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Freedman AS, Sheen JK, Tsai S, Yao J, Lifshitz E, Adinaro D, Levin SA, Grenfell BT, Metcalf CJE. Inferring COVID-19 testing and vaccination behavior from New Jersey testing data. Proc Natl Acad Sci U S A 2024; 121:e2314357121. [PMID: 38630720 PMCID: PMC11047110 DOI: 10.1073/pnas.2314357121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 03/08/2024] [Indexed: 04/19/2024] Open
Abstract
Characterizing the relationship between disease testing behaviors and infectious disease dynamics is of great importance for public health. Tests for both current and past infection can influence disease-related behaviors at the individual level, while population-level knowledge of an epidemic's course may feed back to affect one's likelihood of taking a test. The COVID-19 pandemic has generated testing data on an unprecedented scale for tests detecting both current infection (PCR, antigen) and past infection (serology); this opens the way to characterizing the complex relationship between testing behavior and infection dynamics. Leveraging a rich database of individualized COVID-19 testing histories in New Jersey, we analyze the behavioral relationships between PCR and serology tests, infection, and vaccination. We quantify interactions between individuals' test-taking tendencies and their past testing and infection histories, finding that PCR tests were disproportionately taken by people currently infected, and serology tests were disproportionately taken by people with past infection or vaccination. The effects of previous positive test results on testing behavior are less consistent, as individuals with past PCR positives were more likely to take subsequent PCR and serology tests at some periods of the epidemic time course and less likely at others. Lastly, we fit a model to the titer values collected from serology tests to infer vaccination trends, finding a marked decrease in vaccination rates among individuals who had previously received a positive PCR test. These results exemplify the utility of individualized testing histories in uncovering hidden behavioral variables affecting testing and vaccination.
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Affiliation(s)
- Ari S. Freedman
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ08544
| | - Justin K. Sheen
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ08544
| | - Stella Tsai
- New Jersey Department of Health, Trenton, NJ08625
| | - Jihong Yao
- New Jersey Department of Health, Trenton, NJ08625
| | | | | | - Simon A. Levin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ08544
| | - Bryan T. Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ08544
| | - C. Jessica E. Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ08544
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2
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Park SW, Messacar K, Douek DC, Spaulding AB, Metcalf CJE, Grenfell BT. Predicting the impact of COVID-19 non-pharmaceutical intervention on short- and medium-term dynamics of enterovirus D68 in the US. Epidemics 2024; 46:100736. [PMID: 38118274 DOI: 10.1016/j.epidem.2023.100736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 12/02/2023] [Accepted: 12/10/2023] [Indexed: 12/22/2023] Open
Abstract
Recent outbreaks of enterovirus D68 (EV-D68) infections, and their causal linkage with acute flaccid myelitis (AFM), continue to pose a serious public health concern. During 2020 and 2021, the dynamics of EV-D68 and other pathogens have been significantly perturbed by non-pharmaceutical interventions against COVID-19; this perturbation presents a powerful natural experiment for exploring the dynamics of these endemic infections. In this study, we analyzed publicly available data on EV-D68 infections, originally collected through the New Vaccine Surveillance Network, to predict their short- and long-term dynamics following the COVID-19 interventions. Although long-term predictions are sensitive to our assumptions about underlying dynamics and changes in contact rates during the NPI periods, the likelihood of a large outbreak in 2023 appears to be low. Comprehensive surveillance data are needed to accurately characterize future dynamics of EV-D68. The limited incidence of AFM cases in 2022, despite large EV-D68 outbreaks, poses further questions for the timing of the next AFM outbreaks.
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Affiliation(s)
- Sang Woo Park
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.
| | - Kevin Messacar
- Department of Pediatrics, Section of Infectious Diseases, University of Colorado School of Medicine and Children's Hospital Colorado, Aurora, CO, USA
| | - Daniel C Douek
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Alicen B Spaulding
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
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3
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Sheen JK, Rasambainarivo F, Saad-Roy CM, Grenfell BT, Metcalf CJE. Markets as drivers of selection for highly virulent poultry pathogens. Nat Commun 2024; 15:605. [PMID: 38242897 PMCID: PMC10799013 DOI: 10.1038/s41467-024-44777-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 01/03/2024] [Indexed: 01/21/2024] Open
Abstract
Theoretical models have successfully predicted the evolution of poultry pathogen virulence in industrialized farm contexts of broiler chicken populations. Whether there are ecological factors specific to more traditional rural farming that affect virulence is an open question. Within non-industrialized farming networks, live bird markets are known to be hotspots of transmission, but whether they could shift selection pressures on the evolution of poultry pathogen virulence has not been addressed. Here, we revisit predictions for the evolution of virulence for viral poultry pathogens, such as Newcastle's disease virus, Marek's disease virus, and influenza virus, H5N1, using a compartmental model that represents transmission in rural markets. We show that both the higher turnover rate and higher environmental persistence in markets relative to farms could select for higher optimal virulence strategies. In contrast to theoretical results modeling industrialized poultry farms, we find that cleaning could also select for decreased virulence in the live poultry market setting. Additionally, we predict that more virulent strategies selected in markets could circulate solely within poultry located in markets. Thus, we recommend the close monitoring of markets not only as hotspots of transmission, but as potential sources of more virulent strains of poultry pathogens.
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Affiliation(s)
- Justin K Sheen
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.
| | - Fidisoa Rasambainarivo
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Mahaliana Labs SARL, Antananarivo, Madagascar
| | - Chadi M Saad-Roy
- Miller Institute for Basic Research in Science, University of California, Berkeley, CA, USA
- Department of Integrative Biology, University of California, Berkeley, CA, USA
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- School of Public and International Affairs, Princeton University, Princeton, NJ, USA
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4
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Bents SJ, Viboud C, Grenfell BT, Hogan AB, Tempia S, von Gottberg A, Moyes J, Walaza S, Hansen C, Cohen C, Baker RE. Modeling the impact of COVID-19 nonpharmaceutical interventions on respiratory syncytial virus transmission in South Africa. Influenza Other Respir Viruses 2023; 17:e13229. [PMID: 38090227 PMCID: PMC10710953 DOI: 10.1111/irv.13229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 09/25/2023] [Accepted: 11/11/2023] [Indexed: 12/18/2023] Open
Abstract
Background The South African government employed various nonpharmaceutical interventions (NPIs) to reduce the spread of SARS-CoV-2. Surveillance data from South Africa indicates reduced circulation of respiratory syncytial virus (RSV) throughout the 2020-2021 seasons. Here, we use a mechanistic transmission model to project the rebound of RSV in the two subsequent seasons. Methods We fit an age-structured epidemiological model to hospitalization data from national RSV surveillance in South Africa, allowing for time-varying reduction in RSV transmission during periods of COVID-19 circulation. We apply the model to project the rebound of RSV in the 2022 and 2023 seasons. Results We projected an early and intense outbreak of RSV in April 2022, with an age shift to older infants (6-23 months old) experiencing a larger portion of severe disease burden than typical. In March 2022, government alerts were issued to prepare the hospital system for this potentially intense outbreak. We then assess the 2022 predictions and project the 2023 season. Model predictions for 2023 indicate that RSV activity has not fully returned to normal, with a projected early and moderately intense wave. We estimate that NPIs reduced RSV transmission between 15% and 50% during periods of COVID-19 circulation. Conclusions A wide range of NPIs impacted the dynamics of the RSV outbreaks throughout 2020-2023 in regard to timing, magnitude, and age structure, with important implications in a low- and middle-income countries (LMICs) setting where RSV interventions remain limited. More efforts should focus on adapting RSV models to LMIC data to project the impact of upcoming medical interventions for this disease.
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Affiliation(s)
- Samantha J. Bents
- Fogarty International Center, National Institutes of HealthBethesdaMarylandUSA
| | - Cécile Viboud
- Fogarty International Center, National Institutes of HealthBethesdaMarylandUSA
| | - Bryan T. Grenfell
- Department of Ecology and Evolutionary BiologyPrinceton UniversityPrincetonNew JerseyUSA
| | - Alexandra B. Hogan
- School of Population HealthUniversity of New South WalesSydneyNew South WalesAustralia
| | - Stefano Tempia
- Centre for Respiratory Diseases and MeningitisNational Institute for Communicable Diseases of the National Health Laboratory ServiceJohannesburgSouth Africa
- School of Public Health, Faculty of Health SciencesUniversity of WitwatersrandJohannesburgSouth Africa
| | - Anne von Gottberg
- Centre for Respiratory Diseases and MeningitisNational Institute for Communicable Diseases of the National Health Laboratory ServiceJohannesburgSouth Africa
- School of Pathology, Faculty of Health SciencesUniversity of WitwatersrandJohannesburgSouth Africa
- Department of Pathology, Faculty of Health SciencesUniversity of Cape TownCape TownSouth Africa
| | - Jocelyn Moyes
- Centre for Respiratory Diseases and MeningitisNational Institute for Communicable Diseases of the National Health Laboratory ServiceJohannesburgSouth Africa
- School of Public Health, Faculty of Health SciencesUniversity of WitwatersrandJohannesburgSouth Africa
| | - Sibongile Walaza
- Centre for Respiratory Diseases and MeningitisNational Institute for Communicable Diseases of the National Health Laboratory ServiceJohannesburgSouth Africa
- School of Public Health, Faculty of Health SciencesUniversity of WitwatersrandJohannesburgSouth Africa
| | - Chelsea Hansen
- Fogarty International Center, National Institutes of HealthBethesdaMarylandUSA
- Brotman Baty InstituteUniversity of WashingtonSeattleWashingtonUSA
- PandemiX Center, Department of Science & EnvironmentRoskilde UniversityRoskildeDenmark
| | - Cheryl Cohen
- Centre for Respiratory Diseases and MeningitisNational Institute for Communicable Diseases of the National Health Laboratory ServiceJohannesburgSouth Africa
- School of Public Health, Faculty of Health SciencesUniversity of WitwatersrandJohannesburgSouth Africa
| | - Rachel E. Baker
- School of Public HealthBrown UniversityProvidenceRhode IslandUSA
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Saad-Roy CM, Morris SE, Baker RE, Farrar J, Graham AL, Levin SA, Wagner CE, Metcalf CJE, Grenfell BT. Medium-term scenarios of COVID-19 as a function of immune uncertainties and chronic disease. J R Soc Interface 2023; 20:20230247. [PMID: 37643641 PMCID: PMC10465195 DOI: 10.1098/rsif.2023.0247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 08/04/2023] [Indexed: 08/31/2023] Open
Abstract
As the SARS-CoV-2 trajectory continues, the longer-term immuno-epidemiology of COVID-19, the dynamics of Long COVID, and the impact of escape variants are important outstanding questions. We examine these remaining uncertainties with a simple modelling framework that accounts for multiple (antigenic) exposures via infection or vaccination. If immunity (to infection or Long COVID) accumulates rapidly with the valency of exposure, we find that infection levels and the burden of Long COVID are markedly reduced in the medium term. More pessimistic assumptions on host adaptive immune responses illustrate that the longer-term burden of COVID-19 may be elevated for years to come. However, we also find that these outcomes could be mitigated by the eventual introduction of a vaccine eliciting robust (i.e. durable, transmission-blocking and/or 'evolution-proof') immunity. Overall, our work stresses the wide range of future scenarios that still remain, the importance of collecting real-world epidemiological data to identify likely outcomes, and the crucial need for the development of a highly effective transmission-blocking, durable and broadly protective vaccine.
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Affiliation(s)
- Chadi M. Saad-Roy
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Miller Institute for Basic Research in Science, University of California, Berkeley, CA, USA
- Department of Integrative Biology, University of California, Berkeley, CA, USA
| | - Sinead E. Morris
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY, USA
| | - Rachel E. Baker
- Department of Epidemiology, Brown University, Providence, RI, USA
- Institute at Brown for Environment and Society, Brown University, Providence, RI, USA
| | | | - Andrea L. Graham
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Simon A. Levin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | | | - C. Jessica. E. Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Bryan T. Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- School of Public and International Affairs, Princeton University, Princeton, NJ, USA
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6
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Saad-Roy CM, Levin SA, Grenfell BT, Boots M. Epidemiological impacts of post-infection mortality. Proc Biol Sci 2023; 290:20230343. [PMID: 37434526 PMCID: PMC10336371 DOI: 10.1098/rspb.2023.0343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/16/2023] [Indexed: 07/13/2023] Open
Abstract
Infectious diseases may cause some long-term damage to their host, leading to elevated mortality even after recovery. Mortality due to complications from so-called 'long COVID' is a stark illustration of this potential, but the impacts of such post-infection mortality (PIM) on epidemic dynamics are not known. Using an epidemiological model that incorporates PIM, we examine the importance of this effect. We find that in contrast to mortality during infection, PIM can induce epidemic cycling. The effect is due to interference between elevated mortality and reinfection through the previously infected susceptible pool. In particular, robust immunity (via decreased susceptibility to reinfection) reduces the likelihood of cycling; on the other hand, disease-induced mortality can interact with weak PIM to generate periodicity. In the absence of PIM, we prove that the unique endemic equilibrium is stable and therefore our key result is that PIM is an overlooked phenomenon that is likely to be destabilizing. Overall, given potentially widespread effects, our findings highlight the importance of characterizing heterogeneity in susceptibility (via both PIM and robustness of host immunity) for accurate epidemiological predictions. In particular, for diseases without robust immunity, such as SARS-CoV-2, PIM may underlie complex epidemiological dynamics especially in the context of seasonal forcing.
