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Lopez VK, Cramer EY, Pagano R, Drake JM, O'Dea EB, Adee M, Ayer T, Chhatwal J, Dalgic OO, Ladd MA, Linas BP, Mueller PP, Xiao J, Bracher J, Castro Rivadeneira AJ, Gerding A, Gneiting T, Huang Y, Jayawardena D, Kanji AH, Le K, Mühlemann A, Niemi J, Ray EL, Stark A, Wang Y, Wattanachit N, Zorn MW, Pei S, Shaman J, Yamana TK, Tarasewicz SR, Wilson DJ, Baccam S, Gurung H, Stage S, Suchoski B, Gao L, Gu Z, Kim M, Li X, Wang G, Wang L, Wang Y, Yu S, Gardner L, Jindal S, Marshall M, Nixon K, Dent J, Hill AL, Kaminsky J, Lee EC, Lemaitre JC, Lessler J, Smith CP, Truelove S, Kinsey M, Mullany LC, Rainwater-Lovett K, Shin L, Tallaksen K, Wilson S, Karlen D, Castro L, Fairchild G, Michaud I, Osthus D, Bian J, Cao W, Gao Z, Lavista Ferres J, Li C, Liu TY, Xie X, Zhang S, Zheng S, Chinazzi M, Davis JT, Mu K, Pastore Y Piontti A, Vespignani A, Xiong X, Walraven R, Chen J, Gu Q, Wang L, Xu P, Zhang W, Zou D, Gibson GC, Sheldon D, Srivastava A, Adiga A, Hurt B, Kaur G, Lewis B, Marathe M, Peddireddy AS, Porebski P, Venkatramanan S, Wang L, Prasad PV, Walker JW, Webber AE, Slayton RB, Biggerstaff M, Reich NG, Johansson MA. Challenges of COVID-19 Case Forecasting in the US, 2020-2021. PLoS Comput Biol 2024; 20:e1011200. [PMID: 38709852 DOI: 10.1371/journal.pcbi.1011200] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 04/01/2024] [Indexed: 05/08/2024] Open
Abstract
During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.
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Affiliation(s)
- Velma K Lopez
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Estee Y Cramer
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Robert Pagano
- Unaffiliated, Tucson, Arizona, United States of America
| | - John M Drake
- University of Georgia, Athens, Georgia, United States of America
| | - Eamon B O'Dea
- University of Georgia, Athens, Georgia, United States of America
| | - Madeline Adee
- Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Turgay Ayer
- Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Jagpreet Chhatwal
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Ozden O Dalgic
- Value Analytics Labs, Boston, Massachusetts, United States of America
| | - Mary A Ladd
- Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Benjamin P Linas
- Boston University School of Medicine, Boston, Massachusetts, United States of America
| | - Peter P Mueller
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Jade Xiao
- Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Johannes Bracher
- Chair of Econometrics and Statistics, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | | | - Aaron Gerding
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Tilmann Gneiting
- Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
| | - Yuxin Huang
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Dasuni Jayawardena
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Abdul H Kanji
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Khoa Le
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Anja Mühlemann
- Institute of Mathematical Statistics and Actuarial Science, University of Bern, Bern, Switzerland
| | - Jarad Niemi
- Iowa State University, Ames, Iowa, United States of America
| | - Evan L Ray
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Ariane Stark
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Yijin Wang
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Nutcha Wattanachit
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Martha W Zorn
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Sen Pei
- Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | - Jeffrey Shaman
- Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | - Teresa K Yamana
- Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | - Samuel R Tarasewicz
- Federal Reserve Bank of San Francisco, San Francisco, California, United States of America
| | - Daniel J Wilson
- Federal Reserve Bank of San Francisco, San Francisco, California, United States of America
| | - Sid Baccam
- IEM, Bel Air, Maryland, United States of America
| | - Heidi Gurung
- IEM, Bel Air, Maryland, United States of America
| | - Steve Stage
- IEM, Baton Rouge, Louisiana, United States of America
| | | | - Lei Gao
- George Mason University, Fairfax, Virginia, United States of America
| | - Zhiling Gu
- Iowa State University, Ames, Iowa, United States of America
| | - Myungjin Kim
- Kyungpook National University, Bukgu, Daegu, Republic of Korea
| | - Xinyi Li
- Clemson University, Clemson, South Carolina, United States of America
| | - Guannan Wang
- College of William & Mary, Williamsburg, Virginia, United States of America
| | - Lily Wang
- George Mason University, Fairfax, Virginia, United States of America
| | - Yueying Wang
- Amazon, Seattle, Washington, United States of America
| | - Shan Yu
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Lauren Gardner
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Sonia Jindal
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | | | - Kristen Nixon
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Juan Dent
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Alison L Hill
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Joshua Kaminsky
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Elizabeth C Lee
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | | | - Justin Lessler
- Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Claire P Smith
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Shaun Truelove
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Matt Kinsey
- Johns Hopkins University Applied Physics Lab, Baltimore, Maryland, United States of America
| | - Luke C Mullany
- Johns Hopkins University Applied Physics Lab, Baltimore, Maryland, United States of America
| | | | - Lauren Shin
- Johns Hopkins University Applied Physics Lab, Baltimore, Maryland, United States of America
| | - Katharine Tallaksen
- Johns Hopkins University Applied Physics Lab, Baltimore, Maryland, United States of America
| | - Shelby Wilson
- Johns Hopkins University Applied Physics Lab, Baltimore, Maryland, United States of America
| | - Dean Karlen
- University of Victoria and TRIUMF, Victoria, British Columbia, Canada
| | - Lauren Castro
- Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Geoffrey Fairchild
- Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Isaac Michaud
- Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Dave Osthus
- Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Jiang Bian
- Microsoft, Redmond, Washington, United States of America
| | - Wei Cao
- Microsoft, Redmond, Washington, United States of America
| | - Zhifeng Gao
- Microsoft, Redmond, Washington, United States of America
| | | | - Chaozhuo Li
- Microsoft, Redmond, Washington, United States of America
| | - Tie-Yan Liu
- Microsoft, Redmond, Washington, United States of America
| | - Xing Xie
- Microsoft, Redmond, Washington, United States of America
| | - Shun Zhang
- Microsoft, Redmond, Washington, United States of America
| | - Shun Zheng
- Microsoft, Redmond, Washington, United States of America
| | - Matteo Chinazzi
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Jessica T Davis
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Kunpeng Mu
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Ana Pastore Y Piontti
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Xinyue Xiong
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | | | - Jinghui Chen
- University of California, Los Angeles, Los Angeles, California, United States of America
| | - Quanquan Gu
- University of California, Los Angeles, Los Angeles, California, United States of America
| | - Lingxiao Wang
- University of California, Los Angeles, Los Angeles, California, United States of America
| | - Pan Xu
- University of California, Los Angeles, Los Angeles, California, United States of America
| | - Weitong Zhang
- University of California, Los Angeles, Los Angeles, California, United States of America
| | - Difan Zou
- University of California, Los Angeles, Los Angeles, California, United States of America
| | - Graham Casey Gibson
- Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Daniel Sheldon
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Ajitesh Srivastava
- University of Southern California, Los Angeles, California, United States of America
| | - Aniruddha Adiga
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Benjamin Hurt
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Gursharn Kaur
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Bryan Lewis
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Madhav Marathe
- University of Virginia, Charlottesville, Virginia, United States of America
| | | | | | | | - Lijing Wang
- New Jersey Institute of Technology, Newark, New Jersey, United States of America
| | - Pragati V Prasad
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Jo W Walker
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Alexander E Webber
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Rachel B Slayton
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Matthew Biggerstaff
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Nicholas G Reich
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Michael A Johansson
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
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2
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Pfenning-Butterworth A, Buckley LB, Drake JM, Farner JE, Farrell MJ, Gehman ALM, Mordecai EA, Stephens PR, Gittleman JL, Davies TJ. Interconnecting global threats: climate change, biodiversity loss, and infectious diseases. Lancet Planet Health 2024; 8:e270-e283. [PMID: 38580428 PMCID: PMC11090248 DOI: 10.1016/s2542-5196(24)00021-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 12/06/2023] [Accepted: 02/06/2024] [Indexed: 04/07/2024]
Abstract
The concurrent pressures of rising global temperatures, rates and incidence of species decline, and emergence of infectious diseases represent an unprecedented planetary crisis. Intergovernmental reports have drawn focus to the escalating climate and biodiversity crises and the connections between them, but interactions among all three pressures have been largely overlooked. Non-linearities and dampening and reinforcing interactions among pressures make considering interconnections essential to anticipating planetary challenges. In this Review, we define and exemplify the causal pathways that link the three global pressures of climate change, biodiversity loss, and infectious disease. A literature assessment and case studies show that the mechanisms between certain pairs of pressures are better understood than others and that the full triad of interactions is rarely considered. Although challenges to evaluating these interactions-including a mismatch in scales, data availability, and methods-are substantial, current approaches would benefit from expanding scientific cultures to embrace interdisciplinarity and from integrating animal, human, and environmental perspectives. Considering the full suite of connections would be transformative for planetary health by identifying potential for co-benefits and mutually beneficial scenarios, and highlighting where a narrow focus on solutions to one pressure might aggravate another.
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Affiliation(s)
| | - Lauren B Buckley
- Department of Biology, University of Washington, Seattle, WA, USA
| | - John M Drake
- School of Ecology, University of Georgia, Athens, GA, USA; Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA
| | | | - Maxwell J Farrell
- Department of Ecology & Evolutionary Biology, University of Toronto, Toronto, ON, Canada; School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow, Glasgow, UK
| | - Alyssa-Lois M Gehman
- Institute for the Oceans and Fisheries, University of British Columbia, Vancouver, BC, Canada; Hakai Institute, Calvert, BC, Canada
| | - Erin A Mordecai
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Patrick R Stephens
- Department of Integrative Biology, Oklahoma State University, Stillwater, OK, USA
| | - John L Gittleman
- School of Ecology, University of Georgia, Athens, GA, USA; Nicholas School for the Environment, Duke University, Durham, NC, USA
| | - T Jonathan Davies
- Department of Botany, University of British Columbia, Vancouver, BC, Canada; Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, BC, Canada.
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3
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Mathis SM, Webber AE, León TM, Murray EL, Sun M, White LA, Brooks LC, Green A, Hu AJ, McDonald DJ, Rosenfeld R, Shemetov D, Tibshirani RJ, Kandula S, Pei S, Shaman J, Yaari R, Yamana TK, Agarwal P, Balusu S, Gururajan G, Kamarthi H, Prakash BA, Raman R, Rodríguez A, Zhao Z, Meiyappan A, Omar S, Baccam P, Gurung HL, Stage SA, Suchoski BT, Ajelli M, Kummer AG, Litvinova M, Ventura PC, Wadsworth S, Niemi J, Carcelen E, Hill AL, Jung SM, Lemaitre JC, Lessler J, Loo SL, McKee CD, Sato K, Smith C, Truelove S, McAndrew T, Ye W, Bosse N, Hlavacek WS, Lin YT, Mallela A, Chen Y, Lamm SM, Lee J, Posner RG, Perofsky AC, Viboud C, Clemente L, Lu F, Meyer AG, Santillana M, Chinazzi M, Davis JT, Mu K, Piontti APY, Vespignani A, Xiong X, Ben-Nun M, Riley P, Turtle J, Hulme-Lowe C, Jessa S, Nagraj VP, Turner SD, Williams D, Basu A, Drake JM, Fox SJ, Gibson GC, Suez E, Thommes EW, Cojocaru MG, Cramer EY, Gerding A, Stark A, Ray EL, Reich NG, Shandross L, Wattanachit N, Wang Y, Zorn MW, Al Aawar M, Srivastava A, Meyers LA, Adiga A, Hurt B, Kaur G, Lewis BL, Marathe M, Venkatramanan S, Butler P, Farabow A, Muralidhar N, Ramakrishnan N, Reed C, Biggerstaff M, Borchering RK. Evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations. medRxiv 2023:2023.12.08.23299726. [PMID: 38168429 PMCID: PMC10760285 DOI: 10.1101/2023.12.08.23299726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. Forecasting teams were asked to provide national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one through four weeks ahead for the 2021-22 and 2022-23 influenza seasons. Across both seasons, 26 teams submitted forecasts, with the submitting teams varying between seasons. Forecast skill was evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperformed the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble was the 2nd most accurate model measured by WIS in 2021-22 and the 5th most accurate in the 2022-23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degraded over longer forecast horizons and during periods of rapid change. Current influenza forecasting efforts help inform situational awareness, but research is needed to address limitations, including decreased performance during periods of changing epidemic dynamics.
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Affiliation(s)
- Sarabeth M Mathis
- Centers for Disease Control and Prevention, Atlanta, Georgia, 30329, USA
| | - Alexander E Webber
- Centers for Disease Control and Prevention, Atlanta, Georgia, 30329, USA
| | - Tomás M León
- California Department of Public Health, Richmond, CA, 95899
| | - Erin L Murray
- California Department of Public Health, Richmond, CA, 95899
| | - Monica Sun
- California Department of Public Health, Richmond, CA, 95899
| | - Lauren A White
- California Department of Public Health, Richmond, CA, 95899
| | - Logan C Brooks
- Carnegie Mellon University, Pittsburgh, PA, 15213
- University of California, Berkeley, Berkeley, CA 94720
| | - Alden Green
- Carnegie Mellon University, Pittsburgh, PA, 15213
| | - Addison J Hu
- Carnegie Mellon University, Pittsburgh, PA, 15213
| | | | | | | | - Ryan J Tibshirani
- Carnegie Mellon University, Pittsburgh, PA, 15213
- University of California, Berkeley, Berkeley, CA 94720
| | | | - Sen Pei
- Columbia University, New York, NY, 10032
| | - Jeffrey Shaman
- Columbia University, New York, NY, 10032
- Columbia University School of Climate, New York, NY 10025
| | - Rami Yaari
- Columbia University, New York, NY, 10032
| | | | | | | | | | | | | | - Rishi Raman
- Georgia Institute of Technology, Atlanta, GA, 30318
| | | | - Zhiyuan Zhao
- Georgia Institute of Technology, Atlanta, GA, 30318
| | | | - Shalina Omar
- Guidehouse Advisory and Consulting Services, McClean VA, 22102
| | | | | | | | | | - Marco Ajelli
- Indiana University School of Public Health, Bloomington, IN, 47405
| | | | - Maria Litvinova
- Indiana University School of Public Health, Bloomington, IN, 47405
| | - Paulo C Ventura
- Indiana University School of Public Health, Bloomington, IN, 47405
| | | | | | | | | | - Sung-Mok Jung
- University of North Carolina at Chapel Hill, Chapel Hill, NC
| | | | - Justin Lessler
- University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Sara L Loo
- Johns Hopkins University, Baltimore, MD, 21205
| | | | - Koji Sato
- Johns Hopkins University, Baltimore, MD, 21205
| | | | | | | | | | - Nikos Bosse
- London School of Health and Tropical Medicine, London, UK, WC1E 7HT
| | | | - Yen Ting Lin
- Los Alamos National Laboratory, Los Alamos, NM, 87545
| | | | - Ye Chen
- Northern Arizona University, Flagstaff, AZ, 86011
| | | | - Jaechoul Lee
- Northern Arizona University, Flagstaff, AZ, 86011
| | | | - Amanda C Perofsky
- Fogarty International Center, National Institutes of Health, Bethesda, MD, 20892
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MD, 20892
| | | | - Fred Lu
- Northeastern University, Boston, MA, 02115
| | | | | | | | | | - Kunpeng Mu
- Northeastern University, Boston, MA, 02115
| | | | | | | | | | - Pete Riley
- Predictive Science Inc, San Diego, CA 92121
| | | | | | | | - V P Nagraj
- Signature Science, LLC, Charlottesville, VA, 22911
| | | | | | | | | | | | | | - Ehsan Suez
- University of Georgia, Athens, GA, 30609
| | - Edward W Thommes
- University of Guelph, Guelph, ON N1G 2W1, Canada
- Sanofi, Toronto, ON, M2R 3T4
| | | | | | - Aaron Gerding
- University of Massachusetts Amherst, Amherst, MA, 01003
| | - Ariane Stark
- University of Massachusetts Amherst, Amherst, MA, 01003
| | - Evan L Ray
- University of Massachusetts Amherst, Amherst, MA, 01003
| | | | - Li Shandross
- University of Massachusetts Amherst, Amherst, MA, 01003
| | | | - Yijin Wang
- University of Massachusetts Amherst, Amherst, MA, 01003
| | - Martha W Zorn
- University of Massachusetts Amherst, Amherst, MA, 01003
| | - Majd Al Aawar
- University of Southern California, Los Angeles, CA, 90089
| | | | | | | | | | | | | | | | | | | | | | | | | | - Carrie Reed
- Centers for Disease Control and Prevention, Atlanta, Georgia, 30329, USA
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Drake JM, Handel A, Marty É, O’Dea EB, O’Sullivan T, Righi G, Tredennick AT. A data-driven semi-parametric model of SARS-CoV-2 transmission in the United States. PLoS Comput Biol 2023; 19:e1011610. [PMID: 37939201 PMCID: PMC10659176 DOI: 10.1371/journal.pcbi.1011610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 11/20/2023] [Accepted: 10/17/2023] [Indexed: 11/10/2023] Open
Abstract
To support decision-making and policy for managing epidemics of emerging pathogens, we present a model for inference and scenario analysis of SARS-CoV-2 transmission in the USA. The stochastic SEIR-type model includes compartments for latent, asymptomatic, detected and undetected symptomatic individuals, and hospitalized cases, and features realistic interval distributions for presymptomatic and symptomatic periods, time varying rates of case detection, diagnosis, and mortality. The model accounts for the effects on transmission of human mobility using anonymized mobility data collected from cellular devices, and of difficult to quantify environmental and behavioral factors using a latent process. The baseline transmission rate is the product of a human mobility metric obtained from data and this fitted latent process. We fit the model to incident case and death reports for each state in the USA and Washington D.C., using likelihood Maximization by Iterated particle Filtering (MIF). Observations (daily case and death reports) are modeled as arising from a negative binomial reporting process. We estimate time-varying transmission rate, parameters of a sigmoidal time-varying fraction of hospitalized cases that result in death, extra-demographic process noise, two dispersion parameters of the observation process, and the initial sizes of the latent, asymptomatic, and symptomatic classes. In a retrospective analysis covering March-December 2020, we show how mobility and transmission strength became decoupled across two distinct phases of the pandemic. The decoupling demonstrates the need for flexible, semi-parametric approaches for modeling infectious disease dynamics in real-time.
