propose recommendations for future epidemic responses. The attendees represented a range of career stages, institutions, and disciplines. This piece was collectively produced by those present at the session based on our collective experiences. Results Key issues we identified at the workshop were lack of institutional support, insecure contracts, unequal credit and recognition, and mental health impacts. Our recommendations include rewarding impactful work, fostering academia-public health collaboration, decreasing dependence on key individuals by developing teams, increasing transparency in decision-making, and implementing sustainable work practices. Conclusions Despite limitations in representation, this workshop provided valuable insights into the UK COVID-19 modelling experience and guidance for future public health crises. Recognising and addressing the issues highlighted is crucial, in our view, for ensuring the effectiveness of epidemic response work in the future.
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Affiliation(s)
- Katharine Sherratt
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
| | - Anna C Carnegie
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
| | - Adam Kucharski
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Carl A B Pearson
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, Western Cape, South Africa
| | - Christopher I Jarvis
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
| | - Christopher Overton
- All Hazards Intelligence, Data Analytics and Surveillance, UK Health Security Agency, London, UK
- Department of Mathematical Sciences, University of Liverpool, Liverpool, UK
- Department of Mathematics, The University of Manchester, Manchester, UK
| | - Dale Weston
- Emergency Response Department Science & Technology Behavioural Science, UK Health Security Agency, London, UK
| | - Edward M Hill
- Warwick Mathematics Institute and The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
- Joint UNIversities Pandemic and Epidemiological Research, JUNIPER, https://maths.org/juniper/, UK
| | - Edward Knock
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Elizabeth Fearon
- Institute for Global Health, University College London, London, UK
| | - Emily Nightingale
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
| | - Joel Hellewell
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - W John Edmunds
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
| | - Julián Villabona Arenas
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
| | - Kiesha Prem
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Li Pi
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Marc Baguelin
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Michelle Kendall
- Warwick Mathematics Institute and The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
| | - Neil Ferguson
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Nicholas Davies
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
| | - Rosalind M Eggo
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
| | - Sabine van Elsland
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Timothy Russell
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Sebastian Funk
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
| | - Yang Liu
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
| | - Sam Abbott
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
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13
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Howerton E, Contamin L, Mullany LC, Qin M, Reich NG, Bents S, Borchering RK, Jung SM, Loo SL, Smith CP, Levander J, Kerr J, Espino J, van Panhuis WG, Hochheiser H, Galanti M, Yamana T, Pei S, Shaman J, Rainwater-Lovett K, Kinsey M, Tallaksen K, Wilson S, Shin L, Lemaitre JC, Kaminsky J, Hulse JD, Lee EC, McKee CD, Hill A, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Pastore Y Piontti A, Vespignani A, Rosenstrom ET, Ivy JS, Mayorga ME, Swann JL, España G, Cavany S, Moore S, Perkins A, Hladish T, Pillai A, Ben Toh K, Longini I, Chen S, Paul R, Janies D, Thill JC, Bouchnita A, Bi K, Lachmann M, Fox SJ, Meyers LA, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Hurt B, Chen J, Mortveit H, Wilson A, Marathe M, Hoops S, Bhattacharya P, Machi D, Cadwell BL, Healy JM, Slayton RB, Johansson MA, Biggerstaff M, Truelove S, Runge MC, Shea K, Viboud C, Lessler J. Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty. Nat Commun 2023; 14:7260. [PMID: 37985664 PMCID: PMC10661184 DOI: 10.1038/s41467-023-42680-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 10/17/2023] [Indexed: 11/22/2023] Open
Abstract
Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections.
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Affiliation(s)
- Emily Howerton
- The Pennsylvania State University, University Park, PA, USA.