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Affiliation(s)
- Chadi M. Saad-Roy
- Miller Institute for Basic Research in Science, University of California, Berkeley, CA, USA
- Department of Integrative Biology, University of California, Berkeley, CA, USA
| | - Simon A. Levin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Bryan T. Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Mike Boots
- Department of Integrative Biology, University of California, Berkeley, CA, USA
- Department of Biosciences, University of Exeter, Penryn, UK
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7
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Park SW, Daskalaki I, Izzo RM, Aranovich I, te Velthuis AJW, Notterman DA, Metcalf CJE, Grenfell BT. Relative role of community transmission and campus contagion in driving the spread of SARS-CoV-2: Lessons from Princeton University. PNAS Nexus 2023; 2:pgad201. [PMID: 37457892 PMCID: PMC10338902 DOI: 10.1093/pnasnexus/pgad201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 05/03/2023] [Accepted: 05/22/2023] [Indexed: 07/18/2023]
Abstract
Mathematical models have played a crucial role in exploring and guiding pandemic responses. University campuses present a particularly well-documented case for institutional outbreaks, thereby providing a unique opportunity to understand detailed patterns of pathogen spread. Here, we present descriptive and modeling analyses of SARS-CoV-2 transmission on the Princeton University (PU) campus-this model was used throughout the pandemic to inform policy decisions and operational guidelines for the university campus. Epidemic patterns between the university campus and surrounding communities exhibit strong spatiotemporal correlations. Mathematical modeling analysis further suggests that the amount of on-campus transmission was likely limited during much of the wider pandemic until the end of 2021. Finally, we find that a superspreading event likely played a major role in driving the Omicron variant outbreak on the PU campus during the spring semester of the 2021-2022 academic year. Despite large numbers of cases on campus in this period, case levels in surrounding communities remained low, suggesting that there was little spillover transmission from campus to the local community.
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Affiliation(s)
- Sang Woo Park
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
| | - Irini Daskalaki
- University Health Services, Princeton University, Princeton, NJ 08544, USA
| | - Robin M Izzo
- Environmental Health and Safety, Princeton University, Princeton, NJ 08544, USA
| | - Irina Aranovich
- Princeton University Clinical Laboratory, Princeton University, Princeton, NJ 08544, USA
| | | | - Daniel A Notterman
- Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
- Princeton School of Public and International Affairs, Princeton University, Princeton, NJ 08544, USA
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
- Princeton School of Public and International Affairs, Princeton University, Princeton, NJ 08544, USA
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8
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Schrom E, Kinzig A, Forrest S, Graham AL, Levin SA, Bergstrom CT, Castillo-Chavez C, Collins JP, de Boer RJ, Doupé A, Ensafi R, Feldman S, Grenfell BT, Halderman JA, Huijben S, Maley C, Moses M, Perelson AS, Perrings C, Plotkin J, Rexford J, Tiwari M. Challenges in cybersecurity: Lessons from biological defense systems. Math Biosci 2023:109024. [PMID: 37270102 DOI: 10.1016/j.mbs.2023.109024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 04/27/2023] [Accepted: 05/20/2023] [Indexed: 06/05/2023]
Abstract
Defending against novel, repeated, or unpredictable attacks, while avoiding attacks on the 'self', are the central problems of both mammalian immune systems and computer systems. Both systems have been studied in great detail, but with little exchange of information across the different disciplines. Here, we present a conceptual framework for structured comparisons across the fields of biological immunity and cybersecurity, by framing the context of defense, considering different (combinations of) defensive strategies, and evaluating defensive performance. Throughout this paper, we pose open questions for further exploration. We hope to spark the interdisciplinary discovery of general principles of optimal defense, which can be understood and applied in biological immunity, cybersecurity, and other defensive realms.
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Affiliation(s)
- Edward Schrom
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, United States of America
| | - Ann Kinzig
- School of Life Sciences, Arizona State University, Tempe, AZ 85287, United States of America
| | - Stephanie Forrest
- Biodesign Center for Biocomputation, Security and Society, Arizona State University, Tempe, AZ 85287, United States of America; School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85287, United States of America; Santa Fe Institute, Santa Fe, NM 87501, United States of America
| | - Andrea L Graham
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, United States of America; Santa Fe Institute, Santa Fe, NM 87501, United States of America
| | - Simon A Levin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, United States of America.
| | - Carl T Bergstrom
- Department of Biology, University of Washington, Seattle, WA 98195, United States of America
| | - Carlos Castillo-Chavez
- School of Human Evolution and Social Change, Arizona State University, Tempe, AZ 85287, United States of America
| | - James P Collins
- School of Life Sciences, Arizona State University, Tempe, AZ 85287, United States of America
| | - Rob J de Boer
- Theoretical Biology and Bioinformatics, Utrecht University, 3584 CH Utrecht, The Netherlands
| | - Adam Doupé
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85287, United States of America; Center for Cybersecurity and Trusted Foundations, Global Security Initiative, Arizona State University, Tempe, AZ 85287, United States of America
| | - Roya Ensafi
- Department of Electrical Engineering and Computer Science, Computer Science and Engineering Division, University of Michigan, Ann Arbor, MI 48109, United States of America
| | - Stuart Feldman
- Schmidt Futures, New York, NY 10011, United States of America
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, United States of America; Princeton School of Public and International Affairs, Princeton University, Princeton, NJ 08544, United States of America
| | - J Alex Halderman
- Department of Electrical Engineering and Computer Science, Computer Science and Engineering Division, University of Michigan, Ann Arbor, MI 48109, United States of America; Center for Computer Security and Society, University of Michigan, Ann Arbor, MI 48109, United States of America
| | - Silvie Huijben
- School of Life Sciences, Arizona State University, Tempe, AZ 85287, United States of America
| | - Carlo Maley
- Arizona Cancer Evolution Center, Arizona State University, Tempe, AZ 85287, United States of America; Biodesign Center for Biocomputation, Security and Society, Arizona State University, Tempe, AZ 85287, United States of America
| | - Melanie Moses
- Department of Computer Science, University of New Mexico, Albuquerque, NM 87131, United States of America; Department of Biology, University of New Mexico, Albuquerque, NM 87131, United States of America; Santa Fe Institute, Santa Fe, NM 87501, United States of America
| | - Alan S Perelson
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545, United States of America; Santa Fe Institute, Santa Fe, NM 87501, United States of America
| | - Charles Perrings
- School of Life Sciences, Arizona State University, Tempe, AZ 85287, United States of America
| | - Joshua Plotkin
- Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Jennifer Rexford
- Department of Computer Science, Princeton University, Princeton, NJ 08540, United States of America
| | - Mohit Tiwari
- Department of Electrical and Computer Engineering, University of Texas, Austin, TX 78712, United States of America
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9
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Park SW, Sun K, Abbott S, Sender R, Bar-On YM, Weitz JS, Funk S, Grenfell BT, Backer JA, Wallinga J, Viboud C, Dushoff J. Inferring the differences in incubation-period and generation-interval distributions of the Delta and Omicron variants of SARS-CoV-2. Proc Natl Acad Sci U S A 2023; 120:e2221887120. [PMID: 37216529 DOI: 10.1073/pnas.2221887120] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 03/20/2023] [Indexed: 05/24/2023] Open
Abstract
Estimating the differences in the incubation-period, serial-interval, and generation-interval distributions of SARS-CoV-2 variants is critical to understanding their transmission. However, the impact of epidemic dynamics is often neglected in estimating the timing of infection-for example, when an epidemic is growing exponentially, a cohort of infected individuals who developed symptoms at the same time are more likely to have been infected recently. Here, we reanalyze incubation-period and serial-interval data describing transmissions of the Delta and Omicron variants from the Netherlands at the end of December 2021. Previous analysis of the same dataset reported shorter mean observed incubation period (3.2 d vs. 4.4 d) and serial interval (3.5 d vs. 4.1 d) for the Omicron variant, but the number of infections caused by the Delta variant decreased during this period as the number of Omicron infections increased. When we account for growth-rate differences of two variants during the study period, we estimate similar mean incubation periods (3.8 to 4.5 d) for both variants but a shorter mean generation interval for the Omicron variant (3.0 d; 95% CI: 2.7 to 3.2 d) than for the Delta variant (3.8 d; 95% CI: 3.7 to 4.0 d). The differences in estimated generation intervals may be driven by the "network effect"-higher effective transmissibility of the Omicron variant can cause faster susceptible depletion among contact networks, which in turn prevents late transmission (therefore shortening realized generation intervals). Using up-to-date generation-interval distributions is critical to accurately estimating the reproduction advantage of the Omicron variant.
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Affiliation(s)
- Sang Woo Park
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544
| | - Kaiyuan Sun
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD 20892
| | - Sam Abbott
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | - Ron Sender
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Yinon M Bar-On
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Joshua S Weitz
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332
- School of Physics, Georgia Institute of Technology, Atlanta, GA 30332
- Institut de Biologie, École Normale Supérieure, Paris 75005, France
| | - Sebastian Funk
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544
- Princeton School of Public and International Affairs, Princeton University, Princeton, NJ 08542
| | - Jantien A Backer
- Center for Infectious Disease Control, National Institute for Public Health and the Environment, 3720 Bilthoven, The Netherlands
| | - Jacco Wallinga
- Center for Infectious Disease Control, National Institute for Public Health and the Environment, 3720 Bilthoven, The Netherlands
- Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 Leiden, The Netherlands
| | - Cecile Viboud
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD 20892
| | - Jonathan Dushoff
- Department of Biology, McMaster University, Hamilton, L8S 4L8 ON, Canada
- Department of Mathematics and Statistics, McMaster University, Hamilton, L8S 4L8 ON, Canada
- M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, L8S 4L8 ON, Canada
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10
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Wang Q, Wang W, Winter AK, Zhan Z, Ajelli M, Trentini F, Wang L, Li F, Yang J, Xiang X, Liao Q, Zhou J, Guo J, Yan X, Liu N, Metcalf CJE, Grenfell BT, Yu H. Publisher Correction: Long-term measles antibody profiles following different vaccine schedules in China, a longitudinal study. Nat Commun 2023; 14:2458. [PMID: 37117162 PMCID: PMC10147589 DOI: 10.1038/s41467-023-38167-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/30/2023] Open
Affiliation(s)
- Qianli Wang
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China
| | - Wei Wang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Amy K Winter
- Department of Epidemiology and Biostatistics, University of Georgia, Athens, GA, USA
| | - Zhifei Zhan
- Hunan Provincial Center for Disease Control and Prevention, Changsha, China
| | - Marco Ajelli
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Filippo Trentini
- Dondena Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milan, Italy
| | - Lili Wang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Fangcai Li
- Hunan Provincial Center for Disease Control and Prevention, Changsha, China
| | - Juan Yang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Xingyu Xiang
- Hunan Provincial Center for Disease Control and Prevention, Changsha, China
| | - Qiaohong Liao
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Jiaxin Zhou
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Jinxin Guo
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Xuemei Yan
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Nuolan Liu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Hongjie Yu
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China.
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China.
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11
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Park SW, Dushoff J, Grenfell BT, Weitz JS. Intermediate levels of asymptomatic transmission can lead to the highest epidemic fatalities. PNAS Nexus 2023; 2:pgad106. [PMID: 37091542 PMCID: PMC10118396 DOI: 10.1093/pnasnexus/pgad106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 12/02/2022] [Accepted: 03/13/2023] [Indexed: 04/25/2023]
Abstract
Asymptomatic infections have hampered the ability to characterize and prevent the transmission of SARS-CoV-2 throughout the pandemic. Although asymptomatic infections reduce severity at the individual level, they can make population-level outcomes worse if asymptomatic individuals-unaware they are infected-transmit more than symptomatic individuals. Using an epidemic model, we show that intermediate levels of asymptomatic infection lead to the highest levels of epidemic fatalities when the decrease in symptomatic transmission, due either to individual behavior or mitigation efforts, is strong. We generalize this result to include presymptomatic transmission, showing that intermediate levels of nonsymptomatic transmission lead to the highest levels of fatalities. Finally, we extend our framework to illustrate how the intersection of asymptomatic spread and immunity profiles determine epidemic trajectories, including population-level severity, of future variants. In particular, when immunity provides protection against symptoms, but not against infections or deaths, epidemic trajectories can have faster growth rates and higher peaks, leading to more total deaths. Conversely, even modest levels of protection against infection can mitigate the population-level effects of asymptomatic spread.
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Affiliation(s)
- Sang Woo Park
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Jonathan Dushoff
- Department of Biology, McMaster University, Hamilton, ON, Canada
- Department of Mathematics and Statistics, McMaster University, Hamilton, ON, Canada
- M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON, Canada
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
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12
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Wang Q, Wang W, Winter AK, Zhan Z, Ajelli M, Trentini F, Wang L, Li F, Yang J, Xiang X, Liao Q, Zhou J, Guo J, Yan X, Liu N, Metcalf CJE, Grenfell BT, Yu H. Long-term measles antibody profiles following different vaccine schedules in China, a longitudinal study. Nat Commun 2023; 14:1746. [PMID: 36990986 PMCID: PMC10054217 DOI: 10.1038/s41467-023-37407-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 03/13/2023] [Indexed: 03/31/2023] Open
Abstract
Characterizing the long-term kinetics of maternally derived and vaccine-induced measles immunity is critical for informing measles immunization strategies moving forward. Based on two prospective cohorts of children in China, we estimate that maternally derived immunity against measles persists for 2.4 months. Following two-dose series of measles-containing vaccine (MCV) at 8 and 18 months of age, the immune protection against measles is not lifelong, and antibody concentrations are extrapolated to fall below the protective threshold of 200 mIU/ml at 14.3 years. A catch-up MCV dose in addition to the routine doses between 8 months and 5 years reduce the cumulative incidence of seroreversion by 79.3-88.7% by the age of 6 years. Our findings also support a good immune response after the first MCV vaccination at 8 months. These findings, coupled with the effectiveness of a catch-up dose in addition to the routine doses, could be instrumental to relevant stakeholders when planning routine immunization schedules and supplemental immunization activities.