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Affiliation(s)
- John M. Drake
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Andreas Handel
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
- College of Public Health, University of Georgia, Athens, Georgia, United States of America
| | - Éric Marty
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Eamon B. O’Dea
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Tierney O’Sullivan
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Giovanni Righi
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Andrew T. Tredennick
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
- Western EcoSystems Technology, Inc., Laramie, Wyoming, United States of America
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5
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Drake JM. How to publish a 'Method' article in Ecology Letters. Ecol Lett 2023; 26:1645-1646. [PMID: 37847781 DOI: 10.1111/ele.14304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2023]
Affiliation(s)
- John M Drake
- Odum School of Ecology, University of Georgia, Athens, Georgia, USA
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6
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Drake JM, Marty É, Gandhi KJK, Welch-Devine M, Bledsoe B, Shepherd M, Seymour L, Fortuin CC, Montes C. Disasters collide at the intersection of extreme weather and infectious diseases. Ecol Lett 2023; 26:485-489. [PMID: 36849208 DOI: 10.1111/ele.14188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/12/2023] [Accepted: 02/05/2023] [Indexed: 03/01/2023]
Abstract
Natural disasters interact to affect the resilience and prosperity of communities and disproportionately affect low income families and communities of colour. However, due to lack of a common theoretical framework, these are rarely quantified. Observing severe weather events (e.g. hurricanes and tornadoes) and epidemics (e.g. COVID-19) unfolding in southeastern US communities led us to conjecture that interactions among catastrophic disturbances might be much more considerable than previously recognized. For instance, hurricane evacuations increase human aggregation, a factor that affects the transmission of acute infections like SARS-CoV-2. Similarly, weather damage to health infrastructure can reduce a community's ability to provide services to people who are ill. As globalization and human population and movement continue to increase and weather events are becoming more intense, such complex interactions are expected to magnify and significantly impact environmental and human health.
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Affiliation(s)
- John M Drake
- Odum School of Ecology & Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, USA
| | - Éric Marty
- Odum School of Ecology & Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, USA
| | - Kamal J K Gandhi
- D.B. Warnell School of Forestry and Natural Resources, University of Georgia, Athens, Georgia, USA
| | | | - Brian Bledsoe
- College of Engineering & Institute for Resilient Infrastructure Systems, University of Georgia, Athens, Georgia, USA
| | - Marshall Shepherd
- Department of Geography and Atmospheric Sciences Program, University of Georgia, Athens, Georgia, USA
| | - Lynne Seymour
- Department of Statistics, University of Georgia, Athens, Georgia, USA
| | - Christine C Fortuin
- D.B. Warnell School of Forestry and Natural Resources, University of Georgia, Athens, Georgia, USA
- Mississippi State University, College of Forest Resources, Mississippi State, Mississippi, USA
| | - Cristian Montes
- D.B. Warnell School of Forestry and Natural Resources, University of Georgia, Athens, Georgia, USA
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7
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Drake JM, Chase JM. How to publish a 'perspective' or 'synthesis' article in Ecology Letters. Ecol Lett 2023; 26:349-350. [PMID: 36806413 DOI: 10.1111/ele.14165] [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: 02/22/2023]
Affiliation(s)
- John M Drake
- Odum School of Ecology, University of Georgia, Athens, Georgia, USA
| | - Jonathan M Chase
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
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8
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Evans MV, Bhatnagar S, Drake JM, Murdock CC, Rice JL, Mukherjee S. The mismatch of narratives and local ecologies in the everyday governance of water access and mosquito control in an urbanizing community. Health Place 2023; 80:102989. [PMID: 36804681 DOI: 10.1016/j.healthplace.2023.102989] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 01/05/2023] [Accepted: 02/06/2023] [Indexed: 02/18/2023]
Abstract
Mosquito-borne disease presents a significant threat to urban populations, but risk can be uneven across a city due to underlying environmental patterns. Urban residents rely on social and economic processes to control the environment and mediate disease risk, a phenomenon known as everyday governance. We studied how households employed everyday governance of urban infrastructure relevant to mosquito-borne disease in Bengaluru, India to examine if and how inequalities in everyday governance manifest in differences in mosquito control. We found that governance mechanisms differed for water access and mosquitoes. Economic and social capital served different roles for each, influenced by global narratives of water and vector control.
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Affiliation(s)
- M V Evans
- MIVEGEC, Univ. Montpellier, CNRS, IRD, Montpellier, France; Odum School of Ecology, University of Georgia, Athens, GA, USA; Center for Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA.
| | - S Bhatnagar
- Observatoire de Genève, Université de Genève, Sauverny, Switzerland; School of Arts and Sciences, Azim Premji University, Bengaluru, Karnataka, India
| | - J M Drake
- Odum School of Ecology, University of Georgia, Athens, GA, USA; Center for Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA
| | - C C Murdock
- Odum School of Ecology, University of Georgia, Athens, GA, USA; Center for Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA; Department of Entomology, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, USA; Cornell Institute of Host-Microbe Interactions and Disease, Cornell University, Ithaca, NY, USA; Northeast Regional Center for Excellence in Vector-borne Diseases, Cornell University, Ithaca, NY, USA
| | - J L Rice
- Department of Geography, University of Georgia, Athens, GA, USA
| | - S Mukherjee
- School of Arts and Sciences, Azim Premji University, Bengaluru, Karnataka, India; Biological and Life Sciences Division, School of Arts and Sciences, Ahmedabad University, Ahmedabad, Gujarat, India
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9
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Sundaram M, Schmidt JP, Han BA, Drake JM, Stephens PR. Traits, phylogeny and host cell receptors predict Ebolavirus host status among African mammals. PLoS Negl Trop Dis 2022; 16:e0010993. [PMID: 36542657 PMCID: PMC9815631 DOI: 10.1371/journal.pntd.0010993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 01/05/2023] [Accepted: 11/28/2022] [Indexed: 12/24/2022] Open
Abstract
We explore how animal host traits, phylogenetic identity and cell receptor sequences relate to infection status and mortality from ebolaviruses. We gathered exhaustive databases of mortality from Ebolavirus after exposure and infection status based on PCR and antibody tests. We performed ridge regressions predicting mortality and infection as a function of traits, phylogenetic eigenvectors and separately host receptor sequences. We found that mortality from Ebolavirus had a strong association to life history characteristics and phylogeny. In contrast, infection status related not just to life history and phylogeny, but also to fruit consumption which suggests that geographic overlap of frugivorous mammals can lead to spread of virus in the wild. Niemann Pick C1 (NPC1) receptor sequences predicted infection statuses of bats included in our study with very high accuracy, suggesting that characterizing NPC1 in additional species is a promising avenue for future work. We combine the predictions from our mortality and infection status models to differentiate between species that are infected and also die from Ebolavirus versus species that are infected but tolerate the virus (possible reservoirs of Ebolavirus). We therefore present the first comprehensive estimates of Ebolavirus reservoir statuses for all known terrestrial mammals in Africa.
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Affiliation(s)
- Mekala Sundaram
- Department of Integrative Biology, Oklahoma State University, Stillwater, Oklahoma, United States of America
- * E-mail:
| | - John Paul Schmidt
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
| | - Barbara A. Han
- Cary Institute of Ecosystems Studies, Millbrook, New York, United States of America
| | - John M. Drake
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Patrick R. Stephens
- Department of Integrative Biology, Oklahoma State University, Stillwater, Oklahoma, United States of America
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10
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L Tait J, M Bulmer S, M Drake J, R Drain J, C Main L. Impact of 12 weeks of basic military training on testosterone and cortisol responses. BMJ Mil Health 2022:e002179. [PMID: 36316059 DOI: 10.1136/military-2022-002179] [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: 06/10/2022] [Accepted: 10/10/2022] [Indexed: 11/05/2022]
Abstract
INTRODUCTION Military personnel train and operate in challenging multistressor environments, which can affect hormonal levels, and subsequently compromise performance and recovery. The aims of this project were to evaluate concentrations of cortisol and testosterone and subjective perceptions of stress and recovery across basic military training (BMT). METHODS 32 male recruits undergoing BMT were tracked over a 12-week course. Saliva samples were collected weekly, on waking, 30 min postwaking and bedtime. Perceptions of stress and recovery were collected weekly. Daily physical activity (steps) were measured via wrist-mounted accelerometers across BMT. Physical fitness was assessed via the multistage fitness test and push-ups in weeks 2 and 8. RESULTS Concentrations of testosterone and cortisol, and the testosterone:cortisol ratio changed significantly across BMT, with variations in responses concurrent with programmatic demands. Perceptions of stress and recovery also fluctuated according to training elements. Recruits averaged 17 027 steps per day between weeks 2 and 12, with week-to-week variations. On average, recruits significantly increased predicted VO2max (3.6 (95% CI 1.0 to 6.1) mL/kg/min) and push-ups (5. 5 (95% CI 1.4 to 9.7) repetitions) between weeks 2 and 8. CONCLUSIONS Recruit stress responses oscillated over BMT in line with programmatic demands indicating that BMT was, at a group level, well-tolerated with no signs of enduring physiological strain or overtraining. The sensitivity of cortisol, testosterone and the testosterone:cortisol ratio to the stressors of military training, suggest they may have a role in monitoring physiological strain in military personnel. Subjective measures may also have utility within a monitoring framework to help ensure adaptive, rather than maladaptive (eg, injury, attrition), outcomes in military recruits.
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Affiliation(s)
- Jamie L Tait
- Deakin University, Institute for Physical Activity and Nutrition (IPAN), Burwood, Victoria, Australia
| | - S M Bulmer
- Deakin University, School of Exercise and Nutrition Sciences, Burwood, Victoria, Australia
| | - J M Drake
- Deakin University, School of Exercise and Nutrition Sciences, Burwood, Victoria, Australia
| | - J R Drain
- Defence Science and Technology Group, Melbourne, Victoria, Australia
| | - L C Main
- Deakin University, Institute for Physical Activity and Nutrition (IPAN), Burwood, Victoria, Australia
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11
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Stephens PR, Sundaram M, Ferreira S, Gottdenker N, Nipa KF, Schatz AM, Schmidt JP, Drake JM. Drivers of African Filovirus (Ebola and Marburg) Outbreaks. Vector Borne Zoonotic Dis 2022; 22:478-490. [PMID: 36084314 PMCID: PMC9508452 DOI: 10.1089/vbz.2022.0020] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Outbreaks of African filoviruses often have high mortality, including more than 11,000 deaths among 28,562 cases during the West Africa Ebola outbreak of 2014-2016. Numerous studies have investigated the factors that contributed to individual filovirus outbreaks, but there has been little quantitative synthesis of this work. In addition, the ways in which the typical causes of filovirus outbreaks differ from other zoonoses remain poorly described. In this study, we quantify factors associated with 45 outbreaks of African filoviruses (ebolaviruses and Marburg virus) using a rubric of 48 candidate causal drivers. For filovirus outbreaks, we reviewed >700 peer-reviewed and gray literature sources and developed a list of the factors reported to contribute to each outbreak (i.e., a "driver profile" for each outbreak). We compare and contrast the profiles of filovirus outbreaks to 200 background outbreaks, randomly selected from a global database of 4463 outbreaks of bacterial and viral zoonotic diseases. We also test whether the quantitative patterns that we observed were robust to the influences of six covariates, country-level factors such as gross domestic product, population density, and latitude that have been shown to bias global outbreak data. We find that, regardless of whether covariates are included or excluded from models, the driver profile of filovirus outbreaks differs from that of background outbreaks. Socioeconomic factors such as trade and travel, wild game consumption, failures of medical procedures, and deficiencies in human health infrastructure were more frequently reported in filovirus outbreaks than in the comparison group. Based on our results, we also present a review of drivers reported in at least 10% of filovirus outbreaks, with examples of each provided.
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Affiliation(s)
- Patrick R. Stephens
- Department of Integrative Biology, Oklahoma State University, Stillwater, Oklahoma, USA
| | - Mekala Sundaram
- Department of Integrative Biology, Oklahoma State University, Stillwater, Oklahoma, USA
| | - Susana Ferreira
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, USA
- Department of Agricultural and Applied Economics, University of Georgia, Athens, Georgia, USA
| | - Nicole Gottdenker
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, USA
- Department of Agricultural and Applied Economics, University of Georgia, Athens, Georgia, USA
- Department of Pathology, College of Veterinary Medicine, University of Georgia, Athens, Georgia, USA
- Odum School of Ecology, University of Georgia, Athens, Georgia, USA
| | - Kaniz Fatema Nipa
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, USA
- Odum School of Ecology, University of Georgia, Athens, Georgia, USA
| | - Annakate M. Schatz
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, USA
- Odum School of Ecology, University of Georgia, Athens, Georgia, USA
| | - John Paul Schmidt
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, USA
- Odum School of Ecology, University of Georgia, Athens, Georgia, USA
| | - John M. Drake
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, USA
- Odum School of Ecology, University of Georgia, Athens, Georgia, USA
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12
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Tredennick AT, O'Dea EB, Ferrari MJ, Park AW, Rohani P, Drake JM. Anticipating infectious disease re-emergence and elimination: a test of early warning signals using empirically based models. J R Soc Interface 2022; 19:20220123. [PMID: 35919978 PMCID: PMC9346357 DOI: 10.1098/rsif.2022.0123] [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] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Timely forecasts of the emergence, re-emergence and elimination of human infectious diseases allow for proactive, rather than reactive, decisions that save lives. Recent theory suggests that a generic feature of dynamical systems approaching a tipping point-early warning signals (EWS) due to critical slowing down (CSD)-can anticipate disease emergence and elimination. Empirical studies documenting CSD in observed disease dynamics are scarce, but such demonstration of concept is essential to the further development of model-independent outbreak detection systems. Here, we use fitted, mechanistic models of measles transmission in four cities in Niger to detect CSD through statistical EWS. We find that several EWS accurately anticipate measles re-emergence and elimination, suggesting that CSD should be detectable before disease transmission systems cross key tipping points. These findings support the idea that statistical signals based on CSD, coupled with decision-support algorithms and expert judgement, could provide the basis for early warning systems of disease outbreaks.