| | | | - Luke C Mullany
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | | | | | - Samantha Bents
- National Institutes of Health Fogarty International Center, Bethesda, MD, USA
| | - Rebecca K Borchering
- The Pennsylvania State University, University Park, PA, USA
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Sung-Mok Jung
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Sara L Loo
- Johns Hopkins University, Baltimore, MD, USA
| | | | | | | | - J Espino
- University of Pittsburgh, Pittsburgh, PA, USA
| | | | | | | | | | - Sen Pei
- Columbia University, New York, NY, USA
| | | | | | - Matt Kinsey
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | - Kate Tallaksen
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | - Shelby Wilson
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | - Lauren Shin
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | | | | | | | | | | | - Alison Hill
- Johns Hopkins University, Baltimore, MD, USA
| | - Dean Karlen
- University of Victoria, Victoria, BC, Canada
| | | | | | - Kunpeng Mu
- Northeastern University, Boston, MA, USA
| | | | | | | | | | - Julie S Ivy
- North Carolina State University, Raleigh, NC, USA
| | | | | | | | - Sean Cavany
- University of Notre Dame, Notre Dame, IN, USA
| | - Sean Moore
- University of Notre Dame, Notre Dame, IN, USA
| | | | | | | | | | | | - Shi Chen
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Rajib Paul
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Daniel Janies
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | | | | | - Kaiming Bi
- University of Texas at Austin, Austin, TX, USA
| | | | | | | | | | | | | | | | - Bryan Lewis
- University of Virginia, Charlottesville, VA, USA
| | - Brian Klahn
- University of Virginia, Charlottesville, VA, USA
| | | | | | | | | | | | | | - Stefan Hoops
- University of Virginia, Charlottesville, VA, USA
| | | | - Dustin Machi
- University of Virginia, Charlottesville, VA, USA
| | - Betsy L Cadwell
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Jessica M Healy
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | | | | | | | | | - Michael C Runge
- U.S. Geological Survey Eastern Ecological Science Center, Laurel, MD, USA
| | - Katriona Shea
- The Pennsylvania State University, University Park, PA, USA
| | - Cécile Viboud
- National Institutes of Health Fogarty International Center, Bethesda, MD, USA.
| | - Justin Lessler
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Johns Hopkins University, Baltimore, MD, USA.
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14
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Chapman LAC, Aubry M, Maset N, Russell TW, Knock ES, Lees JA, Mallet HP, Cao-Lormeau VM, Kucharski AJ. Impact of vaccinations, boosters and lockdowns on COVID-19 waves in French Polynesia. Nat Commun 2023; 14:7330. [PMID: 37957160 PMCID: PMC10643399 DOI: 10.1038/s41467-023-43002-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 10/27/2023] [Indexed: 11/15/2023] Open
Abstract
Estimating the impact of vaccination and non-pharmaceutical interventions on COVID-19 incidence is complicated by several factors, including successive emergence of SARS-CoV-2 variants of concern and changing population immunity from vaccination and infection. We develop an age-structured multi-strain COVID-19 transmission model and inference framework to estimate vaccination and non-pharmaceutical intervention impact accounting for these factors. We apply this framework to COVID-19 waves in French Polynesia and estimate that the vaccination programme averted 34.8% (95% credible interval: 34.5-35.2%) of 223,000 symptomatic cases, 49.6% (48.7-50.5%) of 5830 hospitalisations and 64.2% (63.1-65.3%) of 1540 hospital deaths that would have occurred in a scenario without vaccination up to May 2022. We estimate the booster campaign contributed 4.5%, 1.9%, and 0.4% to overall reductions in cases, hospitalisations, and deaths. Our results suggest that removing lockdowns during the first two waves would have had non-linear effects on incidence by altering accumulation of population immunity. Our estimates of vaccination and booster impact differ from those for other countries due to differences in age structure, previous exposure levels and timing of variant introduction relative to vaccination, emphasising the importance of detailed analysis that accounts for these factors.
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Affiliation(s)
- Lloyd A C Chapman
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.
| | - Maite Aubry
- Laboratoire de recherche sur les infections virales émergentes, Institut Louis Malardé, Tahiti, French Polynesia
| | - Noémie Maset
- Cellule Epi-surveillance Plateforme COVID-19, Tahiti, French Polynesia
| | - Timothy W Russell
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Edward S Knock
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - John A Lees
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Cambridgeshire, UK
| | | | - Van-Mai Cao-Lormeau
- Laboratoire de recherche sur les infections virales émergentes, Institut Louis Malardé, Tahiti, French Polynesia
| | - Adam J Kucharski
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
- Laboratoire de recherche sur les infections virales émergentes, Institut Louis Malardé, Tahiti, French Polynesia
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15
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Cavallaro M, Dyson L, Tildesley MJ, Todkill D, Keeling MJ. Spatio-temporal surveillance and early detection of SARS-CoV-2 variants of concern: a retrospective analysis. J R Soc Interface 2023; 20:20230410. [PMID: 37963560 PMCID: PMC10645511 DOI: 10.1098/rsif.2023.0410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 10/20/2023] [Indexed: 11/16/2023] Open
Abstract
The SARS-CoV-2 pandemic has been characterized by the repeated emergence of genetically distinct virus variants of increased transmissibility and immune evasion compared to pre-existing lineages. In many countries, their containment required the intervention of public health authorities and the imposition of control measures. While the primary role of testing is to identify infection, target treatment, and limit spread (through isolation and contact tracing), a secondary benefit is in terms of surveillance and the early detection of new variants. Here we study the spatial invasion and early spread of the Alpha, Delta and Omicron (BA.1 and BA.2) variants in England from September 2020 to February 2022 using the random neighbourhood covering (RaNCover) method. This is a statistical technique for the detection of aberrations in spatial point processes, which we tailored here to community PCR (polymerase-chain-reaction) test data where the TaqPath kit provides a proxy measure of the switch between variants. Retrospectively, RaNCover detected the earliest signals associated with the four novel variants that led to large infection waves in England. With suitable data our method therefore has the potential to rapidly detect outbreaks of future SARS-CoV-2 variants, thus helping to inform targeted public health interventions.