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Affiliation(s)
- Qianli Wang
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China
| | - Wei Wang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Amy K Winter
- Department of Epidemiology and Biostatistics, University of Georgia, Athens, GA, USA
| | - Zhifei Zhan
- Hunan Provincial Center for Disease Control and Prevention, Changsha, China
| | - Marco Ajelli
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Filippo Trentini
- Dondena Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milan, Italy
| | - Lili Wang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Fangcai Li
- Hunan Provincial Center for Disease Control and Prevention, Changsha, China
| | - Juan Yang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Xingyu Xiang
- Hunan Provincial Center for Disease Control and Prevention, Changsha, China
| | - Qiaohong Liao
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Jiaxin Zhou
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Jinxin Guo
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Xuemei Yan
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Nuolan Liu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Hongjie Yu
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China.
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China.
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13
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Lee WE, Woo Park S, Weinberger DM, Olson D, Simonsen L, Grenfell BT, Viboud C. Direct and indirect mortality impacts of the COVID-19 pandemic in the United States, March 1, 2020 to January 1, 2022. eLife 2023; 12:77562. [PMID: 36811598 PMCID: PMC9946455 DOI: 10.7554/elife.77562] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 01/15/2023] [Indexed: 02/24/2023] Open
Abstract
Excess mortality studies provide crucial information regarding the health burden of pandemics and other large-scale events. Here, we use time series approaches to separate the direct contribution of SARS-CoV-2 infection on mortality from the indirect consequences of the pandemic in the United States. We estimate excess deaths occurring above a seasonal baseline from March 1, 2020 to January 1, 2022, stratified by week, state, age, and underlying mortality condition (including COVID-19 and respiratory diseases; Alzheimer's disease; cancer; cerebrovascular diseases; diabetes; heart diseases; and external causes, which include suicides, opioid overdoses, and accidents). Over the study period, we estimate an excess of 1,065,200 (95% Confidence Interval (CI) 909,800-1,218,000) all-cause deaths, of which 80% are reflected in official COVID-19 statistics. State-specific excess death estimates are highly correlated with SARS-CoV-2 serology, lending support to our approach. Mortality from 7 of the 8 studied conditions rose during the pandemic, with the exception of cancer. To separate the direct mortality consequences of SARS-CoV-2 infection from the indirect effects of the pandemic, we fit generalized additive models (GAM) to age- state- and cause-specific weekly excess mortality, using covariates representing direct (COVID-19 intensity) and indirect pandemic effects (hospital intensive care unit (ICU) occupancy and measures of interventions stringency). We find that 84% (95% CI 65-94%) of all-cause excess mortality can be statistically attributed to the direct impact of SARS-CoV-2 infection. We also estimate a large direct contribution of SARS-CoV-2 infection (≥67%) on mortality from diabetes, Alzheimer's, heart diseases, and in all-cause mortality among individuals over 65 years. In contrast, indirect effects predominate in mortality from external causes and all-cause mortality among individuals under 44 years, with periods of stricter interventions associated with greater rises in mortality. Overall, on a national scale, the largest consequences of the COVID-19 pandemic are attributable to the direct impact of SARS-CoV-2 infections; yet, the secondary impacts dominate among younger age groups and in mortality from external causes. Further research on the drivers of indirect mortality is warranted as more detailed mortality data from this pandemic becomes available.
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Affiliation(s)
- Wha-Eum Lee
- Department of Ecology and Evolutionary Biology, Princeton UniversityPrincetonUnited States
| | - Sang Woo Park
- Department of Ecology and Evolutionary Biology, Princeton UniversityPrincetonUnited States
| | | | - Donald Olson
- New York City Department of Health and Mental HygieneNew YorkUnited States
| | - Lone Simonsen
- Department of Science and Environment, Roskilde UniversityRoskildeDenmark
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton UniversityPrincetonUnited States
- Princeton School of Public Affairs, Princeton UniversityPrincetonUnited States
| | - Cécile Viboud
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of HealthBethesdaUnited States
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14
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Casalegno JS, Bents S, Paget J, Gillet Y, Ploin D, Javouhey E, Lina B, Morfin F, Grenfell BT, Baker RE. Application of a forecasting model to mitigate the consequences of unexpected RSV surge: Experience from the post-COVID-19 2021/22 winter season in a major metropolitan centre, Lyon, France. J Glob Health 2023; 13:04007. [PMID: 36757127 PMCID: PMC9893715 DOI: 10.7189/jogh.13.04007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Background The emergence of COVID-19 triggered the massive implementation of non-pharmaceutical interventions (NPI) which impacted the circulation of respiratory syncytial virus (RSV) during the 2020/2021 season. Methods A time-series susceptible-infected-recovered (TSIR) model was used early September 2021 to forecast the implications of this disruption on the future 2021/2022 RSV epidemic in Lyon urban population. Results When compared to observed hospital-confirmed cases, the model successfully captured the early start, peak timing, and end of the 2021/2022 RSV epidemic. These simulations, added to other streams of surveillance data, shared and discussed among the local field experts were of great value to mitigate the consequences of this atypical RSV outbreak on our hospital paediatric department. Conclusions TSIR model, fitted to local hospital data covering large urban areas, can produce plausible post-COVID-19 RSV simulations. Collaborations between modellers and hospital management (who are both model users and data providers) should be encouraged in order to validate the use of dynamical models to timely allocate hospital resources to the future RSV epidemics.
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Affiliation(s)
- Jean-Sebastien Casalegno
- Hospices Civils de Lyon, Lyon, France,Centre International de Recherche en Infectiologie (CIRI), Lyon, France,Université Claude Bernard Lyon 1, Lyon, France
| | | | - John Paget
- Netherlands Institute for Health Services Research (Nivel), Utrecht, the Netherlands
| | | | - Dominique Ploin
- Hospices Civils de Lyon, Lyon, France,Centre International de Recherche en Infectiologie (CIRI), Lyon, France,Université Claude Bernard Lyon 1, Lyon, France
| | - Etienne Javouhey
- Hospices Civils de Lyon, Lyon, France,Université Claude Bernard Lyon 1, Lyon, France
| | - Bruno Lina
- Hospices Civils de Lyon, Lyon, France,Centre International de Recherche en Infectiologie (CIRI), Lyon, France,Université Claude Bernard Lyon 1, Lyon, France
| | - Florence Morfin
- Hospices Civils de Lyon, Lyon, France,Centre International de Recherche en Infectiologie (CIRI), Lyon, France,Université Claude Bernard Lyon 1, Lyon, France
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15
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Nielsen BF, Saad-Roy CM, Li Y, Sneppen K, Simonsen L, Viboud C, Levin SA, Grenfell BT. Host heterogeneity and epistasis explain punctuated evolution of SARS-CoV-2. PLoS Comput Biol 2023; 19:e1010896. [PMID: 36791146 PMCID: PMC9974118 DOI: 10.1371/journal.pcbi.1010896] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 02/28/2023] [Accepted: 01/25/2023] [Indexed: 02/16/2023] Open
Abstract
Identifying drivers of viral diversity is key to understanding the evolutionary as well as epidemiological dynamics of the COVID-19 pandemic. Using rich viral genomic data sets, we show that periods of steadily rising diversity have been punctuated by sudden, enormous increases followed by similarly abrupt collapses of diversity. We introduce a mechanistic model of saltational evolution with epistasis and demonstrate that these features parsimoniously account for the observed temporal dynamics of inter-genomic diversity. Our results provide support for recent proposals that saltational evolution may be a signature feature of SARS-CoV-2, allowing the pathogen to more readily evolve highly transmissible variants. These findings lend theoretical support to a heightened awareness of biological contexts where increased diversification may occur. They also underline the power of pathogen genomics and other surveillance streams in clarifying the phylodynamics of emerging and endemic infections. In public health terms, our results further underline the importance of equitable distribution of up-to-date vaccines.
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Affiliation(s)
- Bjarke Frost Nielsen
- Department of Science and Environment, Roskilde University, Roskilde, Denmark
- Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
- * E-mail:
| | - Chadi M. Saad-Roy
- Department of Integrative Biology, University of California, Berkeley, California, United States of America
- Miller Institute for Basic Research in Science, University of California, Berkeley, California, United States of America
| | - Yimei Li
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
| | - Kim Sneppen
- Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
| | - Lone Simonsen
- Department of Science and Environment, Roskilde University, Roskilde, Denmark
| | - Cécile Viboud
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Simon A. Levin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
| | - Bryan T. Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
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16
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Xia S, Gullickson CC, Metcalf CJE, Grenfell BT, Mina MJ. Assessing the Effects of Measles Virus Infections on Childhood Infectious Disease Mortality in Brazil. J Infect Dis 2022; 227:133-140. [PMID: 35767276 PMCID: PMC10205611 DOI: 10.1093/infdis/jiac233] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 05/29/2022] [Accepted: 06/26/2022] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Measles virus infection induces acute immunosuppression for weeks following infection, and also impairs preexisting immunological memory, resulting in "immune amnesia" that can last for years. Both mechanisms predispose the host to severe outcomes of subsequent infections. Therefore, measles dynamics could potentially affect the epidemiology of other infectious diseases. METHODS To examine this hypothesis, we analyzed the annual mortality rates of children aged 1-9 years in Brazil from 1980 to 1995. We calculated the correlation between nonmeasles infectious disease mortality rates and measles mortality rates using linear and negative-binomial models, with 3 methods to control the confounding effects of time. We also estimated the duration of measles-induced immunomodulation. RESULTS The mortality rates of nonmeasles infectious diseases and measles virus infection were highly correlated. This positive correlation remained significant after removing the time trends. We found no evidence of long-term measles immunomodulation beyond 1 year. CONCLUSIONS These results support that measles virus infection could increase the mortality of other infectious diseases. The short lag identified for measles effects (<1 year) implies that acute immunosuppression was potentially driving this effect in Brazil. Overall, our study indicates disproportionate contributions of measles to childhood infectious disease mortality, highlighting the importance of measles vaccination.
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Affiliation(s)
- Siyang Xia
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Cricket C Gullickson
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, USA
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, USA
- Princeton School of Public and International Affairs, Princeton University, Princeton, New Jersey, USA
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, USA
- Princeton School of Public and International Affairs, Princeton University, Princeton, New Jersey, USA
| | - Michael J Mina
- Department of Pathology at Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
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17
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Lizewski RA, Sealfon RSG, Park SW, Smith GR, Porter CK, Gonzalez-Reiche AS, Ge Y, Miller CM, Goforth CW, Pincas H, Termini MS, Ramos I, Nair VD, Lizewski SE, Alshammary H, Cer RZ, Chen HW, George MC, Arnold CE, Glang LA, Long KA, Malagon F, Marayag JJ, Nunez E, Rice GK, Santa Ana E, Schilling MA, Smith DR, Sugiharto VA, Sun P, van de Guchte A, Khan Z, Dutta J, Vangeti S, Voegtly LJ, Weir DL, Metcalf CJE, Troyanskaya OG, Bishop-Lilly KA, Grenfell BT, van Bakel H, Letizia AG, Sealfon SC. SARS-CoV-2 Outbreak Dynamics in an Isolated US Military Recruit Training Center With Rigorous Prevention Measures. Epidemiology 2022; 33:797-807. [PMID: 35944149 PMCID: PMC9531985 DOI: 10.1097/ede.0000000000001523] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 07/11/2022] [Indexed: 02/05/2023]
Abstract
BACKGROUND Marine recruits training at Parris Island experienced an unexpectedly high rate of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, despite preventive measures including a supervised, 2-week, pre-entry quarantine. We characterize SARS-CoV-2 transmission in this cohort. METHODS Between May and November 2020, we monitored 2,469 unvaccinated, mostly male, Marine recruits prospectively during basic training. If participants tested negative for SARS-CoV-2 by quantitative polymerase chain reaction (qPCR) at the end of quarantine, they were transferred to the training site in segregated companies and underwent biweekly testing for 6 weeks. We assessed the effects of coronavirus disease 2019 (COVID-19) prevention measures on other respiratory infections with passive surveillance data, performed phylogenetic analysis, and modeled transmission dynamics and testing regimens. RESULTS Preventive measures were associated with drastically lower rates of other respiratory illnesses. However, among the trainees, 1,107 (44.8%) tested SARS-CoV-2-positive, with either mild or no symptoms. Phylogenetic analysis of viral genomes from 580 participants revealed that all cases but one were linked to five independent introductions, each characterized by accumulation of mutations across and within companies, and similar viral isolates in individuals from the same company. Variation in company transmission rates (mean reproduction number R 0 ; 5.5 [95% confidence interval [CI], 5.0, 6.1]) could be accounted for by multiple initial cases within a company and superspreader events. Simulations indicate that frequent rapid-report testing with case isolation may minimize outbreaks. CONCLUSIONS Transmission of wild-type SARS-CoV-2 among Marine recruits was approximately twice that seen in the community. Insights from SARS-CoV-2 outbreak dynamics and mutations spread in a remote, congregate setting may inform effective mitigation strategies.