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Affiliation(s)
- Andrew T Tredennick
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA.,Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA.,Western EcoSystems Technology, Inc., 1610 East Reynolds Street, Laramie, WY 82070, USA
| | - Eamon B O'Dea
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA.,Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
| | - Matthew J Ferrari
- The Center for Infectious Disease Dynamics and Department of Biology, The Pennsylvania State University, University Park, PA 16802, USA
| | - Andrew W Park
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA.,Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA.,Department of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
| | - Pejman Rohani
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA.,Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA.,Department of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
| | - John M Drake
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA.,Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
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13
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Taube JC, Miller PB, Drake JM. An open-access database of infectious disease transmission trees to explore superspreader epidemiology. PLoS Biol 2022; 20:e3001685. [PMID: 35731837 PMCID: PMC9255728 DOI: 10.1371/journal.pbio.3001685] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 07/05/2022] [Accepted: 05/23/2022] [Indexed: 12/12/2022] Open
Abstract
Historically, emerging and reemerging infectious diseases have caused large, deadly, and expensive multinational outbreaks. Often outbreak investigations aim to identify who infected whom by reconstructing the outbreak transmission tree, which visualizes transmission between individuals as a network with nodes representing individuals and branches representing transmission from person to person. We compiled a database, called OutbreakTrees, of 382 published, standardized transmission trees consisting of 16 directly transmitted diseases ranging in size from 2 to 286 cases. For each tree and disease, we calculated several key statistics, such as tree size, average number of secondary infections, the dispersion parameter, and the proportion of cases considered superspreaders, and examined how these statistics varied over the course of each outbreak and under different assumptions about the completeness of outbreak investigations. We demonstrated the potential utility of the database through 2 short analyses addressing questions about superspreader epidemiology for a variety of diseases, including Coronavirus Disease 2019 (COVID-19). First, we found that our transmission trees were consistent with theory predicting that intermediate dispersion parameters give rise to the highest proportion of cases causing superspreading events. Additionally, we investigated patterns in how superspreaders are infected. Across trees with more than 1 superspreader, we found preliminary support for the theory that superspreaders generate other superspreaders. In sum, our findings put the role of superspreading in COVID-19 transmission in perspective with that of other diseases and suggest an approach to further research regarding the generation of superspreaders. These data have been made openly available to encourage reuse and further scientific inquiry. This study compiles and standardizes reported infectious disease transmission trees to analyze trends in superspreader frequency and generation; these data support theories that intermediate dispersion parameters give rise to the highest proportion of cases causing superspreading events, and that superspreaders generate other superspreaders.
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Affiliation(s)
- Juliana C. Taube
- Department of Mathematics, Bowdoin College, Brunswick, Maine, United States of America
- * E-mail:
| | - Paige B. Miller
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
| | - John M. Drake
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
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14
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Evans MV, Drake JM. A Data-driven Horizon Scan of Bacterial Pathogens at the Wildlife-livestock Interface. Ecohealth 2022; 19:246-258. [PMID: 35666334 PMCID: PMC9168633 DOI: 10.1007/s10393-022-01599-3] [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] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 04/01/2022] [Indexed: 06/15/2023]
Abstract
Many livestock diseases rely on wildlife for the transmission or maintenance of the pathogen, and the wildlife-livestock interface represents a potential site of disease emergence for novel pathogens in livestock. Predicting which pathogen species are most likely to emerge in the future is an important challenge for infectious disease surveillance and intelligence. We used a machine learning approach to conduct a data-driven horizon scan of bacterial associations at the wildlife-livestock interface for cows, sheep, and pigs. Our model identified and ranked from 76 to 189 potential novel bacterial species that might associate with each livestock species. Wildlife reservoirs of known and novel bacteria were shared among all three species, suggesting that targeting surveillance and/or control efforts towards these reservoirs could contribute disproportionately to reducing spillover risk to livestock. By predicting pathogen-host associations at the wildlife-livestock interface, we demonstrate one way to plan for and prevent disease emergence in livestock.
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Affiliation(s)
- Michelle V Evans
- MIVEGEC, Institut de Recherche pour le Développement, 34000, Montpellier, France.
- Odum School of Ecology, University of Georgia, Athens, 30606, USA.
- Center for Ecology of Infectious Diseases, University of Georgia, Athens, 30606, USA.
| | - John M Drake
- Odum School of Ecology, University of Georgia, Athens, 30606, USA
- Center for Ecology of Infectious Diseases, University of Georgia, Athens, 30606, USA
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15
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Vinson JE, Gottdenker NL, Chaves LF, Kaul RB, Kramer AM, Drake JM, Hall RJ. Land reversion and zoonotic spillover risk. R Soc Open Sci 2022; 9:220582. [PMID: 35706674 PMCID: PMC9174719 DOI: 10.1098/rsos.220582] [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] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 05/11/2022] [Indexed: 05/03/2023]
Abstract
Deforestation alters wildlife communities and modifies human-wildlife interactions, often increasing zoonotic spillover potential. When deforested land reverts to forest, species composition differences between primary and regenerating (secondary) forest could alter spillover risk trajectory. We develop a mathematical model of land-use change, where habitats differ in their relative spillover risk, to understand how land reversion influences spillover risk. We apply this framework to scenarios where spillover risk is higher in deforested land than mature forest, reflecting higher relative abundance of highly competent species and/or increased human-wildlife encounters, and where regenerating forest has either very low or high spillover risk. We find the forest regeneration rate, the spillover risk of regenerating forest relative to deforested land, and how rapidly regenerating forest regains attributes of mature forest determine landscape-level spillover risk. When regenerating forest has a much lower spillover risk than deforested land, reversion lowers cumulative spillover risk, but instaneous spillover risk peaks earlier. However, when spillover risk is high in regenerating and cleared habitats, landscape-level spillover risk remains high, especially when cleared land is rapidly abandoned then slowly regenerates to mature forest. These results suggest that proactive wildlife management and awareness of human exposure risk in regenerating forests could be important tools for spillover mitigation.
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Affiliation(s)
- John E. Vinson
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
| | - Nicole L. Gottdenker
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
- Department of Veterinary Pathology, College of Veterinary Medicine, University of Georgia, Athens, GA 30602, USA
| | - Luis Fernando Chaves
- Instituto Conmemorativo Gorgas de Estudios de la Salud, Apartado Postal 0816-15 02593, Panamá, República de Panamá
| | - RajReni B. Kaul
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
| | - Andrew M. Kramer
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
- Department of Integrative Biology, University of South Florida, Tampa, FL 33620, USA
| | - John M. Drake
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
| | - Richard J. Hall
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
- Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, GA 30602, USA
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Cramer EY, Ray EL, Lopez VK, Bracher J, Brennen A, Castro Rivadeneira AJ, Gerding A, Gneiting T, House KH, Huang Y, Jayawardena D, Kanji AH, Khandelwal A, Le K, Mühlemann A, Niemi J, Shah A, Stark A, Wang Y, Wattanachit N, Zorn MW, Gu Y, Jain S, Bannur N, Deva A, Kulkarni M, Merugu S, Raval A, Shingi S, Tiwari A, White J, Abernethy NF, Woody S, Dahan M, Fox S, Gaither K, Lachmann M, Meyers LA, Scott JG, Tec M, Srivastava A, George GE, Cegan JC, Dettwiller ID, England WP, Farthing MW, Hunter RH, Lafferty B, Linkov I, Mayo ML, Parno MD, Rowland MA, Trump BD, Zhang-James Y, Chen S, Faraone SV, Hess J, Morley CP, Salekin A, Wang D, Corsetti SM, Baer TM, Eisenberg MC, Falb K, Huang Y, Martin ET, McCauley E, Myers RL, Schwarz T, Sheldon D, Gibson GC, Yu R, Gao L, Ma Y, Wu D, Yan X, Jin X, Wang YX, Chen Y, Guo L, Zhao Y, Gu Q, Chen J, Wang L, Xu P, Zhang W, Zou D, Biegel H, Lega J, McConnell S, Nagraj VP, Guertin SL, Hulme-Lowe C, Turner SD, Shi Y, Ban X, Walraven R, Hong QJ, Kong S, van de Walle A, Turtle JA, Ben-Nun M, Riley S, Riley P, Koyluoglu U, DesRoches D, Forli P, Hamory B, Kyriakides C, Leis H, Milliken J, Moloney M, Morgan J, Nirgudkar N, Ozcan G, Piwonka N, Ravi M, Schrader C, Shakhnovich E, Siegel D, Spatz R, Stiefeling C, Wilkinson B, Wong A, Cavany S, España G, Moore S, Oidtman R, Perkins A, Kraus D, Kraus A, Gao Z, Bian J, Cao W, Ferres JL, Li C, Liu TY, Xie X, Zhang S, Zheng S, Vespignani A, Chinazzi M, Davis JT, Mu K, Pastore y Piontti A, Xiong X, Zheng A, Baek J, Farias V, Georgescu A, Levi R, Sinha D, Wilde J, Perakis G, Bennouna MA, Nze-Ndong D, Singhvi D, Spantidakis I, Thayaparan L, Tsiourvas A, Sarker A, Jadbabaie A, Shah D, Della Penna N, Celi LA, Sundar S, Wolfinger R, Osthus D, Castro L, Fairchild G, Michaud I, Karlen D, Kinsey M, Mullany LC, Rainwater-Lovett K, Shin L, Tallaksen K, Wilson S, Lee EC, Dent J, Grantz KH, Hill AL, Kaminsky J, Kaminsky K, Keegan LT, Lauer SA, Lemaitre JC, Lessler J, Meredith HR, Perez-Saez J, Shah S, Smith CP, Truelove SA, Wills J, Marshall M, Gardner L, Nixon K, Burant JC, Wang L, Gao L, Gu Z, Kim M, Li X, Wang G, Wang Y, Yu S, Reiner RC, Barber R, Gakidou E, Hay SI, Lim S, Murray C, Pigott D, Gurung HL, Baccam P, Stage SA, Suchoski BT, Prakash BA, Adhikari B, Cui J, Rodríguez A, Tabassum A, Xie J, Keskinocak P, Asplund J, Baxter A, Oruc BE, Serban N, Arik SO, Dusenberry M, Epshteyn A, Kanal E, Le LT, Li CL, Pfister T, Sava D, Sinha R, Tsai T, Yoder N, Yoon J, Zhang L, Abbott S, Bosse NI, Funk S, Hellewell J, Meakin SR, Sherratt K, Zhou M, Kalantari R, Yamana TK, Pei S, Shaman J, Li ML, Bertsimas D, Lami OS, Soni S, Bouardi HT, Ayer T, Adee M, Chhatwal J, Dalgic OO, Ladd MA, Linas BP, Mueller P, Xiao J, Wang Y, Wang Q, Xie S, Zeng D, Green A, Bien J, Brooks L, Hu AJ, Jahja M, McDonald D, Narasimhan B, Politsch C, Rajanala S, Rumack A, Simon N, Tibshirani RJ, Tibshirani R, Ventura V, Wasserman L, O’Dea EB, Drake JM, Pagano R, Tran QT, Ho LST, Huynh H, Walker JW, Slayton RB, Johansson MA, Biggerstaff M, Reich NG. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States. Proc Natl Acad Sci U S A 2022; 119:e2113561119. [PMID: 35394862 PMCID: PMC9169655 DOI: 10.1073/pnas.2113561119] [Citation(s) in RCA: 87] [Impact Index Per Article: 43.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 01/24/2022] [Indexed: 01/15/2023] Open
Abstract
Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.