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Affiliation(s)
- Massimo Cavallaro
- School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, UK
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
| | - Louise Dyson
- School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, UK
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
| | - Michael J. Tildesley
- School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, UK
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
| | - Dan Todkill
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Matt J. Keeling
- School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, UK
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
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16
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Brand SPC, Cavallaro M, Cumming F, Turner C, Florence I, Blomquist P, Hilton J, Guzman-Rincon LM, House T, Nokes DJ, Keeling MJ. The role of vaccination and public awareness in forecasts of Mpox incidence in the United Kingdom. Nat Commun 2023; 14:4100. [PMID: 37433797 PMCID: PMC10336136 DOI: 10.1038/s41467-023-38816-8] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 05/15/2023] [Indexed: 07/13/2023] Open
Abstract
Beginning in May 2022, Mpox virus spread rapidly in high-income countries through close human-to-human contact primarily amongst communities of gay, bisexual and men who have sex with men (GBMSM). Behavioural change arising from increased knowledge and health warnings may have reduced the rate of transmission and modified Vaccinia-based vaccination is likely to be an effective longer-term intervention. We investigate the UK epidemic presenting 26-week projections using a stochastic discrete-population transmission model which includes GBMSM status, rate of formation of new sexual partnerships, and clique partitioning of the population. The Mpox cases peaked in mid-July; our analysis is that the decline was due to decreased transmission rate per infected individual and infection-induced immunity among GBMSM, especially those with the highest rate of new partners. Vaccination did not cause Mpox incidence to turn over, however, we predict that a rebound in cases due to behaviour reversion was prevented by high-risk group-targeted vaccination.
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Affiliation(s)
- Samuel P C Brand
- The Zeeman Institute for Systems Biology Infectious Disease Epidemiology Research (SBIDER), Coventry, UK.
- School of Life Sciences, University of Warwick, Coventry, UK.
| | - Massimo Cavallaro
- The Zeeman Institute for Systems Biology Infectious Disease Epidemiology Research (SBIDER), Coventry, UK
- School of Life Sciences, University of Warwick, Coventry, UK
- Mathematics Institute, University of Warwick, Coventry, UK
| | | | | | | | | | - Joe Hilton
- The Zeeman Institute for Systems Biology Infectious Disease Epidemiology Research (SBIDER), Coventry, UK
- School of Life Sciences, University of Warwick, Coventry, UK
| | - Laura M Guzman-Rincon
- The Zeeman Institute for Systems Biology Infectious Disease Epidemiology Research (SBIDER), Coventry, UK
- School of Life Sciences, University of Warwick, Coventry, UK
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, UK
| | - D James Nokes
- The Zeeman Institute for Systems Biology Infectious Disease Epidemiology Research (SBIDER), Coventry, UK
- School of Life Sciences, University of Warwick, Coventry, UK
| | - Matt J Keeling
- The Zeeman Institute for Systems Biology Infectious Disease Epidemiology Research (SBIDER), Coventry, UK
- School of Life Sciences, University of Warwick, Coventry, UK
- Mathematics Institute, University of Warwick, Coventry, UK
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17
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Kendall M, Tsallis D, Wymant C, Di Francia A, Balogun Y, Didelot X, Ferretti L, Fraser C. Epidemiological impacts of the NHS COVID-19 app in England and Wales throughout its first year. Nat Commun 2023; 14:858. [PMID: 36813770 PMCID: PMC9947127 DOI: 10.1038/s41467-023-36495-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 02/02/2023] [Indexed: 02/24/2023] Open
Abstract
The NHS COVID-19 app was launched in England and Wales in September 2020, with a Bluetooth-based contact tracing functionality designed to reduce transmission of SARS-CoV-2. We show that user engagement and the app's epidemiological impacts varied according to changing social and epidemic characteristics throughout the app's first year. We describe the interaction and complementarity of manual and digital contact tracing approaches. Results of our statistical analyses of anonymised, aggregated app data include that app users who were recently notified were more likely to test positive than app users who were not recently notified, by a factor that varied considerably over time. We estimate that the app's contact tracing function alone averted about 1 million cases (sensitivity analysis 450,000-1,400,000) during its first year, corresponding to 44,000 hospital cases (SA 20,000-60,000) and 9,600 deaths (SA 4600-13,000).