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Affiliation(s)
| | - Rachel S. G. Sealfon
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY
| | - Sang Woo Park
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ
| | - Gregory R. Smith
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY
| | | | - Ana S. Gonzalez-Reiche
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Yongchao Ge
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Clare M. Miller
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY
| | | | - Hanna Pincas
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY
| | | | - Irene Ramos
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Venugopalan D. Nair
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY
| | | | - Hala Alshammary
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Regina Z. Cer
- Genomics & Bioinformatics Department, Biological Defense Research Directorate, Naval Medical Research Center-Frederick, Fort Detrick, MD
| | - Hua Wei Chen
- Naval Medical Research Center, Silver Spring, MD
- Henry M Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD
| | | | - Catherine E. Arnold
- Genomics & Bioinformatics Department, Biological Defense Research Directorate, Naval Medical Research Center-Frederick, Fort Detrick, MD
- Defense Threat Reduction Agency, Fort Belvoir, VA
| | - Lindsay A. Glang
- Genomics & Bioinformatics Department, Biological Defense Research Directorate, Naval Medical Research Center-Frederick, Fort Detrick, MD
- Leidos, Reston, VA
| | - Kyle A. Long
- Genomics & Bioinformatics Department, Biological Defense Research Directorate, Naval Medical Research Center-Frederick, Fort Detrick, MD
- Leidos, Reston, VA
| | - Francisco Malagon
- Genomics & Bioinformatics Department, Biological Defense Research Directorate, Naval Medical Research Center-Frederick, Fort Detrick, MD
- Leidos, Reston, VA
| | | | - Edgar Nunez
- Naval Medical Research Center, Silver Spring, MD
| | - Gregory K. Rice
- Genomics & Bioinformatics Department, Biological Defense Research Directorate, Naval Medical Research Center-Frederick, Fort Detrick, MD
- Leidos, Reston, VA
| | | | | | - Darci R. Smith
- Immunodiagnostics Department, Biological Defense Research Directorate, Naval Medical Research Center-Frederick, Fort Detrick, MD
| | - Victor A. Sugiharto
- Naval Medical Research Center, Silver Spring, MD
- Henry M Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD
| | - Peifang Sun
- Naval Medical Research Center, Silver Spring, MD
| | - Adriana van de Guchte
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Zenab Khan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Jayeeta Dutta
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Sindhu Vangeti
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Logan J. Voegtly
- Genomics & Bioinformatics Department, Biological Defense Research Directorate, Naval Medical Research Center-Frederick, Fort Detrick, MD
- Leidos, Reston, VA
| | - Dawn L. Weir
- Naval Medical Research Center, Silver Spring, MD
| | | | - Olga G. Troyanskaya
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY
- Department of Computer Science, Princeton University, Princeton, NJ
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ
| | - Kimberly A. Bishop-Lilly
- Genomics & Bioinformatics Department, Biological Defense Research Directorate, Naval Medical Research Center-Frederick, Fort Detrick, MD
| | - Bryan T. Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ
| | - Harm van Bakel
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY
| | | | - Stuart C. Sealfon
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY
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18
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Lau MSY, Becker A, Madden W, Waller LA, Metcalf CJE, Grenfell BT. Comparing and linking machine learning and semi-mechanistic models for the predictability of endemic measles dynamics. PLoS Comput Biol 2022; 18:e1010251. [PMID: 36074763 PMCID: PMC9455846 DOI: 10.1371/journal.pcbi.1010251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 08/02/2022] [Indexed: 11/29/2022] Open
Abstract
Measles is one the best-documented and most-mechanistically-studied non-linear infectious disease dynamical systems. However, systematic investigation into the comparative performance of traditional mechanistic models and machine learning approaches in forecasting the transmission dynamics of this pathogen are still rare. Here, we compare one of the most widely used semi-mechanistic models for measles (TSIR) with a commonly used machine learning approach (LASSO), comparing performance and limits in predicting short to long term outbreak trajectories and seasonality for both regular and less regular measles outbreaks in England and Wales (E&W) and the United States. First, our results indicate that the proposed LASSO model can efficiently use data from multiple major cities and achieve similar short-to-medium term forecasting performance to semi-mechanistic models for E&W epidemics. Second, interestingly, the LASSO model also captures annual to biennial bifurcation of measles epidemics in E&W caused by susceptible response to the late 1940s baby boom. LASSO may also outperform TSIR for predicting less-regular dynamics such as those observed in major cities in US between 1932–45. Although both approaches capture short-term forecasts, accuracy suffers for both methods as we attempt longer-term predictions in highly irregular, post-vaccination outbreaks in E&W. Finally, we illustrate that the LASSO model can both qualitatively and quantitatively reconstruct mechanistic assumptions, notably susceptible dynamics, in the TSIR model. Our results characterize the limits of predictability of infectious disease dynamics for strongly immunizing pathogens with both mechanistic and machine learning models, and identify connections between these two approaches. Machine learning techniques in infectious disease modeling have grown in popularity in recent years. However, systematic investigation into the comparative performance of these approaches with traditional mechanistic models are still rare. In this paper, we compare one of the most widely used semi-mechanistic models for measles (TSIR) with a commonly used machine learning approach (LASSO), comparing performance and limits in predicting short to long term outbreaks of measles, one of the best-documented and most-mechanistically-studied non-linear infectious disease dynamical systems. Our results show that in general the LASSO outperform TSIR for predicting less-regular dynamics, and it can achieve similar performance in other scenarios when compared to the TSIR. The LASSO also has the advantages of not requiring explicit demographic data in model training. Finally, we identify connections between these two approaches and show that the LASSO model can both qualitatively and quantitatively reconstruct mechanistic assumptions, notably susceptible dynamics, in the TSIR model.
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Affiliation(s)
- Max S. Y. Lau
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, United States of America
- * E-mail:
| | - Alex Becker
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, United States of America
| | - Wyatt Madden
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, United States of America
| | - Lance A. Waller
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, United States of America
| | - C. Jessica E. Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, United States of America
| | - Bryan T. Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, United States of America
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19
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Nielsen BF, Li Y, Sneppen K, Simonsen L, Viboud C, Levin SA, Grenfell BT. Immune Heterogeneity and Epistasis Explain Punctuated Evolution of SARS-CoV-2. medRxiv 2022:2022.07.27.22278129. [PMID: 35982659 PMCID: PMC9387145 DOI: 10.1101/2022.07.27.22278129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Identifying drivers of viral diversity is key to understanding the evolutionary as well as epidemiological dynamics of the COVID-19 pandemic. Using rich viral genomic data sets, we show that periods of steadily rising diversity have been punctuated by sudden, enormous increases followed by similarly abrupt collapses of diversity. We introduce a mechanistic model of saltational evolution with epistasis and demonstrate that these features parsimoniously account for the observed temporal dynamics of inter-genomic diversity. Our results provide support for recent proposals that saltational evolution may be a signature feature of SARS-CoV-2, allowing the pathogen to more readily evolve highly transmissible variants. These findings lend theoretical support to a heightened awareness of biological contexts where increased diversification may occur. They also underline the power of pathogen genomics and other surveillance streams in clarifying the phylodynamics of emerging and endemic infections. In public health terms, our results further underline the importance of equitable distribution of up-to-date vaccines.
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Affiliation(s)
- Bjarke Frost Nielsen
- PandemiX Center, Roskilde University
- Niels Bohr Institute, University of Copenhagen
| | - Yimei Li
- Department of Ecology & Evolutionary Biology, Princeton University
| | - Kim Sneppen
- Niels Bohr Institute, University of Copenhagen
| | | | - Cécile Viboud
- Fogarty International Center, National Institutes of Health
| | - Simon A. Levin
- Department of Ecology & Evolutionary Biology, Princeton University
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20
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Abstract
Understanding viral evolution depends on a synthesis of evolutionary biology and immuno-epidemiology.
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Affiliation(s)
- Chadi M Saad-Roy
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.,Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.,Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
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21
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Park SW, Bolker BM, Funk S, Metcalf CJE, Weitz JS, Grenfell BT, Dushoff J. The importance of the generation interval in investigating dynamics and control of new SARS-CoV-2 variants. J R Soc Interface 2022; 19:20220173. [PMID: 35702867 PMCID: PMC9198506 DOI: 10.1098/rsif.2022.0173] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Inferring the relative strength (i.e. the ratio of reproduction numbers) and relative speed (i.e. the difference between growth rates) of new SARS-CoV-2 variants is critical to predicting and controlling the course of the current pandemic. Analyses of new variants have primarily focused on characterizing changes in the proportion of new variants, implicitly or explicitly assuming that the relative speed remains fixed over the course of an invasion. We use a generation-interval-based framework to challenge this assumption and illustrate how relative strength and speed change over time under two idealized interventions: a constant-strength intervention like idealized vaccination or social distancing, which reduces transmission rates by a constant proportion, and a constant-speed intervention like idealized contact tracing, which isolates infected individuals at a constant rate. In general, constant-strength interventions change the relative speed of a new variant, while constant-speed interventions change its relative strength. Differences in the generation-interval distributions between variants can exaggerate these changes and modify the effectiveness of interventions. Finally, neglecting differences in generation-interval distributions can bias estimates of relative strength.
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Affiliation(s)
- Sang Woo Park
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Benjamin M Bolker
- Department of Biology, McMaster University, Hamilton, Ontario, Canada.,Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada.,M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
| | - Sebastian Funk
- Department for Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.,Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.,Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Joshua S Weitz
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.,School of Physics, Georgia Institute of Technology, Atlanta, GA, USA.,Institut de Biologie, École Normale Supérieure, Paris, France
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.,Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Jonathan Dushoff
- Department of Biology, McMaster University, Hamilton, Ontario, Canada.,Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada.,M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
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22
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Lee WE, Park SW, Weinberger DM, Olson D, Simonsen L, Grenfell BT, Viboud C. Direct and indirect mortality impacts of the COVID-19 pandemic in the US, March 2020-April 2021. medRxiv 2022:2022.02.10.22270721. [PMID: 35194617 PMCID: PMC8863161 DOI: 10.1101/2022.02.10.22270721] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Excess mortality studies provide crucial information regarding the health burden of pandemics and other large-scale events. Here, we used time series approaches to separate the direct contribution of SARS-CoV-2 infections on mortality from the indirect consequences of pandemic interventions and behavior changes in the United States. We estimated deaths occurring in excess of seasonal baselines stratified by state, age, week and cause (all causes, COVID-19 and respiratory diseases, Alzheimer's disease, cancer, cerebrovascular disease, diabetes, heart disease, and external causes, including suicides, opioids, accidents) from March 1, 2020 to April 30, 2021. Our estimates of COVID-19 excess deaths were highly correlated with SARS-CoV-2 serology, lending support to our approach. Over the study period, we estimate an excess of 666,000 (95% Confidence Interval (CI) 556000, 774000) all-cause deaths, of which 90% could be attributed to the direct impact of SARS-CoV-2 infection, and 78% were reflected in official COVID-19 statistics. Mortality from all disease conditions rose during the pandemic, except for cancer. The largest direct impacts of the pandemic were seen in mortality from diabetes, Alzheimer's, and heart diseases, and in age groups over 65 years. In contrast, the largest indirect consequences of the pandemic were seen in deaths from external causes, which increased by 45,300 (95% CI 30,800, 59,500) and were statistically linked to the intensity of non-pharmaceutical interventions. Within this category, increases were most pronounced in mortality from accidents and injuries, drug overdoses, and assaults and homicides, while the rate of death from suicides remained stable. Younger age groups suffered the brunt of these indirect effects. Overall, on a national scale, the largest consequences of the COVID-19 pandemic are attributable to the direct impact of SARS-CoV-2 infections; yet, the secondary impacts dominate among younger age groups, in periods of stricter interventions, and in mortality from external causes. Further research on the drivers of indirect mortality is warranted to optimize interventions in future pandemics.
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Affiliation(s)
- Wha-Eum Lee
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, USA, 08544
| | - Sang Woo Park
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, USA, 08544
| | | | - Donald Olson
- Department of Health and Mental Hygiene, New York, New York
| | - Lone Simonsen
- Department of Science and Environment, Roskilde University, Denmark
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, USA, 08544
- Princeton School of Public Affairs, Princeton University, Princeton, USA, 08544
| | - Cécile Viboud
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA. 20892
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23
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Rice BL, Douek DC, McDermott AB, Grenfell BT, Metcalf CJE. Why are there so few (or so many) circulating coronaviruses? Trends Immunol 2021; 42:751-763. [PMID: 34366247 PMCID: PMC8272969 DOI: 10.1016/j.it.2021.07.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 07/05/2021] [Accepted: 07/06/2021] [Indexed: 12/11/2022]
Abstract
Despite vast diversity in non-human hosts and conspicuous recent spillover events, only a small number of coronaviruses have been observed to persist in human populations. This puzzling mismatch suggests substantial barriers to establishment. We detail hypotheses that might contribute to explain the low numbers of endemic coronaviruses, despite their considerable evolutionary and emergence potential. We assess possible explanations ranging from issues of ascertainment, historically lower opportunities for spillover, aspects of human demographic changes, and features of pathogen biology and pre-existing adaptive immunity to related viruses. We describe how successful emergent viral species must triangulate transmission, virulence, and host immunity to maintain circulation. Characterizing the factors that might shape the limits of viral persistence can delineate promising research directions to better understand the combinations of pathogens and contexts that are most likely to lead to spillover.