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Affiliation(s)
- Estee Y. Cramer
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Evan L. Ray
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Velma K. Lopez
- COVID-19 Response, Centers for Disease Control and Prevention; Atlanta, GA 30333
| | - Johannes Bracher
- Chair of Econometrics and Statistics, Karlsruhe Institute of Technology, 76185 Karlsruhe, Germany
- Computational Statistics Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany
| | | | | | - Aaron Gerding
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Tilmann Gneiting
- Computational Statistics Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany
- Institute of Stochastics, Karlsruhe Institute of Technology, 69118 Karlsruhe, Germany
| | - Katie H. House
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Yuxin Huang
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Dasuni Jayawardena
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Abdul H. Kanji
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Ayush Khandelwal
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Khoa Le
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Anja Mühlemann
- Institute of Mathematical Statistics and Actuarial Science, University of Bern, CH-3012 Bern, Switzerland
| | - Jarad Niemi
- Department of Statistics, Iowa State University, Ames, IA 50011
| | - Apurv Shah
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Ariane Stark
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Yijin Wang
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Nutcha Wattanachit
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Martha W. Zorn
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | | | - Sansiddh Jain
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Nayana Bannur
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Ayush Deva
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Mihir Kulkarni
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Srujana Merugu
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Alpan Raval
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Siddhant Shingi
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Avtansh Tiwari
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Jerome White
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | | | - Spencer Woody
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712
| | - Maytal Dahan
- Texas Advanced Computing Center, Austin, TX 78758
| | - Spencer Fox
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712
| | | | | | - Lauren Ancel Meyers
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712
| | - James G. Scott
- Department of Information, Risk, and Operations Management, University of Texas at Austin, Austin, TX 78712
| | - Mauricio Tec
- Department of Statistics and Data Sciences, University of Texas at Austin, Austin, TX 78712
| | - Ajitesh Srivastava
- Ming Hsieh Department of Computer and Electrical Engineering, University of Southern California, Los Angeles, CA 90089
| | - Glover E. George
- US Army Engineer Research and Development Center, Vicksburg, MS 39180
| | - Jeffrey C. Cegan
- US Army Engineer Research and Development Center, Concord, MA 01742
| | - Ian D. Dettwiller
- US Army Engineer Research and Development Center, Vicksburg, MS 39180
| | | | | | - Robert H. Hunter
- US Army Engineer Research and Development Center, Vicksburg, MS 39180
| | - Brandon Lafferty
- US Army Engineer Research and Development Center, Vicksburg, MS 39180
| | - Igor Linkov
- US Army Engineer Research and Development Center, Concord, MA 01742
| | - Michael L. Mayo
- US Army Engineer Research and Development Center, Vicksburg, MS 39180
| | - Matthew D. Parno
- US Army Engineer Research and Development Center, Hanover, NH 03755
| | | | | | - Yanli Zhang-James
- Department of Psychiatry and Behavioral Sciences, State University of New York Upstate Medical University, Syracuse, NY 13210
| | - Samuel Chen
- School of Medicine, State University of New York Upstate Medical University, Syracuse, NY 13210
| | - Stephen V. Faraone
- Department of Psychiatry and Behavioral Sciences, State University of New York Upstate Medical University, Syracuse, NY 13210
| | - Jonathan Hess
- Department of Psychiatry and Behavioral Sciences, State University of New York Upstate Medical University, Syracuse, NY 13210
| | - Christopher P. Morley
- Department of Public Health & Preventive Medicine, State University of New York Upstate Medical University, Syracuse, NY 13210
| | - Asif Salekin
- Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY 13207
| | - Dongliang Wang
- Department of Public Health & Preventive Medicine, State University of New York Upstate Medical University, Syracuse, NY 13210
| | | | - Thomas M. Baer
- Department of Physics, Trinity University, San Antonio, TX 78212
| | - Marisa C. Eisenberg
- Department of Complex Systems, University of Michigan, Ann Arbor, MI 48109
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109
- School of Public Health, Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109
| | - Karl Falb
- Department of Physics, University of Michigan, Ann Arbor, MI, 48109
| | - Yitao Huang
- Department of Physics, University of Michigan, Ann Arbor, MI, 48109
| | - Emily T. Martin
- School of Public Health, Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109
| | - Ella McCauley
- Department of Physics, University of Michigan, Ann Arbor, MI, 48109
| | - Robert L. Myers
- Department of Physics, University of Michigan, Ann Arbor, MI, 48109
| | - Tom Schwarz
- Department of Physics, University of Michigan, Ann Arbor, MI, 48109
| | - Daniel Sheldon
- College of Information and Computer Sciences, University of Massachusetts, Amherst, MA 01003
| | - Graham Casey Gibson
- School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA 01003
| | - Rose Yu
- Department of Computer Science and Engineering, University of California, San Diego, CA 92093
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115
| | - Liyao Gao
- Department of Statistics, University of Washington, Seattle, WA 98185
| | - Yian Ma
- Halıcıoğlu Data Science Institute, University of California, San Diego, CA 92093
| | - Dongxia Wu
- Department of Computer Science and Engineering, University of California, San Diego, CA 92093
| | - Xifeng Yan
- Department of Computer Science, University of California, Santa Barbara, CA 93106
| | - Xiaoyong Jin
- Department of Computer Science, University of California, Santa Barbara, CA 93106
| | - Yu-Xiang Wang
- Department of Computer Science, University of California, Santa Barbara, CA 93106
| | - YangQuan Chen
- Mechatronics, Embedded Systems and Automation Lab, Department of Mechanical Engineering, University of California, Merced, CA 95301
| | - Lihong Guo
- Jilin University, Changchun City, Jilin Province, 130012, People's Republic of China
| | - Yanting Zhao
- University of Science and Technology of China, Heifei, Anhui, 230027, People's Republic of China
| | - Quanquan Gu
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Jinghui Chen
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Lingxiao Wang
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Pan Xu
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Weitong Zhang
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Difan Zou
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Hannah Biegel
- Department of Mathematics, University of Arizona, Tucson, AZ 85721
| | - Joceline Lega
- Department of Mathematics, University of Arizona, Tucson, AZ 85721
| | | | - V. P. Nagraj
- Quality Assurance and Data Science, Signature Science, LLC, Charlottesville, VA 22911
| | - Stephanie L. Guertin
- Quality Assurance and Data Science, Signature Science, LLC, Charlottesville, VA 22911
| | | | - Stephen D. Turner
- Quality Assurance and Data Science, Signature Science, LLC, Charlottesville, VA 22911
| | - Yunfeng Shi
- Department of Materials Science and Engineering, Rensselaer Polytechnic Institute, Troy, NY 12309
| | - Xuegang Ban
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195
| | | | - Qi-Jun Hong
- School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ 85287
- School of Engineering, Brown University, Providence, RI 02912
| | | | | | - James A. Turtle
- Infectious Disease Group, Predictive Science, Inc, San Diego, CA 92121
| | - Michal Ben-Nun
- Infectious Disease Group, Predictive Science, Inc, San Diego, CA 92121
| | - Steven Riley
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College, W2 1PG London, United Kingdom
| | - Pete Riley
- Infectious Disease Group, Predictive Science, Inc, San Diego, CA 92121
| | | | | | - Pedro Forli
- Oliver Wyman Digital, Oliver Wyman, Sao Paolo, Brazil 04711-904
| | - Bruce Hamory
- Health & Life Sciences, Oliver Wyman, Boston, MA 02110
| | | | - Helen Leis
- Health & Life Sciences, Oliver Wyman, New York, NY 10036
| | - John Milliken
- Financial Services, Oliver Wyman, New York, NY 10036
| | | | - James Morgan
- Financial Services, Oliver Wyman, New York, NY 10036
| | | | - Gokce Ozcan
- Financial Services, Oliver Wyman, New York, NY 10036
| | - Noah Piwonka
- Health & Life Sciences, Oliver Wyman, New York, NY 10036
| | - Matt Ravi
- Core Consultant Group, Oliver Wyman, New York, NY 10036
| | - Chris Schrader
- Health & Life Sciences, Oliver Wyman, New York, NY 10036
| | | | - Daniel Siegel
- Financial Services, Oliver Wyman, New York, NY 10036
| | - Ryan Spatz
- Core Consultant Group, Oliver Wyman, New York, NY 10036
| | - Chris Stiefeling
- Financial Services, Oliver Wyman Digital, Toronto, ON, Canada M5J 0A1
| | | | | | - Sean Cavany
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556
| | - Guido España
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556
| | - Sean Moore
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556
| | - Rachel Oidtman
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556
- Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637
| | - Alex Perkins
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556
| | - David Kraus
- Department of Mathematics and Statistics, Masaryk University, 61137 Brno, Czech Republic
| | - Andrea Kraus
- Department of Mathematics and Statistics, Masaryk University, 61137 Brno, Czech Republic
| | | | | | - Wei Cao
- Microsoft, Redmond, WA 98029
| | | | | | | | | | | | | | - Alessandro Vespignani
- Institute for Scientific Interchange Foundation, Turin, 10133, Italy
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Matteo Chinazzi
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Jessica T. Davis
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Kunpeng Mu
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Ana Pastore y Piontti
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Xinyue Xiong
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Andrew Zheng
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Jackie Baek
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Vivek Farias
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02142
| | - Andreea Georgescu
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Retsef Levi
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02142
| | - Deeksha Sinha
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Joshua Wilde
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | | | | | | | - Divya Singhvi
- Technology, Operations and Statistics (TOPS) group, Stern School of Business, New York University, New York, NY 10012
| | | | | | | | - Arnab Sarker
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Ali Jadbabaie
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Devavrat Shah
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Nicolas Della Penna
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Leo A. Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA 02139
| | | | | | - Dave Osthus
- Statistical Sciences Group, Los Alamos National Laboratory, Los Alamos, NM 87545
| | - Lauren Castro
- Information Systems and Modeling Group, Los Alamos National Laboratory, Los Alamos, NM 87545
| | - Geoffrey Fairchild
- Information Systems and Modeling Group, Los Alamos National Laboratory, Los Alamos, NM 87545
| | - Isaac Michaud
- Statistical Sciences Group, Los Alamos National Laboratory, Los Alamos, NM 87545
| | - Dean Karlen
- Department of Physics and Astronomy, University of Victoria, Victoria, BC, V8W 2Y2, Canada
- Physical Sciences Division, TRIUMF, Vancouver, BC, V8W 2Y2, Canada
| | - Matt Kinsey
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723
| | - Luke C. Mullany
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723
| | | | - Lauren Shin
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723
| | | | - Shelby Wilson
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723
| | - Elizabeth C. Lee
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Juan Dent
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Kyra H. Grantz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Alison L. Hill
- Institute for Computational Medicine, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21218
| | - Joshua Kaminsky
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | | | - Lindsay T. Keegan
- Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, UT 84108
| | - Stephen A. Lauer
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Joseph C. Lemaitre
- Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Hannah R. Meredith
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Javier Perez-Saez
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Sam Shah
- Unaffiliated, San Francisco, CA 94122
| | - Claire P. Smith
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Shaun A. Truelove
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
- International Vaccine Access Center, Johns Hopkins University, Baltimore, MD 21231
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21231
| | | | - Maximilian Marshall
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218
| | - Lauren Gardner
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218
| | - Kristen Nixon
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218
| | | | - Lily Wang
- Department of Statistics, Iowa State University, Ames, IA 50011
| | - Lei Gao
- Department of Finance, Iowa State University, Ames, IA 50011
| | - Zhiling Gu
- Department of Statistics, Iowa State University, Ames, IA 50011
| | - Myungjin Kim
- Department of Statistics, Iowa State University, Ames, IA 50011
| | - Xinyi Li
- School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC 29634
| | - Guannan Wang
- Department of Mathematics, College of William & Mary, Williamsburg, VA 23187
| | - Yueying Wang
- Department of Statistics, Iowa State University, Ames, IA 50011
| | - Shan Yu
- Department of Statistics, University of Virginia, Charlottesville, VA 22904
| | - Robert C. Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - Ryan Barber
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - Emmanuela Gakidou
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - Simon I. Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - Steve Lim
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - Chris Murray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - David Pigott
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | | | | | | | | | - B. Aditya Prakash
- College of Computing, Georgia Institute of Technology, Atlanta, GA 30308
| | - Bijaya Adhikari
- Department of Computer Science, University of Iowa, Iowa City, IA 52242
| | - Jiaming Cui
- College of Computing, Georgia Institute of Technology, Atlanta, GA 30308
| | | | - Anika Tabassum
- Department of Computer Science, Virginia Tech, Falls Church, VA 22043
| | - Jiajia Xie
- College of Computing, Georgia Institute of Technology, Atlanta, GA 30308
| | - Pinar Keskinocak
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
| | - John Asplund
- Advanced Data Analytics, Metron, Inc., Reston, VA 20190
| | - Arden Baxter
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
| | - Buse Eylul Oruc
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
| | - Nicoleta Serban
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
| | | | | | | | | | | | | | | | | | | | - Thomas Tsai
- Department of Health Policy and Management, Harvard University, Cambridge, MA 02138
| | | | | | | | - Sam Abbott
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Nikos I. Bosse
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Sebastian Funk
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Joel Hellewell
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Sophie R. Meakin
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Katharine Sherratt
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Mingyuan Zhou
- McCombs School of Business, The University of Texas at Austin, Austin, TX 78712
| | - Rahi Kalantari
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712
| | - Teresa K. Yamana
- Department of Environmental Health Sciences, Columbia University, New York, NY 10032
| | - Sen Pei
- Department of Environmental Health Sciences, Columbia University, New York, NY 10032
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Columbia University, New York, NY 10032
| | - Michael L. Li
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Dimitris Bertsimas
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02142
| | - Omar Skali Lami
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Saksham Soni
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Hamza Tazi Bouardi
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Turgay Ayer
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
- Winship Cancer Institute, Emory University Medical School, Atlanta, GA 30322
| | - Madeline Adee
- Radiology-Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114
| | - Jagpreet Chhatwal
- Radiology-Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114
| | - Ozden O. Dalgic
- Health Economic Modeling, Value Analytics Labs, 34776 İstanbul, Turkey
| | - Mary A. Ladd
- Radiology-Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114
| | - Benjamin P. Linas
- Department of Medicine, Section of Infectious Diseases, Boston University School of Medicine, Boston, MA 02118
| | - Peter Mueller
- Radiology-Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114
| | - Jade Xiao
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
| | - Yuanjia Wang
- Department of Biostatistics, Columbia University, New York, NY 10032
- Department of Psychiatry, Columbia University, New York, NY 10032
| | - Qinxia Wang
- Department of Biostatistics, Columbia University, New York, NY 10032
| | - Shanghong Xie
- Department of Biostatistics, Columbia University, New York, NY 10032
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - Alden Green
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Jacob Bien
- Marshall School of Business, Department of Data Sciences and Operations (DSO), University of Southern California, Los Angeles, CA 90089
| | - Logan Brooks
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Addison J. Hu
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Maria Jahja
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Daniel McDonald
- Department of Statistics, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Balasubramanian Narasimhan
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA 94305
- Department of Statistics, Stanford University, Stanford, CA 94305
| | - Collin Politsch
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Samyak Rajanala
- Department of Statistics, Stanford University, Stanford, CA 94305
| | - Aaron Rumack
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Noah Simon
- Department of Biostatistics, University of Washington, Seattle, WA 98195
| | - Ryan J. Tibshirani
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Rob Tibshirani
- Department of Statistics, Stanford University, Stanford, CA 94305
| | - Valerie Ventura
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Larry Wasserman
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Eamon B. O’Dea
- Odum School of Ecology, University of Georgia, Athens, GA 30602
| | - John M. Drake
- Odum School of Ecology, University of Georgia, Athens, GA 30602
| | | | - Quoc T. Tran
- Catalog Data Science, Walmart Inc., Sunnyvale, CA 94085
| | - Lam Si Tung Ho
- Department of Mathematics and Statistics, Dalhousie University, Halifax, NS, B3H 4R2, Canada
| | - Huong Huynh
- Virtual Power System Inc, Milpitas, CA 95035
| | - Jo W. Walker
- COVID-19 Response, Centers for Disease Control and Prevention; Atlanta, GA 30333
| | - Rachel B. Slayton
- COVID-19 Response, Centers for Disease Control and Prevention; Atlanta, GA 30333
| | - Michael A. Johansson
- COVID-19 Response, Centers for Disease Control and Prevention; Atlanta, GA 30333
| | - Matthew Biggerstaff
- COVID-19 Response, Centers for Disease Control and Prevention; Atlanta, GA 30333
| | - Nicholas G. Reich
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
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17
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Evans MV, Bhatnagar S, Drake JM, Murdock CC, Mukherjee S. Socio‐ecological dynamics in urban systems: An integrative approach to mosquito‐borne disease in Bengaluru, India. People and Nature 2022. [DOI: 10.1002/pan3.10311] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Affiliation(s)
- Michelle V. Evans
- MIVEGEC, Univ. Montpellier, CNRS, IRD Montpellier France
- Odum School of Ecology University of Georgia Athens GA USA
- Center for Ecology of Infectious Diseases University of Georgia Athens GA USA
| | - Siddharth Bhatnagar
- Observatoire de Genève Université de Genève Sauverny Switzerland
- School of Arts and Sciences Azim Premji University Bengaluru India
| | - John M. Drake
- Odum School of Ecology University of Georgia Athens GA USA
- Center for Ecology of Infectious Diseases University of Georgia Athens GA USA
| | - Courtney C. Murdock
- Odum School of Ecology University of Georgia Athens GA USA
- Center for Ecology of Infectious Diseases University of Georgia Athens GA USA
- Department of Entomology, College of Agriculture and Life Sciences Cornell University Ithaca NY USA
- Cornell Institute of Host‐Microbe Interactions and Disease Cornell University Ithaca NY USA
- Northeast Regional Center of Excellence in Vector‐borne Diseases Cornell University Ithaca NY USA
| | - Shomen Mukherjee
- School of Arts and Sciences Azim Premji University Bengaluru India
- Biology and Life Sciences Division, School of Arts and Sciences Ahmedabad University Ahmedabad Gujarat India
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18
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O'Dea EB, Drake JM. A semi-parametric, state-space compartmental model with time-dependent parameters for forecasting COVID-19 cases, hospitalizations and deaths. J R Soc Interface 2022; 19:20210702. [PMID: 35167769 PMCID: PMC8847000 DOI: 10.1098/rsif.2021.0702] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Short-term forecasts of the dynamics of coronavirus disease 2019 (COVID-19) in the period up to its decline following mass vaccination was a task that received much attention but proved difficult to do with high accuracy. However, the availability of standardized forecasts and versioned datasets from this period allows for continued work in this area. Here, we introduce the Gaussian infection state space with time dependence (GISST) forecasting model. We evaluate its performance in one to four weeks ahead forecasts of COVID-19 cases, hospital admissions and deaths in the state of California made with official reports of COVID-19, Google’s mobility reports and vaccination data available each week. Evaluation of these forecasts with a weighted interval score shows them to consistently outperform a naive baseline forecast and often score closer to or better than a high-performing ensemble forecaster. The GISST model also provides parameter estimates for a compartmental model of COVID-19 dynamics, includes a regression submodel for the transmission rate and allows for parameters to vary over time according to a random walk. GISST provides a novel, balanced combination of computational efficiency, model interpretability and applicability to large multivariate datasets that may prove useful in improving the accuracy of infectious disease forecasts.