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Affiliation(s)
- Michelle Kendall
- Department of Statistics, University of Warwick, Coventry, CV4 7AL, UK.
| | - Daphne Tsallis
- Zühlke Engineering Ltd, 80 Great Eastern St, London, EC2A 3JL, UK
| | - Chris Wymant
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, Old Road Campus, University of Oxford, Oxford, OX3 7LF, UK
- Pandemic Sciences Institute, Nuffield Department for Medicine, University of Oxford, Old Road Campus, Oxford, OX3 7DQ, UK
| | - Andrea Di Francia
- UK Health Security Agency, Nobel House, 17 Smith Square, London, SW1P 3JR, UK
| | - Yakubu Balogun
- UK Health Security Agency, Nobel House, 17 Smith Square, London, SW1P 3JR, UK
| | - Xavier Didelot
- Department of Statistics, University of Warwick, Coventry, CV4 7AL, UK
- School of Life Sciences, University of Warwick, Coventry, CV4 7AL, UK
| | - Luca Ferretti
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, Old Road Campus, University of Oxford, Oxford, OX3 7LF, UK
- Pandemic Sciences Institute, Nuffield Department for Medicine, University of Oxford, Old Road Campus, Oxford, OX3 7DQ, UK
| | - Christophe Fraser
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, Old Road Campus, University of Oxford, Oxford, OX3 7LF, UK
- Pandemic Sciences Institute, Nuffield Department for Medicine, University of Oxford, Old Road Campus, Oxford, OX3 7DQ, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Headington, Oxford, OX3 7BN, UK
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18
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The impacts of SARS-CoV-2 vaccine dose separation and targeting on the COVID-19 epidemic in England. Nat Commun 2023; 14:740. [PMID: 36765050 PMCID: PMC9911946 DOI: 10.1038/s41467-023-35943-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 01/09/2023] [Indexed: 02/12/2023] Open
Abstract
In late 2020, the JCVI (the Joint Committee on Vaccination and Immunisation, which provides advice to the Department of Health and Social Care, England) made two important recommendations for the initial roll-out of the COVID-19 vaccine. The first was that vaccines should be targeted to older and vulnerable people, with the aim of maximally preventing disease rather than infection. The second was to increase the interval between first and second doses from 3 to 12 weeks. Here, we re-examine these recommendations through a mathematical model of SARS-CoV-2 infection in England. We show that targeting the most vulnerable had the biggest immediate impact (compared to targeting younger individuals who may be more responsible for transmission). The 12-week delay was also highly beneficial, estimated to have averted between 32-72 thousand hospital admissions and 4-9 thousand deaths over the first ten months of the campaign (December 2020-September 2021) depending on the assumed interaction between dose interval and efficacy.
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19
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Lustig A, Vattiato G, Maclaren O, Watson LM, Datta S, Plank MJ. Modelling the impact of the Omicron BA.5 subvariant in New Zealand. J R Soc Interface 2023; 20:20220698. [PMID: 36722072 PMCID: PMC9890098 DOI: 10.1098/rsif.2022.0698] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 01/06/2023] [Indexed: 02/02/2023] Open
Abstract
New Zealand experienced a wave of the Omicron variant of SARS-CoV-2 in early 2022, which occurred against a backdrop of high two-dose vaccination rates, ongoing roll-out of boosters and paediatric doses, and negligible levels of prior infection. New Omicron subvariants have subsequently emerged with a significant growth advantage over the previously dominant BA.2. We investigated a mathematical model that included waning of vaccine-derived and infection-derived immunity, as well as the impact of the BA.5 subvariant which began spreading in New Zealand in May 2022. The model was used to provide scenarios to the New Zealand Government with differing levels of BA.5 growth advantage, helping to inform policy response and healthcare system preparedness during the winter period. In all scenarios investigated, the projected peak in new infections during the BA.5 wave was smaller than in the first Omicron wave in March 2022. However, results indicated that the peak hospital occupancy was likely to be higher than in March 2022, primarily due to a shift in the age distribution of infections to older groups. We compare model results with subsequent epidemiological data and show that the model provided a good projection of cases, hospitalizations and deaths during the BA.5 wave.