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Affiliation(s)
- Benjamin L Rice
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.
| | - Daniel C Douek
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Adrian B McDermott
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
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24
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Wagner CE, Saad-Roy CM, Morris SE, Baker RE, Mina MJ, Farrar J, Holmes EC, Pybus OG, Graham AL, Emanuel EJ, Levin SA, Metcalf CJE, Grenfell BT. Vaccine nationalism and the dynamics and control of SARS-CoV-2. Science 2021; 373:eabj7364. [PMID: 34404735 PMCID: PMC9835930 DOI: 10.1126/science.abj7364] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Vaccines provide powerful tools to mitigate the enormous public health and economic costs that the ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic continues to exert globally, yet vaccine distribution remains unequal among countries. To examine the potential epidemiological and evolutionary impacts of “vaccine nationalism,” we extend previous models to include simple scenarios of stockpiling between two regions. In general, when vaccines are widely available and the immunity they confer is robust, sharing doses minimizes total cases across regions. A number of subtleties arise when the populations and transmission rates in each region differ, depending on evolutionary assumptions and vaccine availability. When the waning of natural immunity contributes most to evolutionary potential, sustained transmission in low-access regions results in an increased potential for antigenic evolution, which may result in the emergence of novel variants that affect epidemiological characteristics globally. Overall, our results stress the importance of rapid, equitable vaccine distribution for global control of the pandemic.
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Affiliation(s)
- Caroline E. Wagner
- Department of Bioengineering, McGill University, Montreal, QC H3A 0C3, Canada.,Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.,Corresponding author: (C.E.W.); (C.M.S.-R.); (B.T.G.)
| | - Chadi M. Saad-Roy
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA.,Princeton High Meadows Environmental Institute, Princeton University, Princeton, NJ 08540, USA.,Corresponding author: (C.E.W.); (C.M.S.-R.); (B.T.G.)
| | - Sinead E. Morris
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY 10032, USA
| | - Rachel E. Baker
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08540, USA.,Princeton High Meadows Environmental Institute, Princeton University, Princeton, NJ 08540, USA
| | - Michael J. Mina
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.,Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Jeremy Farrar
- Department of Bioengineering, McGill University, Montreal, QC H3A 0C3, Canada.,The Wellcome Trust, London, UK
| | - Edward C. Holmes
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08540, USA.,Marie Bashir Institute for Infectious Diseases and Biosecurity, The University of Sydney, Sydney, NSW, Australia.,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW, Australia.,School of Medical Sciences, The University of Sydney, Sydney, NSW, Australia
| | - Oliver G. Pybus
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY 10032, USA.,Department of Zoology, University of Oxford, Oxford, UK
| | - Andrea L. Graham
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08540, USA.,Princeton High Meadows Environmental Institute, Princeton University, Princeton, NJ 08540, USA
| | - Ezekiel J. Emanuel
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.,Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Simon A. Levin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08540, USA.,Princeton High Meadows Environmental Institute, Princeton University, Princeton, NJ 08540, USA
| | - C. Jessica E. Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08540, USA.,Princeton High Meadows Environmental Institute, Princeton University, Princeton, NJ 08540, USA.,Princeton School of Public and International Affairs, Princeton University, Princeton, NJ 08540, USA
| | - Bryan T. Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08540, USA.,Princeton High Meadows Environmental Institute, Princeton University, Princeton, NJ 08540, USA.,Princeton School of Public and International Affairs, Princeton University, Princeton, NJ 08540, USA.,Corresponding author: (C.E.W.); (C.M.S.-R.); (B.T.G.)
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25
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Giles JR, Cummings DAT, Grenfell BT, Tatem AJ, zu Erbach-Schoenberg E, Metcalf CJE, Wesolowski A. Trip duration drives shift in travel network structure with implications for the predictability of spatial disease spread. PLoS Comput Biol 2021; 17:e1009127. [PMID: 34375331 PMCID: PMC8378725 DOI: 10.1371/journal.pcbi.1009127] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 08/20/2021] [Accepted: 05/28/2021] [Indexed: 11/19/2022] Open
Abstract
Human travel is one of the primary drivers of infectious disease spread. Models of travel are often used that assume the amount of travel to a specific destination decreases as cost of travel increases with higher travel volumes to more populated destinations. Trip duration, the length of time spent in a destination, can also impact travel patterns. We investigated the spatial patterns of travel conditioned on trip duration and find distinct differences between short and long duration trips. In short-trip duration travel networks, trips are skewed towards urban destinations, compared with long-trip duration networks where travel is more evenly spread among locations. Using gravity models to inform connectivity patterns in simulations of disease transmission, we show that pathogens with shorter generation times exhibit initial patterns of spatial propagation that are more predictable among urban locations. Further, pathogens with a longer generation time have more diffusive patterns of spatial spread reflecting more unpredictable disease dynamics. During an epidemic of an infectious pathogen, cases of disease can be imported to new locations when people travel. The amount of time that an infected person spends in a destination (trip duration) determines how likely they are to infect others while travelling. In this study, we analyzed travel data and found specific spatial patterns in trip duration, where short-duration trips are more common between urban destinations and long-duration trips are evenly spread out among locations. To show how this spatial pattern impacts the spread of infectious diseases, we used data-driven models and simulations to show that pathogens with shorter generation times have patterns of spatial spread that are more predictable among urban locations. However, pathogens with longer generation times tend to spread along the long-duration travel networks that are more evenly distributed among locations giving them more unpredictable disease dynamics.
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Affiliation(s)
- John R. Giles
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
- * E-mail:
| | - Derek A. T. Cummings
- Department of Biology and the Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America
| | - Bryan T. Grenfell
- Department of Ecology and Evolutionary Biology and the Princeton School of Public and International Affairs, Princeton University, Princeton, New Jersey, United States of America
| | - Andrew J. Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, United Kingdom
| | | | - CJE Metcalf
- Department of Ecology and Evolutionary Biology and the Princeton School of Public and International Affairs, Princeton University, Princeton, New Jersey, United States of America
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
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26
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Saad-Roy CM, Grenfell BT, Levin SA, van den Driessche P, Wingreen NS. Evolution of an asymptomatic first stage of infection in a heterogeneous population. J R Soc Interface 2021; 18:20210175. [PMID: 34129793 PMCID: PMC8205539 DOI: 10.1098/rsif.2021.0175] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 05/24/2021] [Indexed: 11/12/2022] Open
Abstract
Pathogens evolve different life-history strategies, which depend in part on differences in their host populations. A central feature of hosts is their population structure (e.g. spatial). Additionally, hosts themselves can exhibit different degrees of symptoms when newly infected; this latency is a key life-history property of pathogens. With an evolutionary-epidemiological model, we examine the role of population structure on the evolutionary dynamics of latency. We focus on specific power-law-like formulations for transmission and progression from the first infectious stage as a function of latency, assuming that the across-group to within-group transmission ratio increases if hosts are less symptomatic. We find that simple population heterogeneity can lead to local evolutionarily stable strategies (ESSs) at zero and infinite latency in situations where a unique ESS exists in the corresponding homogeneous case. Furthermore, there can exist more than one interior evolutionarily singular strategy. We find that this diversity of outcomes is due to the (possibly slight) advantage of across-group transmission for pathogens that produce fewer symptoms in a first infectious stage. Thus, our work reveals that allowing individuals without symptoms to travel can have important unintended evolutionary effects and is thus fundamentally problematic in view of the evolutionary dynamics of latency.
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Affiliation(s)
- Chadi M. Saad-Roy
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Bryan T. Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Simon A. Levin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - P. van den Driessche
- Department of Mathematics and Statistics, University of Victoria, Victoria, British Columbia, Canada
| | - Ned S. Wingreen
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
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27
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Saad-Roy CM, Morris SE, Metcalf CJE, Mina MJ, Baker RE, Farrar J, Holmes EC, Pybus OG, Graham AL, Levin SA, Grenfell BT, Wagner CE. Partial immunity and SARS-CoV-2 mutations-Response. Science 2021; 372:354-355. [PMID: 33888633 DOI: 10.1126/science.abi6719] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Affiliation(s)
- Chadi M Saad-Roy
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA.
| | - Sinead E Morris
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY 10032, USA
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA.,Princeton School of Public and International Affairs, Princeton University, Princeton, NJ 08544, USA
| | - Michael J Mina
- Departments of Epidemiology and Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Rachel E Baker
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA.,High Meadows Environmental Institute, Princeton University, Princeton, NJ 08544, USA
| | | | - Edward C Holmes
- Marie Bashir Institute for Infectious Diseases and Biosecurity, School of Life and Environmental Sciences, and School of Medical Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Oliver G Pybus
- Department of Zoology, University of Oxford, Oxford OX1 3SZ, UK
| | - Andrea L Graham
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
| | - Simon A Levin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA. .,Princeton School of Public and International Affairs, Princeton University, Princeton, NJ 08544, USA.,Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Caroline E Wagner
- Department of Bioengineering, McGill University, Montreal, QC H3A 0C3, Canada.
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28
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Saad-Roy CM, Morris SE, Metcalf CJE, Mina MJ, Baker RE, Farrar J, Holmes EC, Pybus OG, Graham AL, Levin SA, Grenfell BT, Wagner CE. Epidemiological and evolutionary considerations of SARS-CoV-2 vaccine dosing regimes. Science 2021; 372:363-370. [PMID: 33688062 PMCID: PMC8128287 DOI: 10.1126/science.abg8663] [Citation(s) in RCA: 121] [Impact Index Per Article: 40.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 03/04/2021] [Indexed: 12/11/2022]
Abstract
Given vaccine dose shortages and logistical challenges, various deployment strategies are being proposed to increase population immunity levels to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Two critical issues arise: How timing of delivery of the second dose will affect infection dynamics and how it will affect prospects for the evolution of viral immune escape via a buildup of partially immune individuals. Both hinge on the robustness of the immune response elicited by a single dose as compared with natural and two-dose immunity. Building on an existing immuno-epidemiological model, we find that in the short term, focusing on one dose generally decreases infections, but that longer-term outcomes depend on this relative immune robustness. We then explore three scenarios of selection and find that a one-dose policy may increase the potential for antigenic evolution under certain conditions of partial population immunity. We highlight the critical need to test viral loads and quantify immune responses after one vaccine dose and to ramp up vaccination efforts globally.
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Affiliation(s)
- Chadi M Saad-Roy
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA.
| | - Sinead E Morris
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY 10032, USA
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
- Princeton School of Public and International Affairs, Princeton University, Princeton, NJ 08544, USA
| | - Michael J Mina
- Departments of Epidemiology and Immunology and Infectious Diseases, Harvard School of Public Health, Boston, MA 02115, USA
| | - Rachel E Baker
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
- High Meadows Environmental Institute, Princeton University, Princeton, NJ 08544, USA
| | | | - Edward C Holmes
- Marie Bashir Institute for Infectious Diseases and Biosecurity, School of Life and Environmental Sciences, and School of Medical Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Oliver G Pybus
- Department of Zoology, University of Oxford, Oxford OX1 3SZ, UK
| | - Andrea L Graham
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
| | - Simon A Levin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA.
- Princeton School of Public and International Affairs, Princeton University, Princeton, NJ 08544, USA
- Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Caroline E Wagner
- Department of Bioengineering, McGill University, Montreal, QC H3A 0C3, Canada.
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29
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Phillips MT, Owers KA, Grenfell BT, Pitzer VE. Correction: Changes in historical typhoid transmission across 16 U.S. cities, 1889-1931: Quantifying the impact of investments in water and sewer infrastructures. PLoS Negl Trop Dis 2021; 15:e0009347. [PMID: 33848294 PMCID: PMC8043389 DOI: 10.1371/journal.pntd.0009347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
[This corrects the article DOI: 10.1371/journal.pntd.0008048.].
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30
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Park SW, Pons-Salort M, Messacar K, Cook C, Meyers L, Farrar J, Grenfell BT. Epidemiological dynamics of enterovirus D68 in the United States and implications for acute flaccid myelitis. Sci Transl Med 2021; 13:13/584/eabd2400. [PMID: 33692131 DOI: 10.1126/scitranslmed.abd2400] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 07/24/2020] [Accepted: 02/08/2021] [Indexed: 01/02/2023]
Abstract
Acute flaccid myelitis (AFM) recently emerged in the United States as a rare but serious neurological condition since 2012. Enterovirus D68 (EV-D68) is thought to be a main causative agent, but limited surveillance of EV-D68 in the United States has hampered the ability to assess their causal relationship. Using surveillance data from the BioFire Syndromic Trends epidemiology network in the United States from January 2014 to September 2019, we characterized the epidemiological dynamics of EV-D68 and found latitudinal gradient in the mean timing of EV-D68 cases, which are likely climate driven. We also demonstrated a strong spatiotemporal association of EV-D68 with AFM. Mathematical modeling suggested that the recent dominant biennial cycles of EV-D68 dynamics may not be stable. Nonetheless, we predicted that a major EV-D68 outbreak, and hence an AFM outbreak, would have still been possible in 2020 under normal epidemiological conditions. Nonpharmaceutical intervention efforts due to the ongoing COVID-19 pandemic are likely to have reduced the sizes of EV-D68 and AFM outbreaks in 2020, illustrating the broader epidemiological impact of the pandemic.