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Affiliation(s)
- Eamon B O'Dea
- Odum School of Ecology and Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA
| | - John M Drake
- Odum School of Ecology and Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA
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19
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Dablander F, Heesterbeek H, Borsboom D, Drake JM. Overlapping timescales obscure early warning signals of the second COVID-19 wave. Proc Biol Sci 2022; 289:20211809. [PMID: 35135355 PMCID: PMC8825995 DOI: 10.1098/rspb.2021.1809] [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] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 01/13/2022] [Indexed: 11/12/2022] Open
Abstract
Early warning indicators based on critical slowing down have been suggested as a model-independent and low-cost tool to anticipate the (re)emergence of infectious diseases. We studied whether such indicators could reliably have anticipated the second COVID-19 wave in European countries. Contrary to theoretical predictions, we found that characteristic early warning indicators generally decreased rather than increased prior to the second wave. A model explains this unexpected finding as a result of transient dynamics and the multiple timescales of relaxation during a non-stationary epidemic. Particularly, if an epidemic that seems initially contained after a first wave does not fully settle to its new quasi-equilibrium prior to changing circumstances or conditions that force a second wave, then indicators will show a decreasing rather than an increasing trend as a result of the persistent transient trajectory of the first wave. Our simulations show that this lack of timescale separation was to be expected during the second European epidemic wave of COVID-19. Overall, our results emphasize that the theory of critical slowing down applies only when the external forcing of the system across a critical point is slow relative to the internal system dynamics.
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Affiliation(s)
- Fabian Dablander
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - Hans Heesterbeek
- Department of Population Health Sciences, Utrecht University, Utrecht, The Netherlands
| | - Denny Borsboom
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - John M. Drake
- Odum School of Ecology, University of Georgia, Athens, GA, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA
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20
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Rohani P, Drake JM. Untangling the evolution of dengue viruses. Science 2021; 374:941-942. [PMID: 34793209 DOI: 10.1126/science.abm6812] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
[Figure: see text].
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Affiliation(s)
- Pejman Rohani
- Odum School of Ecology, Department of Infectious Diseases, College of Veterinary Medicine, the Center for the Ecology of Infectious Diseases and the Center for Influenza Disease and Emergence Research (CIDER), University of Georgia, Athens, GA, USA
| | - John M Drake
- Odum School of Ecology, the Center for the Ecology of Infectious Diseases and CIDER, University of Georgia, Athens, GA, USA
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21
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Abstract
Helminths are parasites that cause disease at considerable cost to public health and present a risk for emergence as novel human infections. Although recent research has elucidated characteristics conferring a propensity to emergence in other parasite groups (e.g. viruses), the understanding of factors associated with zoonotic potential in helminths remains poor. We applied an investigator-directed learning algorithm to a global dataset of mammal helminth traits to identify factors contributing to spillover of helminths from wild animal hosts into humans. We characterized parasite traits that distinguish between zoonotic and non-zoonotic species with 91% accuracy. Results suggest that helminth traits relating to transmission (e.g. definitive and intermediate hosts) and geography (e.g. distribution) are more important to discriminating zoonotic from non-zoonotic species than morphological or epidemiological traits. Whether or not a helminth causes infection in companion animals (cats and dogs) is the most important predictor of propensity to cause human infection. Finally, we identified helminth species with high modelled propensity to cause zoonosis (over 70%) that have not previously been considered to be of risk. This work highlights the importance of prioritizing studies on the transmission of helminths that infect pets and points to the risks incurred by close associations with these animals. This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.
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Affiliation(s)
- Ania A Majewska
- Odum School of Ecology and the Center for Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA.,Biology Department, Emory University, Atlanta, GA, USA
| | - Tao Huang
- Cary Institute of Ecosystem Studies, Millbrook, NY, USA.,Ecology, Evolution, and Behavior, Boise State University, Boise, ID, USA
| | - Barbara Han
- Cary Institute of Ecosystem Studies, Millbrook, NY, USA
| | - John M Drake
- Odum School of Ecology and the Center for Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA
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22
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Stephens PR, Gottdenker N, Schatz AM, Schmidt JP, Drake JM. Characteristics of the 100 largest modern zoonotic disease outbreaks. Philos Trans R Soc Lond B Biol Sci 2021; 376:20200535. [PMID: 34538141 PMCID: PMC8450623 DOI: 10.1098/rstb.2020.0535] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/14/2021] [Indexed: 12/19/2022] Open
Abstract
Zoonotic disease outbreaks are an important threat to human health and numerous drivers have been recognized as contributing to their increasing frequency. Identifying and quantifying relationships between drivers of zoonotic disease outbreaks and outbreak severity is critical to developing targeted zoonotic disease surveillance and outbreak prevention strategies. However, quantitative studies of outbreak drivers on a global scale are lacking. Attributes of countries such as press freedom, surveillance capabilities and latitude also bias global outbreak data. To illustrate these issues, we review the characteristics of the 100 largest outbreaks in a global dataset (n = 4463 bacterial and viral zoonotic outbreaks), and compare them with 200 randomly chosen background controls. Large outbreaks tended to have more drivers than background outbreaks and were related to large-scale environmental and demographic factors such as changes in vector abundance, human population density, unusual weather conditions and water contamination. Pathogens of large outbreaks were more likely to be viral and vector-borne than background outbreaks. Overall, our case study shows that the characteristics of large zoonotic outbreaks with thousands to millions of cases differ consistently from those of more typical outbreaks. We also discuss the limitations of our work, hoping to pave the way for more comprehensive future studies. This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.
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Affiliation(s)
- Patrick R. Stephens
- Odum School of Ecology and Center for the Ecology of Infectious Diseases, University of Georgia, Athens, 30602 GA, USA
| | - N. Gottdenker
- Odum School of Ecology and Center for the Ecology of Infectious Diseases, University of Georgia, Athens, 30602 GA, USA
- Department of Pathology, College of Veterinary Medicine, University of Georgia, Athens, 30602 GA, USA
| | - A. M. Schatz
- Odum School of Ecology and Center for the Ecology of Infectious Diseases, University of Georgia, Athens, 30602 GA, USA
| | - J. P. Schmidt
- Odum School of Ecology and Center for the Ecology of Infectious Diseases, University of Georgia, Athens, 30602 GA, USA
| | - John M. Drake
- Odum School of Ecology and Center for the Ecology of Infectious Diseases, University of Georgia, Athens, 30602 GA, USA
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23
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Richards RL, Drake JM, Ezenwa VO. Do predators keep prey healthy or make them sicker? A meta-analysis. Ecol Lett 2021; 25:278-294. [PMID: 34738700 DOI: 10.1111/ele.13919] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 07/17/2021] [Accepted: 10/14/2021] [Indexed: 11/27/2022]
Abstract
Ecological theory suggests that predators can either keep prey populations healthy by reducing parasite burdens or alternatively, increase parasitism in prey. To quantify the overall magnitude and direction of the effect of predation on parasitism in prey observed in practice, we conducted a meta-analysis of 47 empirical studies. We also examined how study attributes, including parasite type and life cycle, habitat type, study design, and whether predators were able to directly consume prey contributed to variation in the predator-prey-parasite interaction. We found that the overall effect of predation on parasitism differed between parasites and parasitoids and that whether consumptive effects were present, and whether a predator was a non-host spreader of parasites, were the most important traits predicting the parasite response. Our results suggest that the mechanistic basis of predator-prey interactions strongly influences the effects of predators on parasites and that these effects, although context dependent, are predictable.
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Affiliation(s)
- Robert L Richards
- Odum School of Ecology, University of Georgia, Athens, Georgia, USA.,Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, USA
| | - John M Drake
- Odum School of Ecology, University of Georgia, Athens, Georgia, USA.,Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, USA
| | - Vanessa O Ezenwa
- Odum School of Ecology, University of Georgia, Athens, Georgia, USA.,Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, USA.,Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, Georgia, USA
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24
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Miller PB, Zalwango S, Galiwango R, Kakaire R, Sekandi J, Steinbaum L, Drake JM, Whalen CC, Kiwanuka N. Association between tuberculosis in men and social network structure in Kampala, Uganda. BMC Infect Dis 2021; 21:1023. [PMID: 34592946 PMCID: PMC8482622 DOI: 10.1186/s12879-021-06475-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 07/26/2021] [Indexed: 11/22/2022] Open
Abstract
Background Globally, tuberculosis disease (TB) is more common among males than females. Recent research proposes that differences in social mixing by sex could alter infection patterns in TB. We examine evidence for two mechanisms by which social-mixing could increase men’s contact rates with TB cases. First, men could be positioned in social networks such that they contact more people or social groups. Second, preferential mixing by sex could prime men to have more exposure to TB cases. Methods We compared the networks of male and female TB cases and healthy matched controls living in Kampala, Uganda. Specifically, we estimated their positions in social networks (network distance to TB cases, degree, betweenness, and closeness) and assortativity patterns (mixing with adult men, women, and children inside and outside the household). Results The observed network consisted of 11,840 individuals. There were few differences in estimates of node position by sex. We found distinct mixing patterns by sex and TB disease status including that TB cases have proportionally more adult male contacts and fewer contacts with children. Conclusions This analysis used a network approach to study how social mixing patterns are associated with TB disease. Understanding these mechanisms may have implications for designing targeted intervention strategies in high-burden populations.
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Affiliation(s)
- Paige B Miller
- Odum School of Ecology, University of Georgia, Athens, GA, 30602, USA
| | | | | | - Robert Kakaire
- Global Health Institute, College of Public Health, University of Georgia, 100 Foster Drive, Athens, GA, 30602, USA
| | - Juliet Sekandi
- Global Health Institute, College of Public Health, University of Georgia, 100 Foster Drive, Athens, GA, 30602, USA
| | - Lauren Steinbaum
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA, 30602, USA
| | - John M Drake
- Odum School of Ecology, University of Georgia, Athens, GA, 30602, USA
| | - Christopher C Whalen
- Global Health Institute, College of Public Health, University of Georgia, 100 Foster Drive, Athens, GA, 30602, USA. .,Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA, 30602, USA.
| | - Noah Kiwanuka
- Makerere University School of Public Health, Kampala, Uganda
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25
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Rakotonanahary RJL, Andriambolamanana H, Razafinjato B, Raza-Fanomezanjanahary EM, Ramanandraitsiory V, Ralaivavikoa F, Tsirinomen'ny Aina A, Rahajatiana L, Rakotonirina L, Haruna J, Cordier LF, Murray MB, Cowley G, Jordan D, Krasnow MA, Wright PC, Gillespie TR, Docherty M, Loyd T, Evans MV, Drake JM, Ngonghala CN, Rich ML, Popper SJ, Miller AC, Ihantamalala FA, Randrianambinina A, Ramiandrisoa B, Rakotozafy E, Rasolofomanana A, Rakotozafy G, Andriamahatana Vololoniaina MC, Andriamihaja B, Garchitorena A, Rakotonirina J, Mayfield A, Finnegan KE, Bonds MH. Integrating Health Systems and Science to Respond to COVID-19 in a Model District of Rural Madagascar. Front Public Health 2021; 9:654299. [PMID: 34368043 PMCID: PMC8333873 DOI: 10.3389/fpubh.2021.654299] [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: 01/15/2021] [Accepted: 05/31/2021] [Indexed: 11/17/2022] Open
Abstract
There are many outstanding questions about how to control the global COVID-19 pandemic. The information void has been especially stark in the World Health Organization Africa Region, which has low per capita reported cases, low testing rates, low access to therapeutic drugs, and has the longest wait for vaccines. As with all disease, the central challenge in responding to COVID-19 is that it requires integrating complex health systems that incorporate prevention, testing, front line health care, and reliable data to inform policies and their implementation within a relevant timeframe. It requires that the population can rely on the health system, and decision-makers can rely on the data. To understand the process and challenges of such an integrated response in an under-resourced rural African setting, we present the COVID-19 strategy in Ifanadiana District, where a partnership between Malagasy Ministry of Public Health (MoPH) and non-governmental organizations integrates prevention, diagnosis, surveillance, and treatment, in the context of a model health system. These efforts touch every level of the health system in the district-community, primary care centers, hospital-including the establishment of the only RT-PCR lab for SARS-CoV-2 testing outside of the capital. Starting in March of 2021, a second wave of COVID-19 occurred in Madagascar, but there remain fewer cases in Ifanadiana than for many other diseases (e.g., malaria). At the Ifanadiana District Hospital, there have been two deaths that are officially attributed to COVID-19. Here, we describe the main components and challenges of this integrated response, the broad epidemiological contours of the epidemic, and how complex data sources can be developed to address many questions of COVID-19 science. Because of data limitations, it still remains unclear how this epidemic will affect rural areas of Madagascar and other developing countries where health system utilization is relatively low and there is limited capacity to diagnose and treat COVID-19 patients. Widespread population based seroprevalence studies are being implemented in Ifanadiana to inform the COVID-19 response strategy as health systems must simultaneously manage perennial and endemic disease threats.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Megan B. Murray
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, United States
| | | | - Demetrice Jordan
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, United States
| | - Mark A. Krasnow
- Centre Valbio, Ranomafana, Madagascar
- Department of Biochemistry, Stanford University, Stanford, CA, United States
| | - Patricia C. Wright
- Centre Valbio, Ranomafana, Madagascar
- Institute for the Conservation of Tropical Environments, Stony Brook University, Stony Brook, NY, United States
- Department of Anthropology, Stony Brook University, Stony Brook, NY, United States
| | - Thomas R. Gillespie
- Centre Valbio, Ranomafana, Madagascar
- Department of Environmental Sciences and Program in Population Biology, Ecology, and Evolutionary Biology, Emory University, Atlanta, GA, United States
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States
| | | | | | - Michelle V. Evans
- Odum School of Ecology and Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA, United States
| | - John M. Drake
- Odum School of Ecology and Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA, United States
| | - Calistus N. Ngonghala
- Department of Mathematics, University of Florida, Gainesville, FL, United States
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, United States
- Center for African Studies, University of Florida, Gainesville, FL, United States
| | - Michael L. Rich
- PIVOT NGO, Ranomafana, Madagascar
- Brigham and Women's Hospital, Boston, MA, United States
- Partners in Health, Boston, MA, United States
| | - Stephen J. Popper
- PIVOT NGO, Ranomafana, Madagascar
- Division of Infectious Diseases and Vaccinology, School of Public Health, University of California, Berkeley, Berkeley, CA, United States
| | - Ann C. Miller
- PIVOT NGO, Ranomafana, Madagascar
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, United States
| | | | | | | | | | | | | | | | | | - Andres Garchitorena
- PIVOT NGO, Ranomafana, Madagascar
- MIVEGEC, Université de Montpellier, CNRS, IRD, Montpellier, France
| | - Julio Rakotonirina
- Faculty of Medicine, University of Antananarivo, Antananarivo, Madagascar
| | - Alishya Mayfield
- PIVOT NGO, Ranomafana, Madagascar
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, United States
- Brigham and Women's Hospital, Boston, MA, United States
| | - Karen E. Finnegan
- PIVOT NGO, Ranomafana, Madagascar
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, United States
| | - Matthew H. Bonds
- PIVOT NGO, Ranomafana, Madagascar
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, United States
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26
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Evans MV, Drake JM, Jones L, Murdock CC. Assessing temperature-dependent competition between two invasive mosquito species. Ecol Appl 2021; 31:e02334. [PMID: 33772946 DOI: 10.1002/eap.2334] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 12/10/2020] [Accepted: 01/14/2021] [Indexed: 06/12/2023]
Abstract
Invasive mosquitoes are expanding their ranges into new geographic areas and interacting with resident mosquito species. Understanding how novel interactions can affect mosquito population dynamics is necessary to predict transmission risk at invasion fronts. Mosquito life-history traits are extremely sensitive to temperature, and this can lead to temperature-dependent competition between competing invasive mosquito species. We explored temperature-dependent competition between Aedes aegypti and Anopheles stephensi, two invasive mosquito species whose distributions overlap in India, the Middle East, and North Africa, where An. stephensi is currently expanding into the endemic range of Ae. aegypti. We followed mosquito cohorts raised at different intraspecific and interspecific densities across five temperatures (16-32°C) to measure traits relevant for population growth and to estimate species' per capita growth rates. We then used these growth rates to derive each species' competitive ability at each temperature. We find strong evidence for asymmetric competition at all temperatures, with Ae. aegypti emerging as the dominant competitor. This was primarily because of differences in larval survival and development times across all temperatures that resulted in a higher estimated intrinsic growth rate and competitive tolerance estimate for Ae. aegypti compared to An. stephensi. The spread of An. stephensi into the African continent could lead to urban transmission of malaria, an otherwise rural disease, increasing the human population at risk and complicating malaria elimination efforts. Competition has resulted in habitat segregation of other invasive mosquito species, and our results suggest that it may play a role in determining the distribution of An. stephensi across its invasive range.