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Affiliation(s)
| | - Giorgia Vattiato
- Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
- Department of Physics, The University of Auckland, Auckland, New Zealand
| | - Oliver Maclaren
- Department of Engineering Science, University of Auckland, Auckland, New Zealand
| | - Leighton M. Watson
- School of Earth and Environment, University of Canterbury, Christchurch, New Zealand
| | - Samik Datta
- National Institute of Water and Atmospheric Research, Wellington, New Zealand
| | - Michael J. Plank
- Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
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20
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Patterns of reported infection and reinfection of SARS-CoV-2 in England. J Theor Biol 2023; 556:111299. [PMID: 36252843 PMCID: PMC9568275 DOI: 10.1016/j.jtbi.2022.111299] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 09/27/2022] [Accepted: 09/30/2022] [Indexed: 11/05/2022]
Abstract
One of the key features of any infectious disease is whether infection generates long-lasting immunity or whether repeated reinfection is common. In the former, the long-term dynamics are driven by the birth of susceptible individuals while in the latter the dynamics are governed by the speed of waning immunity. Between these two extremes a range of scenarios is possible. During the early waves of SARS-CoV-2, the underlying paradigm was for long-lasting immunity, but more recent data and in particular the 2022 Omicron waves have shown that reinfection can be relatively common. Here we investigate reported SARS-CoV-2 cases in England, partitioning the data into four main waves, and consider the temporal distribution of first and second reports of infection. We show that a simple low-dimensional statistical model of random (but scaled) reinfection captures much of the observed dynamics, with the value of this scaling, k, providing information of underlying epidemiological patterns. We conclude that there is considerable heterogeneity in risk of reporting reinfection by wave, age-group and location. The high levels of reinfection in the Omicron wave (we estimate that 18% of all Omicron cases had been previously infected, although not necessarily previously reported infection) point to reinfection events dominating future COVID-19 dynamics. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".
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21
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Plank MJ, Hendy SC, Binny RN, Vattiato G, Lustig A, Maclaren OJ. Using mechanistic model-based inference to understand and project epidemic dynamics with time-varying contact and vaccination rates. Sci Rep 2022; 12:20451. [PMID: 36443439 PMCID: PMC9702885 DOI: 10.1038/s41598-022-25018-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 11/23/2022] [Indexed: 11/29/2022] Open
Abstract
Epidemiological models range in complexity from relatively simple statistical models that make minimal assumptions about the variables driving epidemic dynamics to more mechanistic models that include effects such as vaccine-derived and infection-derived immunity, population structure and heterogeneity. The former are often fitted to data in real-time and used for short-term forecasting, while the latter are more suitable for comparing longer-term scenarios under differing assumptions about control measures or other factors. Here, we present a mechanistic model of intermediate complexity that can be fitted to data in real-time but is also suitable for investigating longer-term dynamics. Our approach provides a bridge between primarily empirical approaches to forecasting and assumption-driven scenario models. The model was developed as a policy advice tool for New Zealand's 2021 outbreak of the Delta variant of SARS-CoV-2 and includes the effects of age structure, non-pharmaceutical interventions, and the ongoing vaccine rollout occurring during the time period studied. We use an approximate Bayesian computation approach to infer the time-varying transmission coefficient from real-time data on reported cases. We then compare projections of the model with future, out-of-sample data. We find that this approach produces a good fit with in-sample data and reasonable forward projections given the inherent limitations of predicting epidemic dynamics during periods of rapidly changing policy and behaviour. Results from the model helped inform the New Zealand Government's policy response throughout the outbreak.
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Affiliation(s)
- Michael J Plank
- School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand.
| | - Shaun C Hendy
- Department of Physics, University of Auckland, Auckland, New Zealand
| | | | - Giorgia Vattiato
- School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
- Department of Physics, University of Auckland, Auckland, New Zealand
| | | | - Oliver J Maclaren
- Department of Engineering Science, University of Auckland, Auckland, New Zealand
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