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Affiliation(s)
- Sang Woo Park
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08540, USA.
| | - Margarita Pons-Salort
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK
| | - Kevin Messacar
- Department of Pediatrics, Sections of Hospital Medicine and Infectious Diseases, University of Colorado, Aurora, CO 80045, USA.,Children's Hospital Colorado, Aurora, CO 80045, USA
| | - Camille Cook
- BioFire Diagnostics LLC, 515 Colorow Drive, Salt Lake City, UT 84108, USA
| | - Lindsay Meyers
- BioFire Diagnostics LLC, 515 Colorow Drive, Salt Lake City, UT 84108, USA
| | - Jeremy Farrar
- Wellcome Trust, Gibbs Building, 215 Euston Road, London NW1 2BE, UK
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08540, USA.,Princeton School of Public and International Affairs, Princeton University, Princeton, NJ 08540, USA.,Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA
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31
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Rice BL, Annapragada A, Baker RE, Bruijning M, Dotse-Gborgbortsi W, Mensah K, Miller IF, Motaze NV, Raherinandrasana A, Rajeev M, Rakotonirina J, Ramiadantsoa T, Rasambainarivo F, Yu W, Grenfell BT, Tatem AJ, Metcalf CJE. Variation in SARS-CoV-2 outbreaks across sub-Saharan Africa. Nat Med 2021; 27:447-453. [PMID: 33531710 PMCID: PMC8590469 DOI: 10.1038/s41591-021-01234-8] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 01/11/2021] [Indexed: 12/27/2022]
Abstract
A surprising feature of the SARS-CoV-2 pandemic to date is the low burdens reported in sub-Saharan Africa (SSA) countries relative to other global regions. Potential explanations (for example, warmer environments1, younger populations2-4) have yet to be framed within a comprehensive analysis. We synthesized factors hypothesized to drive the pace and burden of this pandemic in SSA during the period from 25 February to 20 December 2020, encompassing demographic, comorbidity, climatic, healthcare capacity, intervention efforts and human mobility dimensions. Large diversity in the probable drivers indicates a need for caution in interpreting analyses that aggregate data across low- and middle-income settings. Our simulation shows that climatic variation between SSA population centers has little effect on early outbreak trajectories; however, heterogeneity in connectivity, although rarely considered, is likely an important contributor to variance in the pace of viral spread across SSA. Our synthesis points to the potential benefits of context-specific adaptation of surveillance systems during the ongoing pandemic. In particular, characterizing patterns of severity over age will be a priority in settings with high comorbidity burdens and poor access to care. Understanding the spatial extent of outbreaks warrants emphasis in settings where low connectivity could drive prolonged, asynchronous outbreaks resulting in extended stress to health systems.
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Affiliation(s)
- Benjamin L Rice
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.
- Madagascar Health and Environmental Research, Maroantsetra, Madagascar.
| | | | - Rachel E Baker
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Princeton Environmental Institute, Princeton University, Princeton, NJ, USA
| | - Marjolein Bruijning
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | | | - Keitly Mensah
- Centre Population et Développement (CEPED), Institut de Recherche pour le Développement (IRD) and Université de Paris, Inserm ERL 1244, Paris, France
| | - Ian F Miller
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Nkengafac Villyen Motaze
- Centre for Vaccines and Immunology, National Institute for Comnmunicable Diseases, National Health Laboratory Service, Johannesburg, South Africa
- Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Antso Raherinandrasana
- Faculty of Medicine, University of Antananarivo, Antananarivo, Madagascar
- Teaching Hospital of Care and Public Health Analakely, Antananarivo, Madagascar
| | - Malavika Rajeev
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Julio Rakotonirina
- Faculty of Medicine, University of Antananarivo, Antananarivo, Madagascar
- Teaching Hospital of Care and Public Health Analakely, Antananarivo, Madagascar
| | - Tanjona Ramiadantsoa
- Department of Life Science, University of Fianarantsoa, Fianarantsoa, Madagascar
- Department of Mathematics, University of Fianarantsoa, Fianarantsoa, Madagascar
- Department of Integrative Biology, University of Wisconsin-Madison, Madison, WI, USA
| | - Fidisoa Rasambainarivo
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Mahaliana Labs SARL, Antananarivo, Madagascar
| | - Weiyu Yu
- School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
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32
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Baker RE, Yang W, Vecchi GA, Metcalf CJE, Grenfell BT. Assessing the influence of climate on wintertime SARS-CoV-2 outbreaks. Nat Commun 2021; 12:846. [PMID: 33558479 PMCID: PMC7870658 DOI: 10.1038/s41467-021-20991-1] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 01/06/2021] [Indexed: 12/23/2022] Open
Abstract
High susceptibility has limited the role of climate in the SARS-CoV-2 pandemic to date. However, understanding a possible future effect of climate, as susceptibility declines and the northern-hemisphere winter approaches, is an important open question. Here we use an epidemiological model, constrained by observations, to assess the sensitivity of future SARS-CoV-2 disease trajectories to local climate conditions. We find this sensitivity depends on both the susceptibility of the population and the efficacy of non-pharmaceutical interventions (NPIs) in reducing transmission. Assuming high susceptibility, more stringent NPIs may be required to minimize outbreak risk in the winter months. Our results suggest that the strength of NPIs remain the greatest determinant of future pre-vaccination outbreak size. While we find a small role for meteorological forecasts in projecting outbreak severity, reducing uncertainty in epidemiological parameters will likely have a more substantial impact on generating accurate predictions.
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Affiliation(s)
- Rachel E Baker
- High Meadows Environmental Institute, Princeton University, Princeton, NJ, USA.
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.
| | - Wenchang Yang
- Department of Geosciences, Princeton University, Princeton, NJ, USA
| | - Gabriel A Vecchi
- High Meadows Environmental Institute, Princeton University, Princeton, NJ, USA
- Department of Geosciences, Princeton University, Princeton, NJ, USA
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- School of Public and International Affairs, Princeton University, Princeton, NJ, USA
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
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33
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Saad-Roy CM, Levin SA, Metcalf CJE, Grenfell BT. Trajectory of individual immunity and vaccination required for SARS-CoV-2 community immunity: a conceptual investigation. J R Soc Interface 2021; 18:20200683. [PMID: 33530857 PMCID: PMC8086877 DOI: 10.1098/rsif.2020.0683] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
SARS-CoV-2 is an international public health emergency; high transmissibility and morbidity and mortality can result in the virus overwhelming health systems. Combinations of social distancing, and test, trace, and isolate strategies can reduce the number of new infections per infected individual below 1, thus driving declines in case numbers, but may be both challenging and costly. These interventions must also be maintained until development and (now likely) mass deployment of a vaccine (or therapeutics), since otherwise, many susceptible individuals are still at risk of infection. We use a simple analytical model to explore how low levels of infection, combined with vaccination, determine the trajectory to community immunity. Understanding the repercussions of the biological characteristics of the viral life cycle in this scenario is of considerable importance. We provide a simple description of this process by modelling the scenario where the effective reproduction number [Formula: see text] is maintained at 1. Since the additional complexity imposed by the strength and duration of transmission-blocking immunity is not yet clear, we use our framework to probe the impact of these uncertainties. Through intuitive analytical relations, we explore how the necessary magnitude of vaccination rates and mitigation efforts depends crucially on the durations of natural and vaccinal immunity. We also show that our framework can encompass seasonality or preexisting immunity due to epidemic dynamics prior to strong mitigation measures. Taken together, our simple conceptual model illustrates the importance of individual and vaccinal immunity for community immunity, and that the quantification of individuals immunized against SARS-CoV-2 is paramount.
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Affiliation(s)
- Chadi M Saad-Roy
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Simon A Levin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.,Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.,Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA.,Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
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34
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Saad-Roy CM, Morris SE, Metcalf CJE, Mina MJ, Baker RE, Farrar J, Holmes EC, Pybus OG, Graham AL, Levin SA, Grenfell BT, Wagner CE. Epidemiological and evolutionary considerations of SARS-CoV-2 vaccine dosing regimes. medRxiv 2021:2021.02.01.21250944. [PMID: 33564785 PMCID: PMC7872380 DOI: 10.1101/2021.02.01.21250944] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
As the threat of Covid-19 continues and in the face of vaccine dose shortages and logistical challenges, various deployment strategies are being proposed to increase population immunity levels. How timing of delivery of the second dose affects infection burden but also prospects for the evolution of viral immune escape are critical questions. Both hinge on the strength and duration (i.e. robustness) of the immune response elicited by a single dose, compared to natural and two-dose immunity. Building on an existing immuno-epidemiological model, we find that in the short-term, focusing on one dose generally decreases infections, but longer-term outcomes depend on this relative immune robustness. We then explore three scenarios of selection, evaluating how different second dose delays might drive immune escape via a build-up of partially immune individuals. Under certain scenarios, we find that a one-dose policy may increase the potential for antigenic evolution. We highlight the critical need to test viral loads and quantify immune responses after one vaccine dose, and to ramp up vaccination efforts throughout the world.
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Affiliation(s)
- Chadi M. Saad-Roy
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton NJ 08540, USA
| | - Sinead E. Morris
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York NY 10032, USA
| | - C. Jessica E. Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton NJ 08540, USA
- Princeton School of Public and International Affairs, Princeton University, Princeton NJ 08540, USA
| | - Michael J. Mina
- Departments of Epidemiology and Immunology and Infectious Diseases, Harvard School of Public Health, Boston MA 02115, USA
| | - Rachel E. Baker
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton NJ 08540, USA
- Princeton Environmental Institute, Princeton University, Princeton NJ 08540, USA
| | | | - Edward C. Holmes
- Marie Bashir Institute for Infectious Diseases and Biosecurity, School of Life and Environmental Sciences and School of Medical Sciences, The University of Sydney, Sydney, NSW, Australia
| | | | - Andrea L. Graham
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton NJ 08540, USA
| | - Simon A. Levin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton NJ 08540, USA
| | - Bryan T. Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton NJ 08540, USA
- Princeton School of Public and International Affairs, Princeton University, Princeton NJ 08540, USA
- Fogarty International Center, National Institutes of Health, Bethesda MD 20892, USA
| | - Caroline E. Wagner
- Department of Bioengineering, McGill University, Montreal QC H3A 0C3, Canada
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35
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Park SW, Farrar J, Messacar K, Meyers L, Pons-Salort M, Grenfell BT. Epidemiological dynamics of enterovirus D68 in the US: implications for acute flaccid myelitis. medRxiv 2021:2020.07.23.20069468. [PMID: 32766605 PMCID: PMC7402064 DOI: 10.1101/2020.07.23.20069468] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The lack of active surveillance for enterovirus D68 (EV-D68) in the US has hampered the ability to assess the relationship with predominantly biennial epidemics of acute flaccid myelitis (AFM), a rare but serious neurological condition. Using novel surveillance data from the BioFire® Syndromic Trends (Trend) epidemiology network, we characterize the epidemiological dynamics of EV-D68 and demonstrate strong spatiotemporal association with AFM. Although the recent dominant biennial cycles of EV-D68 dynamics may not be stable, we show that a major EV-D68 epidemic, and hence an AFM outbreak, would still be possible in 2020 under normal epidemiological conditions. Significant social distancing due to the ongoing COVID-19 pandemic could reduce the size of an EV-D68 epidemic in 2020, illustrating the potential broader epidemiological impact of the pandemic.
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Affiliation(s)
- Sang Woo Park
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08540, USA
| | - Jeremy Farrar
- Wellcome Trust, Gibbs Building, 215 Euston Road, London NW1 2BE, UK
| | - Kevin Messacar
- Department of Pediatrics, Sections of Hospital Medicine and Infectious Diseases, University of Colorado, Aurora, CO 80045, USA
- Children’s Hospital Colorado, Aurora, CO, USA
| | - Lindsay Meyers
- BioFire Diagnostics, LLC 515 Colorow Drive, Salt Lake City, UT 84108 USA
| | - Margarita Pons-Salort
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK
| | - Bryan T. Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08540, USA
- Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ 08540, USA
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
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36
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Saad-Roy CM, Grenfell BT, Levin SA, Pellis L, Stage HB, van den Driessche P, Wingreen NS. Superinfection and the evolution of an initial asymptomatic stage. R Soc Open Sci 2021; 8:202212. [PMID: 33614103 PMCID: PMC7890506 DOI: 10.1098/rsos.202212] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 12/16/2020] [Indexed: 06/12/2023]
Abstract
Pathogens have evolved a variety of life-history strategies. An important strategy consists of successful transmission by an infected host before the appearance of symptoms, that is, while the host is still partially or fully asymptomatic. During this initial stage of infection, it is possible for another pathogen to superinfect an already infected host and replace the previously infecting pathogen. Here, we study the effect of superinfection during the first stage of an infection on the evolutionary dynamics of the degree to which the host is asymptomatic (host latency) in that same stage. We find that superinfection can lead to major differences in evolutionary behaviour. Most strikingly, the duration of immunity following infection can significantly influence pathogen evolutionary dynamics, whereas without superinfection the outcomes are independent of host immunity. For example, changes in host immunity can drive evolutionary transitions from a fully symptomatic to a fully asymptomatic first infection stage. Additionally, if superinfection relative to susceptible infection is strong enough, evolution can lead to a unique strategy of latency that corresponds to a local fitness minimum, and is therefore invasible by nearby mutants. Thus, this strategy is a branching point, and can lead to coexistence of pathogens with different latencies. Furthermore, in this new framework with superinfection, we also find that there can exist two interior singular strategies. Overall, new evolutionary outcomes can cascade from superinfection.