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Affiliation(s)
- Michelle V Evans
- Odum School of Ecology, University of Georgia, 140 E Green St., Athens, Georgia, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, 203 DW Brooks Dr, Athens, Georgia, 30602, USA
| | - John M Drake
- Odum School of Ecology, University of Georgia, 140 E Green St., Athens, Georgia, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, 203 DW Brooks Dr, Athens, Georgia, 30602, USA
| | - Lindsey Jones
- Department of Biology, Albany State University, 504 College Dr., Albany, Georgia, 31705, USA
| | - Courtney C Murdock
- Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, 501 DW Brooks Dr, Athens, Georgia, 30602, USA
- Department of Entomology, College of Agricultural and Life Sciences, Cornell University, 2126 Comstock Hall, Ithaca, New York, 14853, USA
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27
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Abstract
BACKGROUND During outbreaks of emerging and re-emerging infections, the lack of effective drugs and vaccines increases reliance on non-pharmacologic public health interventions and behavior change to limit human-to-human transmission. Interventions that increase the speed with which infected individuals remove themselves from the susceptible population are paramount, particularly isolation and hospitalization. Ebola virus disease (EVD), Severe Acute Respiratory Syndrome (SARS), and Middle East Respiratory Syndrome (MERS) are zoonotic viruses that have caused significant recent outbreaks with sustained human-to-human transmission. METHODS This investigation quantified changing mean removal rates (MRR) and days from symptom onset to hospitalization (DSOH) of infected individuals from the population in seven different outbreaks of EVD, SARS, and MERS, to test for statistically significant differences in these metrics between outbreaks. RESULTS We found that epidemic week and viral serial interval were correlated with the speed with which populations developed and maintained health behaviors in each outbreak. CONCLUSIONS These findings highlight intrinsic population-level changes in isolation rates in multiple epidemics of three zoonotic infections with established human-to-human transmission and significant morbidity and mortality. These data are particularly useful for disease modelers seeking to forecast the spread of emerging pathogens.
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Affiliation(s)
- Evans K Lodge
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC, 27599, USA.
- School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
| | - Annakate M Schatz
- Odum School of Ecology and Center for Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA
| | - John M Drake
- Odum School of Ecology and Center for Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA
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Sánchez CA, Venkatachalam-Vaz J, Drake JM. Spillover of zoonotic pathogens: A review of reviews. Zoonoses Public Health 2021; 68:563-577. [PMID: 34018336 DOI: 10.1111/zph.12846] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [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: 11/24/2020] [Revised: 02/22/2021] [Accepted: 04/03/2021] [Indexed: 11/30/2022]
Abstract
Zoonotic spillover and subsequent disease emergence cause significant, long-lasting impacts on our social, economic, environmental and political systems. Identifying and averting spillover transmission is crucial for preventing outbreaks and mitigating infectious disease burdens. Investigating the processes that lead to spillover fundamentally involves interactions between animals, humans, pathogens and the environments they inhabit. Accordingly, it is recognized that transdisciplinary approaches provide a more holistic understanding of spillover phenomena. To characterize the discourse about spillover within and between disciplines, we conducted a review of review papers about spillover from multiple disciplines. We systematically searched and screened literature from several databases to identify a corpus of review papers from ten academic disciplines. We performed qualitative content analysis on text where authors described either a spillover pathway, or a conceptual gap in spillover theory. Cluster analysis of pathway data identified nine major spillover processes discussed in the review literature. We summarized the main features of each process, how different disciplines contributed to them, and identified specialist and generalist disciplines based on the breadth of processes they studied. Network analyses showed strong similarities between concepts reviewed by 'One Health' disciplines (e.g. Veterinary Science & Animal Health, Public Health & Medicine, Ecology & Evolution, Environmental Science), which had broad conceptual scope and were well-connected to other disciplines. By contrast, awas focused on processes that are relatively overlooked by other disciplines, especially those involving food behaviour and livestock husbandry practices. Virology and Cellular & Molecular Biology were narrower in scope, primarily focusing on concepts related to adaption and evolution of zoonotic viruses. Finally, we identified priority areas for future research into zoonotic spillover by studying the gap data.
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Affiliation(s)
- Cecilia A Sánchez
- Odum School of Ecology, University of Georgia, Athens, GA, USA.,Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA
| | - Joy Venkatachalam-Vaz
- Odum School of Ecology, University of Georgia, Athens, GA, USA.,Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA
| | - John M Drake
- Odum School of Ecology, University of Georgia, Athens, GA, USA.,Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA
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Drake JM, Dahlin K, Rohani P, Handel A. Five approaches to the suppression of SARS-CoV-2 without intensive social distancing. Proc Biol Sci 2021; 288:20203074. [PMID: 33906405 PMCID: PMC8080008 DOI: 10.1098/rspb.2020.3074] [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] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 03/27/2021] [Indexed: 02/06/2023] Open
Abstract
Initial efforts to mitigate transmission of SARS-CoV-2 relied on intensive social distancing measures such as school and workplace closures, shelter-in-place orders and prohibitions on the gathering of people. Other non-pharmaceutical interventions for suppressing transmission include active case finding, contact tracing, quarantine, immunity or health certification, and a wide range of personal protective measures. Here we investigate the potential effectiveness of these alternative approaches to suppression. We introduce a conceptual framework represented by two mathematical models that differ in strategy. We find both strategies may be effective, although both require extensive testing and work within a relatively narrow range of conditions. Generalized protective measures such as wearing face masks, improved hygiene and local reductions in density are found to significantly increase the effectiveness of targeted interventions.
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Affiliation(s)
- John M. Drake
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
| | - Kyle Dahlin
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
| | - Pejman Rohani
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
| | - Andreas Handel
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
- College of Public Health, Epidemiology and Biostatistics, University of Georgia, Athens, GA 30602, USA
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30
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Evans MV, Bonds MH, Cordier LF, Drake JM, Ihantamalala F, Haruna J, Miller AC, Murdock CC, Randriamanambtsoa M, Raza-Fanomezanjanahary EM, Razafinjato BR, Garchitorena AC. Socio-demographic, not environmental, risk factors explain fine-scale spatial patterns of diarrhoeal disease in Ifanadiana, rural Madagascar. Proc Biol Sci 2021; 288:20202501. [PMID: 33653145 PMCID: PMC7934917 DOI: 10.1098/rspb.2020.2501] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
Precision health mapping is a technique that uses spatial relationships between socio-ecological variables and disease to map the spatial distribution of disease, particularly for diseases with strong environmental signatures, such as diarrhoeal disease (DD). While some studies use GPS-tagged location data, other precision health mapping efforts rely heavily on data collected at coarse-spatial scales and may not produce operationally relevant predictions at fine enough spatio-temporal scales to inform local health programmes. We use two fine-scale health datasets collected in a rural district of Madagascar to identify socio-ecological covariates associated with childhood DD. We constructed generalized linear mixed models including socio-demographic, climatic and landcover variables and estimated variable importance via multi-model inference. We find that socio-demographic variables, and not environmental variables, are strong predictors of the spatial distribution of disease risk at both individual and commune-level (cluster of villages) spatial scales. Climatic variables predicted strong seasonality in DD, with the highest incidence in colder, drier months, but did not explain spatial patterns. Interestingly, the occurrence of a national holiday was highly predictive of increased DD incidence, highlighting the need for including cultural factors in modelling efforts. Our findings suggest that precision health mapping efforts that do not include socio-demographic covariates may have reduced explanatory power at the local scale. More research is needed to better define the set of conditions under which the application of precision health mapping can be operationally useful to local public health professionals.
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Affiliation(s)
- Michelle V Evans
- Odum School of Ecology, University of Georgia, Athens, GA, USA.,Center for Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA
| | - Matthew H Bonds
- Department of Global Health and Social Medicine, Blavatnik Institute at Harvard Medical School, Boston, MA, USA.,PIVOT, Ranomafana, Madagascar.,PIVOT, Boston, MA, USA
| | | | - John M Drake
- Odum School of Ecology, University of Georgia, Athens, GA, USA.,Center for Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA
| | - Felana Ihantamalala
- Department of Global Health and Social Medicine, Blavatnik Institute at Harvard Medical School, Boston, MA, USA.,PIVOT, Ranomafana, Madagascar.,PIVOT, Boston, MA, USA
| | - Justin Haruna
- PIVOT, Ranomafana, Madagascar.,PIVOT, Boston, MA, USA
| | - Ann C Miller
- Department of Global Health and Social Medicine, Blavatnik Institute at Harvard Medical School, Boston, MA, USA
| | - Courtney C Murdock
- Odum School of Ecology, University of Georgia, Athens, GA, USA.,Center for Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA.,Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, GA, USA.,Department of Entomology, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, USA
| | | | | | | | - Andres C Garchitorena
- PIVOT, Ranomafana, Madagascar.,PIVOT, Boston, MA, USA.,MIVEGEC, Univ. Montpellier, CNRS, IRD, Montpellier, France
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31
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Hackett EJ, Leahey E, Parker JN, Rafols I, Hampton SE, Corte U, Chavarro D, Drake JM, Penders B, Sheble L, Vermeulen N, Vision TJ. Do synthesis centers synthesize? A semantic analysis of topical diversity in research. Res Policy 2021; 50:104069. [PMID: 33390628 PMCID: PMC7695893 DOI: 10.1016/j.respol.2020.104069] [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] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 11/26/2019] [Accepted: 07/01/2020] [Indexed: 11/18/2022]
Abstract
Synthesis centers are a form of scientific organization that catalyzes and supports research that integrates diverse theories, methods and data across spatial or temporal scales to increase the generality, parsimony, applicability, or empirical soundness of scientific explanations. Synthesis working groups are a distinctive form of scientific collaboration that produce consequential, high-impact publications. But no one has asked if synthesis working groups synthesize: are their publications substantially more diverse than others, and if so, in what ways and with what effect? We investigate these questions by using Latent Dirichlet Analysis to compare the topical diversity of papers published by synthesis center collaborations with that of papers in a reference corpus. Topical diversity was operationalized and measured in several ways, both to reflect aggregate diversity and to emphasize particular aspects of diversity (such as variety, evenness, and balance). Synthesis center publications have greater topical variety and evenness, but less disparity, than do papers in the reference corpus. The influence of synthesis center origins on aspects of diversity is only partly mediated by the size and heterogeneity of collaborations: when taking into account the numbers of authors, distinct institutions, and references, synthesis center origins retain a significant direct effect on diversity measures. Controlling for the size and heterogeneity of collaborative groups, synthesis center origins and diversity measures significantly influence the visibility of publications, as indicated by citation measures. We conclude by suggesting social processes within collaborations that might account for the observed effects, by inviting further exploration of what this novel textual analysis approach might reveal about interdisciplinary research, and by offering some practical implications of our results.
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Affiliation(s)
- Edward J. Hackett
- School of Human Evolution and Social Change, Arizona State University and Vice Provost for Research and Professor, Heller School for Social Policy and Management, Brandeis University
| | | | - John N. Parker
- Department of Sociology and Geography, University of Oslo
| | - Ismael Rafols
- Centre for Science and Technology Studies, Leiden University
| | - Stephanie E. Hampton
- Center for Environmental Research, Education and Outreach, Washington State University
| | - Ugo Corte
- Department of Media and Social Sciences, University of Stavanger
| | | | - John M. Drake
- Odum School of Ecology and Center for the Study of Infectious Diseases, University of Georgia
| | - Bart Penders
- Department of Health, Ethics, and Society, Care and Public Health Research Institute (CAPHRI), Maastricht University
| | - Laura Sheble
- School of Information Sciences, Wayne State University, Duke Network Analysis Center, Social Science Research Institute (SSRI), Duke University
| | - Niki Vermeulen
- Science, Technology, and Innovation Studies, University of Edinburgh
| | - Todd J. Vision
- Department of Biology and School of Information and Library Sciences, University of North Carolina at Chapel Hill
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Evans MV, Garchitorena A, Rakotonanahary RJL, Drake JM, Andriamihaja B, Rajaonarifara E, Ngonghala CN, Roche B, Bonds MH, Rakotonirina J. Reconciling model predictions with low reported cases of COVID-19 in Sub-Saharan Africa: insights from Madagascar. Glob Health Action 2020; 13:1816044. [PMID: 33012269 PMCID: PMC7580764 DOI: 10.1080/16549716.2020.1816044] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [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] [Received: 07/17/2020] [Accepted: 08/24/2020] [Indexed: 12/14/2022] Open
Abstract
COVID-19 has wreaked havoc globally with particular concerns for sub-Saharan Africa (SSA), where models suggest that the majority of the population will become infected. Conventional wisdom suggests that the continent will bear a higher burden of COVID-19 for the same reasons it suffers from other infectious diseases: ecology, socio-economic conditions, lack of water and sanitation infrastructure, and weak health systems. However, so far SSA has reported lower incidence and fatalities compared to the predictions of standard models and the experience of other regions of the world. There are three leading explanations, each with different implications for the final epidemic burden: (1) low case detection, (2) differences in epidemiology (e.g. low R 0 ), and (3) policy interventions. The low number of cases have led some SSA governments to relaxing these policy interventions. Will this result in a resurgence of cases? To understand how to interpret the lower-than-expected COVID-19 case data in Madagascar, we use a simple age-structured model to explore each of these explanations and predict the epidemic impact associated with them. We show that the incidence of COVID-19 cases as of July 2020 can be explained by any combination of the late introduction of first imported cases, early implementation of non-pharmaceutical interventions (NPIs), and low case detection rates. We then re-evaluate these findings in the context of the COVID-19 epidemic in Madagascar through August 2020. This analysis reinforces that Madagascar, along with other countries in SSA, remains at risk of a growing health crisis. If NPIs remain enforced, up to 50,000 lives may be saved. Even with NPIs, without vaccines and new therapies, COVID-19 could infect up to 30% of the population, making it the largest public health threat in Madagascar for the coming year, hence the importance of clinical trials and continually improving access to healthcare.