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Affiliation(s)
- Chadi M. Saad-Roy
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Bryan T. Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Simon A. Levin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Lorenzo Pellis
- Department of Mathematics, University of Manchester, Manchester, UK
- The Alan Turing Institute, London, UK
| | - Helena B. Stage
- Department of Mathematics, University of Manchester, Manchester, UK
| | - P. van den Driessche
- Department of Mathematics and Statistics, University of Victoria, Victoria, British Columbia, Canada
| | - Ned S. Wingreen
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
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37
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Wagner CE, Prentice JA, Saad-Roy CM, Yang L, Grenfell BT, Levin SA, Laxminarayan R. Economic and Behavioral Influencers of Vaccination and Antimicrobial Use. Front Public Health 2020; 8:614113. [PMID: 33409264 PMCID: PMC7779682 DOI: 10.3389/fpubh.2020.614113] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 12/01/2020] [Indexed: 01/07/2023] Open
Abstract
Despite vast improvements in global vaccination coverage during the last decade, there is a growing trend in vaccine hesitancy and/or refusal globally. This has implications for the acceptance and coverage of a potential vaccine against COVID-19. In the United States, the number of children exempt from vaccination for “philosophical belief-based” non-medical reasons increased in 12 of the 18 states that allowed this policy from 2009 to 2017 (1). Meanwhile, the overuse and misuse of antibiotics, especially in young children, have led to increasing rates of drug resistance that threaten our ability to treat infectious diseases. Vaccine hesitancy and antibiotic overuse exist side-by-side in the same population of young children, and it is unclear why one modality (antibiotics) is universally seen as safe and effective, while the other (vaccines) is seen as potentially hazardous by some. In this review, we consider the drivers shaping the use of vaccines and antibiotics in the context of three factors: individual incentives, risk perceptions, and social norms and group dynamics. We illustrate how these factors contribute to the societal and individual costs of vaccine underuse and antimicrobial overuse. Ultimately, we seek to understand these factors that are at the nexus of infectious disease epidemiology and social science to inform policy-making.
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Affiliation(s)
- Caroline E Wagner
- Department of Bioengineering, McGill University, Montreal, QC, Canada
| | - Joseph A Prentice
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, United States
| | - Chadi M Saad-Roy
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, United States.,Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, United States
| | - Luojun Yang
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, United States
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, United States.,Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, United States.,Fogarty International Center, National Institutes of Health, Bethesda, MD, United States
| | - Simon A Levin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, United States.,Princeton Environmental Institute, Princeton University, Princeton, NJ, United States
| | - Ramanan Laxminarayan
- Princeton Environmental Institute, Princeton University, Princeton, NJ, United States.,Center for Disease Dynamics, Economics & Policy, Washington, DC, United States
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38
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Korevaar H, Metcalf CJ, Grenfell BT. Tensor decomposition for infectious disease incidence data. Methods Ecol Evol 2020; 11:1690-1700. [PMID: 33381294 PMCID: PMC7756762 DOI: 10.1111/2041-210x.13480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 08/18/2020] [Indexed: 11/27/2022]
Abstract
Many demographic and ecological processes generate seasonal and other periodicities. Seasonality in infectious disease transmission can result from climatic forces such as temperature and humidity; variation in contact rates as a result of migration or school calendar; or temporary surges in birth rates. Seasonal drivers of acute immunizing infections can also drive longer-term fluctuations.Tensor decomposition has been used in many disciplines to uncover dominant trends in multi-dimensional data. We introduce tensors as a novel method for decomposing oscillatory infectious disease time series.We illustrate the reliability of the method by applying it to simulated data. We then present decompositions of measles data from England and Wales. This paper leverages simulations as well as much-studied data to illustrate the power of tensor decomposition to uncover dominant epidemic signals as well as variation in space and time. We then use tensor decomposition to uncover new findings and demonstrate the potential power of the method for disease incidence data. In particular, we are able to distinguish between annual and biennial signals across locations and shifts in these signals over time.Tensor decomposition is able to isolate variation in disease seasonality as a result of variation in demographic rates. The method allows us to discern variation in the strength of such signals by space and population size. Tensors provide an opportunity for a concise approach to uncovering heterogeneity in disease transmission across space and time in large datasets.
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Affiliation(s)
- Hannah Korevaar
- Office of Population ResearchPrinceton UniversityPrincetonNYUSA
| | - C. Jessica Metcalf
- Office of Population ResearchPrinceton UniversityPrincetonNYUSA
- Ecology and Evolutionary BiologyPrinceton UniversityPrincetonNYUSA
| | - Bryan T. Grenfell
- Office of Population ResearchPrinceton UniversityPrincetonNYUSA
- Ecology and Evolutionary BiologyPrinceton UniversityPrincetonNYUSA
- Fogarty International CenterNational Institutes of HealthBethesdaMDUSA
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39
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Abstract
Health outcomes following infection with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) are remarkably variable. The way the virus spreads inside hosts, and how this spread interacts with host immunity and physiology, is likely to determine variation in health outcomes. Decades of data and dynamical analyses of how other viruses spread and interact with host cells could shed light on SARS-CoV-2 within-host trajectories. We review how common axes of variation in within-host dynamics and emergent pathology (such as age and sex) might be combined with ecological principles to understand the case of SARS-CoV-2. We highlight pitfalls in application of existing theoretical frameworks relevant to the complexity of the within-host context and frame the discussion in terms of growing knowledge of the biology of SARS-CoV-2. Viewing health outcomes for SARS-CoV-2 through the lens of ecological models underscores the value of repeated measures on individuals, especially since many lines of evidence suggest important contingence on trajectory.
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Affiliation(s)
- C. Jessica E. Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
- Princeton School of Public and International Affairs, Princeton University, New Jersey, United States of America
| | - Bryan T. Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
- Princeton School of Public and International Affairs, Princeton University, New Jersey, United States of America
| | - Andrea L. Graham
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
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40
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Baker RE, Park SW, Yang W, Vecchi GA, Metcalf CJE, Grenfell BT. The impact of COVID-19 nonpharmaceutical interventions on the future dynamics of endemic infections. Proc Natl Acad Sci U S A 2020; 117:30547-30553. [PMID: 33168723 PMCID: PMC7720203 DOI: 10.1073/pnas.2013182117] [Citation(s) in RCA: 262] [Impact Index Per Article: 65.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Nonpharmaceutical interventions (NPIs) have been employed to reduce the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), yet these measures are already having similar effects on other directly transmitted, endemic diseases. Disruptions to the seasonal transmission patterns of these diseases may have consequences for the timing and severity of future outbreaks. Here we consider the implications of SARS-CoV-2 NPIs for two endemic infections circulating in the United States of America: respiratory syncytial virus (RSV) and seasonal influenza. Using laboratory surveillance data from 2020, we estimate that RSV transmission declined by at least 20% in the United States at the start of the NPI period. We simulate future trajectories of both RSV and influenza, using an epidemic model. As susceptibility increases over the NPI period, we find that substantial outbreaks of RSV may occur in future years, with peak outbreaks likely occurring in the winter of 2021-2022. Longer NPIs, in general, lead to larger future outbreaks although they may display complex interactions with baseline seasonality. Results for influenza broadly echo this picture, but are more uncertain; future outbreaks are likely dependent on the transmissibility and evolutionary dynamics of circulating strains.
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Affiliation(s)
- Rachel E Baker
- Princeton Environmental Institute, Princeton University, Princeton, NJ 08544;
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544
| | - Sang Woo Park
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544
| | - Wenchang Yang
- Department of Geosciences, Princeton University, Princeton, NJ 08544
| | - Gabriel A Vecchi
- Princeton Environmental Institute, Princeton University, Princeton, NJ 08544
- Department of Geosciences, Princeton University, Princeton, NJ 08544
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544
- Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ 08544
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544
- Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ 08544
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD 20892
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41
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Saad-Roy CM, Wagner CE, Baker RE, Morris SE, Farrar J, Graham AL, Levin SA, Mina MJ, Metcalf CJE, Grenfell BT. Immune life history, vaccination, and the dynamics of SARS-CoV-2 over the next 5 years. Science 2020; 370:811-818. [PMID: 32958581 PMCID: PMC7857410 DOI: 10.1126/science.abd7343] [Citation(s) in RCA: 148] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 09/16/2020] [Indexed: 01/08/2023]
Abstract
The future trajectory of the coronavirus disease 2019 (COVID-19) pandemic hinges on the dynamics of adaptive immunity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); however, salient features of the immune response elicited by natural infection or vaccination are still uncertain. We use simple epidemiological models to explore estimates for the magnitude and timing of future COVID-19 cases, given different assumptions regarding the protective efficacy and duration of the adaptive immune response to SARS-CoV-2, as well as its interaction with vaccines and nonpharmaceutical interventions. We find that variations in the immune response to primary SARS-CoV-2 infections and a potential vaccine can lead to markedly different immune landscapes and burdens of critically severe cases, ranging from sustained epidemics to near elimination. Our findings illustrate likely complexities in future COVID-19 dynamics and highlight the importance of immunological characterization beyond the measurement of active infections for adequately projecting the immune landscape generated by SARS-CoV-2 infections.
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Affiliation(s)
- Chadi M Saad-Roy
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA
| | - Caroline E Wagner
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
- Princeton Environmental Institute, Princeton University, Princeton, NJ 08544, USA
- Department of Bioengineering, McGill University, Montreal, Quebec H3A 0C3, Canada
| | - Rachel E Baker
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
- Princeton Environmental Institute, Princeton University, Princeton, NJ 08544, USA
| | - Sinead E Morris
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY 10032, USA
| | | | - Andrea L Graham
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
| | - Simon A Levin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
| | - Michael J Mina
- Departments of Epidemiology and Immunology and Infectious Diseases, Harvard School of Public Health, Boston, MA 02115, USA
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
- Princeton School of Public and International Affairs, Princeton University, Princeton, NJ 08544, USA
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA.
- Princeton School of Public and International Affairs, Princeton University, Princeton, NJ 08544, USA
- Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA
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42
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Morris DH, Petrova VN, Rossine FW, Parker E, Grenfell BT, Neher RA, Levin SA, Russell CA. Asynchrony between virus diversity and antibody selection limits influenza virus evolution. eLife 2020; 9:e62105. [PMID: 33174838 PMCID: PMC7748417 DOI: 10.7554/elife.62105] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 11/04/2020] [Indexed: 12/14/2022] Open
Abstract
Seasonal influenza viruses create a persistent global disease burden by evolving to escape immunity induced by prior infections and vaccinations. New antigenic variants have a substantial selective advantage at the population level, but these variants are rarely selected within-host, even in previously immune individuals. Using a mathematical model, we show that the temporal asynchrony between within-host virus exponential growth and antibody-mediated selection could limit within-host antigenic evolution. If selection for new antigenic variants acts principally at the point of initial virus inoculation, where small virus populations encounter well-matched mucosal antibodies in previously-infected individuals, there can exist protection against reinfection that does not regularly produce observable new antigenic variants within individual infected hosts. Our results provide a theoretical explanation for how virus antigenic evolution can be highly selective at the global level but nearly neutral within-host. They also suggest new avenues for improving influenza control.
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MESH Headings
- Antibodies, Neutralizing/genetics
- Antibodies, Neutralizing/immunology
- Antibodies, Viral/immunology
- Biological Evolution
- Genetic Variation/genetics
- Humans
- Influenza A Virus, H3N2 Subtype/genetics
- Influenza A Virus, H3N2 Subtype/immunology
- Influenza A virus/genetics
- Influenza A virus/immunology
- Influenza, Human/immunology
- Influenza, Human/transmission
- Influenza, Human/virology
- Models, Statistical
- Selection, Genetic/genetics
- Selection, Genetic/immunology
- Virion/genetics
- Virion/immunology
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Affiliation(s)
- Dylan H Morris
- Department of Ecology & Evolutionary Biology, Princeton UniversityPrincetonUnited States
| | - Velislava N Petrova
- Department of Human Genetics, Wellcome Trust Sanger InstituteCambridgeUnited Kingdom
| | - Fernando W Rossine
- Department of Ecology & Evolutionary Biology, Princeton UniversityPrincetonUnited States
| | - Edyth Parker
- Department of Veterinary Medicine, University of CambridgeCambridgeUnited Kingdom
- Department of Medical Microbiology, Academic Medical Center, University of AmsterdamAmsterdamNetherlands
| | - Bryan T Grenfell
- Department of Ecology & Evolutionary Biology, Princeton UniversityPrincetonUnited States
- Fogarty International Center, National Institutes of HealthBethesdaUnited States
| | | | - Simon A Levin
- Department of Ecology & Evolutionary Biology, Princeton UniversityPrincetonUnited States
| | - Colin A Russell
- Department of Medical Microbiology, Academic Medical Center, University of AmsterdamAmsterdamNetherlands
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43
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Hooshyar M, Wagner CE, Baker RE, Metcalf CJE, Grenfell BT, Porporato A. Cyclic epidemics and extreme outbreaks induced by hydro-climatic variability and memory. J R Soc Interface 2020; 17:20200521. [PMID: 33081643 DOI: 10.1098/rsif.2020.0521] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
A minimalist model of ecohydrologic dynamics is coupled to the well-known susceptible-infected-recovered epidemiological model to explore hydro-climatic controls on infection dynamics and extreme outbreaks. The resulting HYSIR model reveals the existence of a noise-induced bifurcation producing oscillations in infection dynamics. Linearization of the governing equations allows for an analytic expression for the periodicity of infections in terms of both epidemiological (e.g. transmission and recovery rate) and hydrologic (i.e. soil moisture decay rate or memory) parameters. Numerical simulations of the full stochastic, nonlinear system show extreme outbreaks in response to particular combinations of hydro-climatic conditions, neither of which is extreme per se, rather than a single major climatic event. These combinations depend on the assumed functional relationship between the hydrologic variables and the transmission rate. Our results emphasize the importance of hydro-climatic history and system memory in evaluating the risk of severe outbreaks.
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Affiliation(s)
- Milad Hooshyar
- CEE, PEI, and PIIRS, Princeton University, Princeton, NJ, USA
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44
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Grantz KH, Meredith HR, Cummings DAT, Metcalf CJE, Grenfell BT, Giles JR, Mehta S, Solomon S, Labrique A, Kishore N, Buckee CO, Wesolowski A. The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology. Nat Commun 2020; 11:4961. [PMID: 32999287 PMCID: PMC7528106 DOI: 10.1038/s41467-020-18190-5] [Citation(s) in RCA: 151] [Impact Index Per Article: 37.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 08/06/2020] [Indexed: 11/24/2022] Open
Abstract
The ongoing coronavirus disease 2019 (COVID-19) pandemic has heightened discussion of the use of mobile phone data in outbreak response. Mobile phone data have been proposed to monitor effectiveness of non-pharmaceutical interventions, to assess potential drivers of spatiotemporal spread, and to support contact tracing efforts. While these data may be an important part of COVID-19 response, their use must be considered alongside a careful understanding of the behaviors and populations they capture. Here, we review the different applications for mobile phone data in guiding and evaluating COVID-19 response, the relevance of these applications for infectious disease transmission and control, and potential sources and implications of selection bias in mobile phone data. We also discuss best practices and potential pitfalls for directly integrating the collection, analysis, and interpretation of these data into public health decision making.
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Affiliation(s)
- Kyra H Grantz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Hannah R Meredith
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Derek A T Cummings
- Department of Biology and the Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology and the Woodrow Wilson School of International and Public Affairs, Princeton University, Princeton, NJ, USA
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology and the Woodrow Wilson School of International and Public Affairs, Princeton University, Princeton, NJ, USA
| | - John R Giles
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Shruti Mehta
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Sunil Solomon
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Alain Labrique
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Nishant Kishore
- Department of Epidemiology and the Center for Communicable Disease Dynamics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Caroline O Buckee
- Department of Epidemiology and the Center for Communicable Disease Dynamics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
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45
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Abstract
In South Korea, the coronavirus disease outbreak peaked at the end of February and subsided in mid-March. We analyzed the likely roles of social distancing in reducing transmission. Our analysis indicated that although transmission might persist in some regions, epidemics can be suppressed with less extreme measures than those taken by China.
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46
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Metcalf CJE, Wesolowski A, Winter AK, Lessler J, Cauchemez S, Moss WJ, McLean AR, Grenfell BT. Using Serology to Anticipate Measles Post-honeymoon Period Outbreaks. Trends Microbiol 2020; 28:597-600. [PMID: 32359782 PMCID: PMC7167541 DOI: 10.1016/j.tim.2020.04.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 04/13/2020] [Accepted: 04/14/2020] [Indexed: 11/13/2022]
Abstract
Measles vaccination is a public health 'best buy', with the highest cost of illness averted of any vaccine-preventable disease (Ozawa et al., Bull. WHO 2017;95:629). In recent decades, substantial reductions have been made in the number of measles cases, with an estimated 20 million deaths averted from 2000 to 2017 (Dabbagh et al., MMWR 2018;67:1323). Yet, an important feature of epidemic dynamics is that large outbreaks can occur following years of apparently successful control (Mclean et al., Epidemiol. Infect. 1988;100:419-442). Such 'post-honeymoon period' outbreaks are a result of the nonlinear dynamics of epidemics (Mclean et al., Epidemiol. Infect. 1988;100:419-442). Anticipating post-honeymoon outbreaks could lead to substantial gains in public health, helping to guide the timing, age-range, and location of catch-up vaccination campaigns (Grais et al., J. Roy. Soc. Interface 2008003B6:67-74). Theoretical conditions for such outbreaks are well understood for measles, yet the information required to make these calculations policy-relevant is largely lacking. We propose that a major extension of serological studies to directly characterize measles susceptibility is a high priority.
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Affiliation(s)
- C J E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.
| | - A Wesolowski
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - A K Winter
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - J Lessler
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - W J Moss
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - A R McLean
- Department of Zoology, Oxford University, Oxford, UK
| | - B T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
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47
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Rice BL, Annapragada A, Baker RE, Bruijning M, Dotse-Gborgbortsi W, Mensah K, Miller IF, Motaze NV, Raherinandrasana A, Rajeev M, Rakotonirina J, Ramiadantsoa T, Rasambainarivo F, Yu W, Grenfell BT, Tatem AJ, Metcalf CJE. High variation expected in the pace and burden of SARS-CoV-2 outbreaks across sub-Saharan Africa. medRxiv 2020:2020.07.23.20161208. [PMID: 32743598 PMCID: PMC7386522 DOI: 10.1101/2020.07.23.20161208] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
A surprising feature of the SARS-CoV-2 pandemic to date is the low burdens reported in sub-Saharan Africa (SSA) countries relative to other global regions. Potential explanations (e.g., warmer environments1, younger populations2-4) have yet to be framed within a comprehensive analysis accounting for factors that may offset the effects of climate and demography. Here, we synthesize factors hypothesized to shape the pace of this pandemic and its burden as it moves across SSA, encompassing demographic, comorbidity, climatic, healthcare and intervention capacity, and human mobility dimensions of risk. We find large scale diversity in probable drivers, such that outcomes are likely to be highly variable among SSA countries. While simulation shows that extensive climatic variation among SSA population centers has little effect on early outbreak trajectories, heterogeneity in connectivity is likely to play a large role in shaping the pace of viral spread. The prolonged, asynchronous outbreaks expected in weakly connected settings may result in extended stress to health systems. In addition, the observed variability in comorbidities and access to care will likely modulate the severity of infection: We show that even small shifts in the infection fatality ratio towards younger ages, which are likely in high risk settings, can eliminate the protective effect of younger populations. We highlight countries with elevated risk of 'slow pace', high burden outbreaks. Empirical data on the spatial extent of outbreaks within SSA countries, their patterns in severity over age, and the relationship between epidemic pace and health system disruptions are urgently needed to guide efforts to mitigate the high burden scenarios explored here.
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Affiliation(s)
- Benjamin L Rice
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Madagascar Health and Environmental Research (MAHERY), Maroantsetra, Madagascar
| | | | - Rachel E Baker
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Princeton Environmental Institute, Princeton University, Princeton, NJ, USA
| | - Marjolein Bruijning
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | | | - Keitly Mensah
- Centre population et Développement CEPED (Université de Paris), Institut Recherche et Développement, Paris, France
| | - Ian F Miller
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Nkengafac Villyen Motaze
- Centre for Vaccines and Immunology (CVI), National Institute for Communicable Diseases (NICD) a division of the National Health Laboratory Service (NHLS), South Africa
- Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Antso Raherinandrasana
- Faculty of Medicine, University of Antananarivo, Madagascar
- Institute of Public Health Analakely, Antananarivo, Madagascar
| | - Malavika Rajeev
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Julio Rakotonirina
- Faculty of Medicine, University of Antananarivo, Madagascar
- Institute of Public Health Analakely, Antananarivo, Madagascar
| | - Tanjona Ramiadantsoa
- Department of Life Science, University of Fianarantsoa, Madagascar
- Department of Mathematics, University of Fianarantsoa, Madagascar
- Department of Integrative Biology, University of Wisconsin-Madison, WI, USA
| | - Fidisoa Rasambainarivo
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Mahaliana Labs SARL, Antananarivo, Madagascar
| | - Weiyu Yu
- School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Princeton School of Public and International Affairs, Princeton University, NJ, USA
| | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Princeton School of Public and International Affairs, Princeton University, NJ, USA
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48
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Park SW, Bolker BM, Champredon D, Earn DJD, Li M, Weitz JS, Grenfell BT, Dushoff J. Reconciling early-outbreak estimates of the basic reproductive number and its uncertainty: framework and applications to the novel coronavirus (SARS-CoV-2) outbreak. J R Soc Interface 2020; 17:20200144. [PMID: 32693748 PMCID: PMC7423425 DOI: 10.1098/rsif.2020.0144] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
A novel coronavirus (SARS-CoV-2) emerged as a global threat in December 2019. As the epidemic progresses, disease modellers continue to focus on estimating the basic reproductive number [Formula: see text]-the average number of secondary cases caused by a primary case in an otherwise susceptible population. The modelling approaches and resulting estimates of [Formula: see text] during the beginning of the outbreak vary widely, despite relying on similar data sources. Here, we present a statistical framework for comparing and combining different estimates of [Formula: see text] across a wide range of models by decomposing the basic reproductive number into three key quantities: the exponential growth rate, the mean generation interval and the generation-interval dispersion. We apply our framework to early estimates of [Formula: see text] for the SARS-CoV-2 outbreak, showing that many [Formula: see text] estimates are overly confident. Our results emphasize the importance of propagating uncertainties in all components of [Formula: see text], including the shape of the generation-interval distribution, in efforts to estimate [Formula: see text] at the outset of an epidemic.
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Affiliation(s)
- Sang Woo Park
- Department of Ecology and Evolutionary Biology, Princeton, NJ, USA
| | - Benjamin M Bolker
- Department of Biology, McMaster University, Hamilton, Ontario, Canada.,Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada.,M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
| | - David Champredon
- Department of Pathology and Laboratory Medicine, University of Western Ontario, London, Ontario, Canada
| | - David J D Earn
- Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada.,M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
| | - Michael Li
- Department of Biology, McMaster University, Hamilton, Ontario, Canada
| | - Joshua S Weitz
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.,School of Physics, Georgia Institute of Technology, Atlanta, GA, USA
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton, NJ, USA.,Princeton School of Public and International Affairs, Princeton, NJ, USA.,Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Jonathan Dushoff
- Department of Biology, McMaster University, Hamilton, Ontario, Canada.,Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada.,M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
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49
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Abstract
A key concern in public health is whether disparities exist between urban and rural areas. One dimension of potential variation is the transmission of infectious diseases. In addition to potential differences between urban and rural local dynamics, the question of whether urban and rural areas participate equally in national dynamics remains unanswered. Specifically, urban and rural areas may diverge in local transmission as well as spatial connectivity, and thus risk for receiving imported cases. Finally, the potential confounding relationship of spatial proximity with size and urban/rural district type has not been addressed by previous research. It is rare to have sufficient data to explore these questions thoroughly. We use exhaustive weekly case reports of measles in 954 urban and 468 rural districts of the UK (1944–1965) to compare both local disease dynamics as well as regional transmission. We employ the time-series susceptible–infected–recovered model to estimate disease transmission, epidemic severity, seasonality and import dependence. Congruent with past results, we observe a clear dependence on population size for the majority of these measures. We use a matched-pair strategy to compare proximate urban and rural districts and control for possible spatial confounders. This analytical strategy reveals a modest difference between urban and rural areas. Rural areas tend to be characterized by more frequent, smaller outbreaks compared to urban counterparts. The magnitude of the difference is slight and the results primarily reinforce the importance of population size, both in terms of local and regional transmission. In sum, urban and rural areas demonstrate remarkable epidemiological similarity in this recent UK context.
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Affiliation(s)
- Hannah Korevaar
- Office of Population Research, Princeton University, Princeton, NJ, USA.,Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - C Jessica Metcalf
- Office of Population Research, Princeton University, Princeton, NJ, USA.,Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ, USA.,Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Bryan T Grenfell
- Office of Population Research, Princeton University, Princeton, NJ, USA.,Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ, USA.,Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.,Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
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50
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Park SW, Bolker BM, Champredon D, Earn DJD, Li M, Weitz JS, Grenfell BT, Dushoff J. Reconciling early-outbreak estimates of the basic reproductive number and its uncertainty: framework and applications to the novel coronavirus (SARS-CoV-2) outbreak. J R Soc Interface 2020. [PMID: 32693748 DOI: 10.1101/2020.01.30.20019877] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/09/2023] Open
Abstract
A novel coronavirus (SARS-CoV-2) emerged as a global threat in December 2019. As the epidemic progresses, disease modellers continue to focus on estimating the basic reproductive number [Formula: see text]-the average number of secondary cases caused by a primary case in an otherwise susceptible population. The modelling approaches and resulting estimates of [Formula: see text] during the beginning of the outbreak vary widely, despite relying on similar data sources. Here, we present a statistical framework for comparing and combining different estimates of [Formula: see text] across a wide range of models by decomposing the basic reproductive number into three key quantities: the exponential growth rate, the mean generation interval and the generation-interval dispersion. We apply our framework to early estimates of [Formula: see text] for the SARS-CoV-2 outbreak, showing that many [Formula: see text] estimates are overly confident. Our results emphasize the importance of propagating uncertainties in all components of [Formula: see text], including the shape of the generation-interval distribution, in efforts to estimate [Formula: see text] at the outset of an epidemic.
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Affiliation(s)
- Sang Woo Park
- Department of Ecology and Evolutionary Biology, Princeton, NJ, USA
| | - Benjamin M Bolker
- Department of Biology, McMaster University, Hamilton, Ontario, Canada
- Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada
- M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
| | - David Champredon
- Department of Pathology and Laboratory Medicine, University of Western Ontario, London, Ontario, Canada
| | - David J D Earn
- Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada
- M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
| | - Michael Li
- Department of Biology, McMaster University, Hamilton, Ontario, Canada
| | - Joshua S Weitz
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA
- School of Physics, Georgia Institute of Technology, Atlanta, GA, USA
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton, NJ, USA
- Princeton School of Public and International Affairs, Princeton, NJ, USA
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Jonathan Dushoff
- Department of Biology, McMaster University, Hamilton, Ontario, Canada
- Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada
- M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
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