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Affiliation(s)
- Michelle V. Evans
- Odum School of Ecology and Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA
| | - Andres Garchitorena
- MIVEGEC, Ecole Pierre Louis de Santé Publique, Université de Montpellier, CNRS, IRD, Montpellier, France
- PIVOT, Ranomafana, Madagascar
| | | | - John M. Drake
- Odum School of Ecology and Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA
| | - Benjamin Andriamihaja
- PIVOT, Ranomafana, Madagascar
- Madagascar Institut pour la Conservation des Ecosystèmes Tropicaux, Antananarivo, Madagascar
| | - Elinambinina Rajaonarifara
- MIVEGEC, Ecole Pierre Louis de Santé Publique, Université de Montpellier, CNRS, IRD, Montpellier, France
- PIVOT, Ranomafana, Madagascar
- Sorbonne Universite, Paris, France
| | - Calistus N. Ngonghala
- Department of Mathematics and Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
| | - Benjamin Roche
- MIVEGEC, Ecole Pierre Louis de Santé Publique, Université de Montpellier, CNRS, IRD, Montpellier, France
- IRD, Sorbonne Université, UMMISCO, Bondy, France
- Universidad Nacional Autónoma de México, Ciudad de México, México
| | - Matthew H. Bonds
- PIVOT, Ranomafana, Madagascar
- Harvard Medical School, Boston, MA, USA
| | - Julio Rakotonirina
- Faculty of Medicine, University of Antananarivo, Antananarivo, Madagascar
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Abstract
The majority of known early warning indicators of critical transitions rely on asymptotic resilience and critical slowing down. In continuous systems, critical slowing down is mathematically described by a decrease in magnitude of the dominant eigenvalue of the Jacobian matrix on the approach to a critical transition. Here, we show that measures of transient dynamics, specifically, reactivity and the maximum of the amplification envelope, also change systematically as a bifurcation is approached in an important class of models for epidemics of infectious diseases. Furthermore, we introduce indicators designed to detect trends in these measures and find that they reliably classify time series of case notifications simulated from stochastic models according to levels of vaccine uptake. Greater attention should be focused on the potential for systems to exhibit transient amplification of perturbations as a critical threshold is approached, and should be considered when searching for generic leading indicators of tipping points. Awareness of this phenomenon will enrich understanding of the dynamics of complex systems on the verge of a critical transition.
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Affiliation(s)
- Suzanne M O'Regan
- Department of Mathematics and Statistics, Marteena Hall, 1601 E. Market St., North Carolina A&T State University, Greensboro, NC 27411 USA
| | - Eamon B O'Dea
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA.,Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
| | - Pejman Rohani
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA.,Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA.,Department of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
| | - John M Drake
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA.,Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
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Abstract
Initial efforts to mitigate transmission of SARS-CoV-2 relied on intensive social distancing measures such as school and workplace closures, shelter-in-place orders, and prohibitions on the gathering of people. Other non-pharmaceutical interventions for suppressing transmission include active case finding, contact tracing, quarantine, immunity or health certification, and a wide range of personal protective measures. Here we investigate the potential effectiveness of these alternative approaches to suppression. We introduce a conceptual framework represented by two mathematical models that differ in strategy. We find both strategies may be effective, although both require extensive testing and work within a relatively narrow range of conditions. Generalized protective measures such as wearing face masks, improved hygiene, and local reductions in density are found to significantly increase the effectiveness of targeted interventions.
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35
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Han BA, O'Regan SM, Paul Schmidt J, Drake JM. Integrating data mining and transmission theory in the ecology of infectious diseases. Ecol Lett 2020; 23:1178-1188. [PMID: 32441459 PMCID: PMC7384120 DOI: 10.1111/ele.13520] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 01/21/2020] [Accepted: 03/27/2020] [Indexed: 01/07/2023]
Abstract
Our understanding of ecological processes is built on patterns inferred from data. Applying modern analytical tools such as machine learning to increasingly high dimensional data offers the potential to expand our perspectives on these processes, shedding new light on complex ecological phenomena such as pathogen transmission in wild populations. Here, we propose a novel approach that combines data mining with theoretical models of disease dynamics. Using rodents as an example, we incorporate statistical differences in the life history features of zoonotic reservoir hosts into pathogen transmission models, enabling us to bound the range of dynamical phenomena associated with hosts, based on their traits. We then test for associations between equilibrium prevalence, a key epidemiological metric and data on human outbreaks of rodent-borne zoonoses, identifying matches between empirical evidence and theoretical predictions of transmission dynamics. We show how this framework can be generalized to other systems through a rubric of disease models and parameters that can be derived from empirical data. By linking life history components directly to their effects on disease dynamics, our mining-modelling approach integrates machine learning and theoretical models to explore mechanisms in the macroecology of pathogen transmission and their consequences for spillover infection to humans.
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Affiliation(s)
- Barbara A Han
- Cary Institute of Ecosystem Studies, Box AB Millbrook, NY, 12571, USA
| | - Suzanne M O'Regan
- Department of Mathematics and Statistics, North Carolina A&T State University, 1601 E. Market St., Greensboro, NC, 27411, USA
| | - John Paul Schmidt
- Odum School of Ecology, University of Georgia, 140 E. Green St., Athens, GA, 30602, USA.,Center for the Ecology of Infectious Diseases, University of Georgia, 203 D.W. Brooks Drive, Athens, GA, 30602, USA
| | - John M Drake
- Odum School of Ecology, University of Georgia, 140 E. Green St., Athens, GA, 30602, USA.,Center for the Ecology of Infectious Diseases, University of Georgia, 203 D.W. Brooks Drive, Athens, GA, 30602, USA
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Walker JW, Kittur N, Binder S, Castleman JD, Drake JM, Campbell CH, King CH, Colley DG. Environmental Predictors of Schistosomiasis Persistent Hotspots following Mass Treatment with Praziquantel. Am J Trop Med Hyg 2020; 102:328-338. [PMID: 31889506 PMCID: PMC7008331 DOI: 10.4269/ajtmh.19-0658] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [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] [Indexed: 12/31/2022] Open
Abstract
Schistosomiasis control programs rely heavily on mass drug administration (MDA) campaigns with praziquantel for preventative chemotherapy. Areas where the prevalence and/or intensity of schistosomiasis infection remains high even after several rounds of treatment, termed "persistent hotspots" (PHSs), have been identified in trials of MDA effectiveness conducted by the Schistosomiasis Consortium for Operational Research and Evaluation (SCORE) in Kenya, Mozambique, Tanzania, and Côte d'Ivoire. In this analysis, we apply a previously developed set of criteria to classify the PHS status of 531 study villages from five SCORE trials. We then fit logistic regression models to data from SCORE and publically available georeferenced datasets to evaluate the influence of local environmental and population features, pre-intervention infection burden, and treatment scheduling on PHS status in each trial. The frequency of PHS in individual trials ranged from 35.3% to 71.6% in study villages. Significant relationships between PHS status and MDA frequency, distance to freshwater, rainfall, baseline schistosomiasis burden, elevation, land cover type, and village remoteness were each observed in at least one trial, although the strength and direction of these relationships was not always consistent among study sites. These findings suggest that PHSs are driven in part by environmental conditions that modify the risk and frequency of reinfection.
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Affiliation(s)
- Joseph W Walker
- Center for the Ecology of Infectious Disease, University of Georgia, Athens, Georgia.,University of Georgia College of Public Health, Athens, Georgia
| | - Nupur Kittur
- Schistosomiasis Consortium for Operational Research and Evaluation (SCORE), Center for Tropical and Emerging Global Diseases (CTEGD), University of Georgia, Athens, Georgia
| | - Sue Binder
- Schistosomiasis Consortium for Operational Research and Evaluation (SCORE), Center for Tropical and Emerging Global Diseases (CTEGD), University of Georgia, Athens, Georgia
| | - Jennifer D Castleman
- Schistosomiasis Consortium for Operational Research and Evaluation (SCORE), Center for Tropical and Emerging Global Diseases (CTEGD), University of Georgia, Athens, Georgia
| | - John M Drake
- Odum School of Ecology, University of Georgia, Athens, Georgia.,Center for the Ecology of Infectious Disease, University of Georgia, Athens, Georgia
| | - Carl H Campbell
- Schistosomiasis Consortium for Operational Research and Evaluation (SCORE), Center for Tropical and Emerging Global Diseases (CTEGD), University of Georgia, Athens, Georgia
| | - Charles H King
- Center for Global Health and Diseases, Case Western Reserve University School of Medicine, Cleveland, Ohio.,Schistosomiasis Consortium for Operational Research and Evaluation (SCORE), Center for Tropical and Emerging Global Diseases (CTEGD), University of Georgia, Athens, Georgia
| | - Daniel G Colley
- Department of Microbiology, University of Georgia, Athens, Georgia.,Schistosomiasis Consortium for Operational Research and Evaluation (SCORE), Center for Tropical and Emerging Global Diseases (CTEGD), University of Georgia, Athens, Georgia
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Drake JM, O’Regan SM, Dakos V, Kéfi S, Rohani P. Alternative stable states, tipping points, and early warning signals of ecological transitions. THEOR ECOL-NETH 2020. [DOI: 10.1093/oso/9780198824282.003.0015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Ecological systems are prone to dramatic shifts between alternative stable states. In reality, these shifts are often caused by slow forces external to the system that eventually push it over a tipping point. Theory predicts that when ecological systems are brought close to a tipping point, the dynamical feedback intrinsic to the system interact with intrinsic noise and extrinsic perturbations in characteristic ways. The resulting phenomena thus serve as “early warning signals” for shifts such as population collapse. In this chapter, we review the basic (qualitative) theory of such systems. We then illustrate the main ideas with a series of models that both represent fundamental ecological ideas (e.g. density-dependence) and are amenable to mathematical analysis. These analyses provide theoretical predictions about the nature of measurable fluctuations in the vicinity of a tipping point. We conclude with a review of empirical evidence from laboratory microcosms, field manipulations, and observational studies.
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Bock SL, Lowers RH, Rainwater TR, Stolen E, Drake JM, Wilkinson PM, Weiss S, Back B, Guillette L, Parrott BB. Spatial and temporal variation in nest temperatures forecasts sex ratio skews in a crocodilian with environmental sex determination. Proc Biol Sci 2020; 287:20200210. [PMID: 32345164 DOI: 10.1098/rspb.2020.0210] [Citation(s) in RCA: 13] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Species displaying temperature-dependent sex determination (TSD) are especially vulnerable to the effects of a rapidly changing global climate due to their profound sensitivity to thermal cues during development. Predicting the consequences of climate change for these species, including skewed offspring sex ratios, depends on understanding how climatic factors interface with features of maternal nesting behaviour to shape the developmental environment. Here, we measure thermal profiles in 86 nests at two geographically distinct sites in the northern and southern regions of the American alligator's (Alligator mississippiensis) geographical range, and examine the influence of both climatic factors and maternally driven nest characteristics on nest temperature variation. Changes in daily maximum air temperatures drive annual trends in nest temperatures, while variation in individual nest temperatures is also related to local habitat factors and microclimate characteristics. Without any compensatory nesting behaviours, nest temperatures are projected to increase by 1.6-3.7°C by the year 2100, and these changes are predicted to have dramatic consequences for offspring sex ratios. Exact sex ratio outcomes vary widely depending on site and emission scenario as a function of the unique temperature-by-sex reaction norm exhibited by all crocodilians. By revealing the ecological drivers of nest temperature variation in the American alligator, this study provides important insights into the potential consequences of climate change for crocodilian species, many of which are already threatened by extinction.
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Affiliation(s)
- Samantha L Bock
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA.,Savannah River Ecology Laboratory, Aiken, SC 29802, USA
| | - Russell H Lowers
- Integrated Mission Support Services, John F. Kennedy Space Center, FL 32899, USA
| | - Thomas R Rainwater
- Tom Yawkey Wildlife Center, Georgetown, SC 29440, USA.,Belle W. Baruch Institute of Coastal Ecology & Forest Science, Clemson University, Georgetown, SC 29442, USA
| | - Eric Stolen
- Integrated Mission Support Services, John F. Kennedy Space Center, FL 32899, USA
| | - John M Drake
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA.,Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
| | | | - Stephanie Weiss
- Integrated Mission Support Services, John F. Kennedy Space Center, FL 32899, USA
| | - Brenton Back
- Integrated Mission Support Services, John F. Kennedy Space Center, FL 32899, USA
| | - Louis Guillette
- Medical University of South Carolina, Hollings Marine Laboratory, Charleston, SC 29412, USA
| | - Benjamin B Parrott
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA.,Savannah River Ecology Laboratory, Aiken, SC 29802, USA
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39
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Evans MV, Hintz CW, Jones L, Shiau J, Solano N, Drake JM, Murdock CC. Microclimate and Larval Habitat Density Predict Adult Aedes albopictus Abundance in Urban Areas. Am J Trop Med Hyg 2020; 101:362-370. [PMID: 31190685 PMCID: PMC6685558 DOI: 10.4269/ajtmh.19-0220] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [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] [Indexed: 01/16/2023] Open
Abstract
The Asian tiger mosquito, Aedes albopictus, transmits several arboviruses of public health importance, including chikungunya and dengue. Since its introduction to the United States in 1985, the species has invaded more than 40 states, including temperate areas not previously at risk of Aedes-transmitted arboviruses. Mathematical models incorporate climatic variables in predictions of site-specific Ae. albopictus abundances to identify human populations at risk of disease. However, these models rely on coarse resolutions of environmental data that may not accurately represent the climatic profile experienced by mosquitoes in the field, particularly in climatically heterogeneous urban areas. In this study, we pair field surveys of larval and adult Ae. albopictus mosquitoes with site-specific microclimate data across a range of land use types to investigate the relationships between microclimate, density of larval habitat, and adult mosquito abundance and determine whether these relationships change across an urban gradient. We find no evidence for a difference in larval habitat density or adult abundance between rural, suburban, and urban land classes. Adult abundance increases with increasing larval habitat density, which itself is dependent on microclimate. Adult abundance is strongly explained by microclimate variables, demonstrating that theoretically derived, laboratory-parameterized relationships in ectotherm physiology apply to the field. Our results support the continued use of temperature-dependent models to predict Ae. albopictus abundance in urban areas.
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Affiliation(s)
- Michelle V Evans
- Center for Ecology of Infectious Diseases, University of Georgia, Athens, Georgia.,Odum School of Ecology, University of Georgia, Athens, Georgia
| | - Carl W Hintz
- Department of Applied Ecology, North Carolina State University, Raleigh, North Carolina
| | - Lindsey Jones
- Department of Biology, Albany State University, Albany, Georgia
| | - Justine Shiau
- Department of Infectious Disease, University of Georgia, Athens, Georgia
| | - Nicole Solano
- Center for Ecology of Infectious Diseases, University of Georgia, Athens, Georgia.,Odum School of Ecology, University of Georgia, Athens, Georgia
| | - John M Drake
- Center for Ecology of Infectious Diseases, University of Georgia, Athens, Georgia.,Odum School of Ecology, University of Georgia, Athens, Georgia
| | - Courtney C Murdock
- Department of Infectious Disease, University of Georgia, Athens, Georgia.,Center for Ecology of Infectious Diseases, University of Georgia, Athens, Georgia.,Odum School of Ecology, University of Georgia, Athens, Georgia
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40
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Abstract
Campaigns to eliminate infectious diseases could be greatly aided by methods for providing early warning signals of resurgence. Theory predicts that as a disease transmission system undergoes a transition from stability at the disease-free equilibrium to sustained transmission, it will exhibit characteristic behaviours known as critical slowing down, referring to the speed at which fluctuations in the number of cases are dampened, for instance the extinction of a local transmission chain after infection from an imported case. These phenomena include increases in several summary statistics, including lag-1 autocorrelation, variance and the first difference of variance. Here, we report the first empirical test of this prediction during the resurgence of malaria in Kericho, Kenya. For 10 summary statistics, we measured the approach to criticality in a rolling window to quantify the size of effect and directions. Nine of the statistics increased as predicted and variance, the first difference of variance, autocovariance, lag-1 autocorrelation and decay time returned early warning signals of critical slowing down based on permutation tests. These results show that time series of disease incidence collected through ordinary surveillance activities may exhibit characteristic signatures prior to an outbreak, a phenomenon that may be quite general among infectious disease systems.
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Affiliation(s)
- Mallory J. Harris
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA
- Biology Department, Stanford University, 371 Serra Mall, Stanford, CA, USA
| | - Simon I. Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98121, USA
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA 98121, USA
| | - John M. Drake
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
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41
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Brett T, Ajelli M, Liu QH, Krauland MG, Grefenstette JJ, van Panhuis WG, Vespignani A, Drake JM, Rohani P. Detecting critical slowing down in high-dimensional epidemiological systems. PLoS Comput Biol 2020; 16:e1007679. [PMID: 32150536 PMCID: PMC7082051 DOI: 10.1371/journal.pcbi.1007679] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [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: 06/04/2019] [Revised: 03/19/2020] [Accepted: 01/23/2020] [Indexed: 01/05/2023] Open
Abstract
Despite medical advances, the emergence and re-emergence of infectious diseases continue to pose a public health threat. Low-dimensional epidemiological models predict that epidemic transitions are preceded by the phenomenon of critical slowing down (CSD). This has raised the possibility of anticipating disease (re-)emergence using CSD-based early-warning signals (EWS), which are statistical moments estimated from time series data. For EWS to be useful at detecting future (re-)emergence, CSD needs to be a generic (model-independent) feature of epidemiological dynamics irrespective of system complexity. Currently, it is unclear whether the predictions of CSD-derived from simple, low-dimensional systems-pertain to real systems, which are high-dimensional. To assess the generality of CSD, we carried out a simulation study of a hierarchy of models, with increasing structural complexity and dimensionality, for a measles-like infectious disease. Our five models included: i) a nonseasonal homogeneous Susceptible-Exposed-Infectious-Recovered (SEIR) model, ii) a homogeneous SEIR model with seasonality in transmission, iii) an age-structured SEIR model, iv) a multiplex network-based model (Mplex) and v) an agent-based simulator (FRED). All models were parameterised to have a herd-immunity immunization threshold of around 90% coverage, and underwent a linear decrease in vaccine uptake, from 92% to 70% over 15 years. We found evidence of CSD prior to disease re-emergence in all models. We also evaluated the performance of seven EWS: the autocorrelation, coefficient of variation, index of dispersion, kurtosis, mean, skewness, variance. Performance was scored using the Area Under the ROC Curve (AUC) statistic. The best performing EWS were the mean and variance, with AUC > 0.75 one year before the estimated transition time. These two, along with the autocorrelation and index of dispersion, are promising candidate EWS for detecting disease emergence.
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Affiliation(s)
- Tobias Brett
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Marco Ajelli
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
- Bruno Kessler Foundation, Trento, Italy
| | - Quan-Hui Liu
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
- College of Computer Science, Sichuan University, Chengdu, China
| | - Mary G. Krauland
- University of Pittsburgh, Department of Health Policy and Management, Pittsburgh, Pennsylvania, United States of America
| | - John J. Grefenstette
- University of Pittsburgh, Department of Health Policy and Management, Pittsburgh, Pennsylvania, United States of America
| | - Willem G. van Panhuis
- University of Pittsburgh, Department of Epidemiology, Pittsburgh, Pennsylvania, United States of America
- University of Pittsburgh, Department of Biomedical Informatics, Pittsburgh, Pennsylvania, United States of America
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
- ISI Foundation, Turin, Italy
| | - John M. Drake
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Pejman Rohani
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
- Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, Georgia, United States of America
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42
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Abstract
The epidemic threshold of the susceptible-infected-recovered model is a boundary separating parameters that permit epidemics from those that do not. This threshold corresponds to parameters where the system's equilibrium becomes unstable. Consequently, we use the average rate at which deviations from the equilibrium shrink to define a distance to this threshold. However, the vital dynamics of the host population may occur slowly even when transmission is far from threshold levels. Here, we show analytically how such slow dynamics can prevent estimation of the distance to the threshold from fluctuations in the susceptible population. Although these results are exact only in the limit of long-term observation of a large system, simulations show that they still provide useful insight into systems with a range of population sizes, environmental noise and observation schemes. Having established some guidelines about when estimates are accurate, we then illustrate how multiple distance estimates can be used to estimate the rate of approach to the threshold. The estimation approach is general and may be applicable to zoonotic pathogens such as Middle East respiratory syndrome-related coronavirus (MERS-CoV) as well as vaccine-preventable diseases like measles.
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Affiliation(s)
- Eamon B O'Dea
- Department of Ecology, College of Veterinary Medicine, University of Georgia, Athens, GA, USA .,Center for the Ecology of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, GA, USA
| | - Andrew W Park
- Odum School of Ecology, College of Veterinary Medicine, University of Georgia, Athens, GA, USA.,Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, GA, USA
| | - John M Drake
- Center for the Ecology of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, GA, USA.,Odum School of Ecology, College of Veterinary Medicine, University of Georgia, Athens, GA, USA
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43
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Abstract
Much of the basic ecology of Ebolavirus remains unresolved despite accumulating disease outbreaks, viral strains and evidence of animal hosts. Because human Ebolavirus epidemics have been linked to contact with wild mammals other than bats, traits shared by species that have been infected by Ebolavirus and their phylogenetic distribution could suggest ecological mechanisms contributing to human Ebolavirus spillovers. We compiled data on Ebolavirus exposure in mammals and corresponding data on life-history traits, movement, and diet, and used boosted regression trees (BRT) to identify predictors of exposure and infection for 119 species (hereafter hosts). Mapping the phylogenetic distribution of presumptive Ebolavirus hosts reveals that they are scattered across several distinct mammal clades, but concentrated among Old World fruit bats, primates and artiodactyls. While sampling effort was the most important predictor, explaining nearly as much of the variation among hosts as traits, BRT models distinguished hosts from all other species with greater than 97% accuracy, and revealed probable Ebolavirus hosts as large-bodied, frugivorous, and with slow life histories. Provisionally, results suggest that some insectivorous bat genera, Old World monkeys and forest antelopes should receive priority in Ebolavirus survey efforts. This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’.
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Affiliation(s)
- John Paul Schmidt
- Odum School of Ecology and Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
| | - Sean Maher
- Department of Biology, Missouri State University, 901 S. National Ave, Springfield, MO 65897, USA
| | - John M Drake
- Odum School of Ecology and Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
| | - Tao Huang
- Cary Institute of Ecosystem Studies, 2801 Sharon Turnpike, Millbrook, NY 12545, USA
| | - Maxwell J Farrell
- Odum School of Ecology and Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
| | - Barbara A Han
- Cary Institute of Ecosystem Studies, 2801 Sharon Turnpike, Millbrook, NY 12545, USA
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44
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Kramer AM, Teitelbaum CS, Griffin A, Drake JM. Multiscale model of regional population decline in little brown bats due to white-nose syndrome. Ecol Evol 2019; 9:8639-8651. [PMID: 31410268 PMCID: PMC6686297 DOI: 10.1002/ece3.5405] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Accepted: 05/11/2019] [Indexed: 01/26/2023] Open
Abstract
The introduced fungal pathogen Pseudogymnoascus destructans is causing decline of several species of bats in North America, with some even at risk of extinction or extirpation. The severity of the epidemic of white-nose syndrome caused by P. destructans has prompted investigation of the transmission and virulence of infection at multiple scales, but linking these scales is necessary to quantify the mechanisms of transmission and assess population-scale declines.We built a model connecting within-hibernaculum disease dynamics of little brown bats to regional-scale dispersal, reproduction, and disease spread, including multiple plausible mechanisms of transmission.We parameterized the model using the approach of plausible parameter sets, by comparing stochastic simulation results to statistical probes from empirical data on within-hibernaculum prevalence and survival, as well as among-hibernacula spread across a region.Our results are consistent with frequency-dependent transmission between bats, support an important role of environmental transmission, and show very little effect of dispersal among colonies on metapopulation survival.The results help identify the influential parameters and largest sources of uncertainty. The model also offers a generalizable method to assess hypotheses about hibernaculum-to-hibernaculum transmission and to identify gaps in knowledge about key processes, and could be expanded to include additional mechanisms or bat species.
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Affiliation(s)
- Andrew M. Kramer
- Department of Integrative BiologyUniversity of South FloridaTampaFloridaUSA
| | | | - Ashton Griffin
- Odum School of EcologyUniversity of GeorgiaAthensGeorgiaUSA
| | - John M. Drake
- Odum School of Ecology and Center for Ecology of Infectious DiseasesUniversity of GeorgiaAthensGeorgiaUSA
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45
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Drake JM, Brett TS, Chen S, Epureanu BI, Ferrari MJ, Marty É, Miller PB, O’Dea EB, O’Regan SM, Park AW, Rohani P. The statistics of epidemic transitions. PLoS Comput Biol 2019; 15:e1006917. [PMID: 31067217 PMCID: PMC6505855 DOI: 10.1371/journal.pcbi.1006917] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Emerging and re-emerging pathogens exhibit very complex dynamics, are hard to model and difficult to predict. Their dynamics might appear intractable. However, new statistical approaches-rooted in dynamical systems and the theory of stochastic processes-have yielded insight into the dynamics of emerging and re-emerging pathogens. We argue that these approaches may lead to new methods for predicting epidemics. This perspective views pathogen emergence and re-emergence as a "critical transition," and uses the concept of noisy dynamic bifurcation to understand the relationship between the system observables and the distance to this transition. Because the system dynamics exhibit characteristic fluctuations in response to perturbations for a system in the vicinity of a critical point, we propose this information may be harnessed to develop early warning signals. Specifically, the motion of perturbations slows as the system approaches the transition.
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Affiliation(s)
- John M. Drake
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Tobias S. Brett
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Shiyang Chen
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Bogdan I. Epureanu
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
- Automotive Research Center, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Matthew J. Ferrari
- Center for Infectious Disease Dynamics, Pennsylvania State University, State College, Pennsylvania, United States of America
| | - Éric Marty
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Paige B. Miller
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Eamon B. O’Dea
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Suzanne M. O’Regan
- Department of Mathematics, North Carolina A&T State University, Greensboro, North Carolina, United States of America
| | - Andrew W. Park
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Pejman Rohani
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
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46
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Abstract
Second-order statistics such as the variance and autocorrelation can be useful indicators of the stability of randomly perturbed systems, in some cases providing early warning of an impending, dramatic change in the system’s dynamics. One specific application area of interest is the surveillance of infectious diseases. In the context of disease (re-)emergence, a goal could be to have an indicator that is informative of whether the system is approaching the epidemic threshold, a point beyond which a major outbreak becomes possible. Prior work in this area has provided some proof of this principle but has not analytically treated the effect of imperfect observation on the behavior of indicators. This work provides expected values for several moments of the number of reported cases, where reported cases follow a binomial or negative binomial distribution with a mean based on the number of deaths in a birth-death-immigration process over some reporting interval. The normalized second factorial moment and the decay time of the number of reported cases are two indicators that are insensitive to the reporting probability. Simulation is used to show how this insensitivity could be used to distinguish a trend of increased reporting from a trend of increased transmission. The simulation study also illustrates both the high variance of estimates and the possibility of reducing the variance by averaging over an ensemble of estimates from multiple time series.
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Affiliation(s)
- Eamon B. O’Dea
- Odum School of Ecology and Center for the Ecology of Infectious Diseases, University of Georgia, 140 E. Green Street, Athens, GA, 30602, USA
| | - John M. Drake
- Odum School of Ecology and Center for the Ecology of Infectious Diseases, University of Georgia, 140 E. Green Street, Athens, GA, 30602, USA
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47
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Chen S, O'Dea EB, Drake JM, Epureanu BI. Eigenvalues of the covariance matrix as early warning signals for critical transitions in ecological systems. Sci Rep 2019; 9:2572. [PMID: 30796264 PMCID: PMC6385210 DOI: 10.1038/s41598-019-38961-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Accepted: 12/27/2018] [Indexed: 11/23/2022] Open
Abstract
Many ecological systems are subject critical transitions, which are abrupt changes to contrasting states triggered by small changes in some key component of the system. Temporal early warning signals such as the variance of a time series, and spatial early warning signals such as the spatial correlation in a snapshot of the system's state, have been proposed to forecast critical transitions. However, temporal early warning signals do not take the spatial pattern into account, and past spatial indicators only examine one snapshot at a time. In this study, we propose the use of eigenvalues of the covariance matrix of multiple time series as early warning signals. We first show theoretically why these indicators may increase as the system moves closer to the critical transition. Then, we apply the method to simulated data from several spatial ecological models to demonstrate the method's applicability. This method has the advantage that it takes into account only the fluctuations of the system about its equilibrium, thus eliminating the effects of any change in equilibrium values. The eigenvector associated with the largest eigenvalue of the covariance matrix is helpful for identifying the regions that are most vulnerable to the critical transition.
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Affiliation(s)
- Shiyang Chen
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Eamon B O'Dea
- Odum School of Ecology, University of Georgia, Athens, Georgia, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, USA
| | - John M Drake
- Odum School of Ecology, University of Georgia, Athens, Georgia, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, USA
| | - Bogdan I Epureanu
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, USA.
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48
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Park AW, Farrell MJ, Schmidt JP, Huang S, Dallas TA, Pappalardo P, Drake JM, Stephens PR, Poulin R, Nunn CL, Davies TJ. Characterizing the phylogenetic specialism-generalism spectrum of mammal parasites. Proc Biol Sci 2019. [PMID: 29514973 DOI: 10.1098/rspb.2017.2613] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [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: 12/12/2022] Open
Abstract
The distribution of parasites across mammalian hosts is complex and represents a differential ability or opportunity to infect different host species. Here, we take a macroecological approach to investigate factors influencing why some parasites show a tendency to infect species widely distributed in the host phylogeny (phylogenetic generalism) while others infect only closely related hosts. Using a database on over 1400 parasite species that have been documented to infect up to 69 terrestrial mammal host species, we characterize the phylogenetic generalism of parasites using standard effect sizes for three metrics: mean pairwise phylogenetic distance (PD), maximum PD and phylogenetic aggregation. We identify a trend towards phylogenetic specialism, though statistically host relatedness is most often equivalent to that expected from a random sample of host species. Bacteria and arthropod parasites are typically the most generalist, viruses and helminths exhibit intermediate generalism, and protozoa are on average the most specialist. While viruses and helminths have similar mean pairwise PD on average, the viruses exhibit higher variation as a group. Close-contact transmission is the transmission mode most associated with specialism. Most parasites exhibiting phylogenetic aggregation (associating with discrete groups of species dispersed across the host phylogeny) are helminths and viruses.
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Affiliation(s)
- A W Park
- Odum School of Ecology, University of Georgia, 140 E. Green Street, Athens, GA 30602, USA .,Center for the Ecology of Infectious Diseases, University of Georgia, Athens, USA
| | - M J Farrell
- Department of Biology, McGill University, Montreal, Quebec, Canada H3G 0B1
| | - J P Schmidt
- Odum School of Ecology, University of Georgia, 140 E. Green Street, Athens, GA 30602, USA.,Center for the Ecology of Infectious Diseases, University of Georgia, Athens, USA
| | - S Huang
- Senckenberg Biodiversity and Climate Research Center (BiK-F), Senckenberganlage 25, D-60325 Frankfurt (Main), Germany
| | - T A Dallas
- Department of Environmental Science and Policy, University of California, One Shields Avenue, Davis, CA 95616, USA
| | - P Pappalardo
- Odum School of Ecology, University of Georgia, 140 E. Green Street, Athens, GA 30602, USA
| | - J M Drake
- Odum School of Ecology, University of Georgia, 140 E. Green Street, Athens, GA 30602, USA.,Center for the Ecology of Infectious Diseases, University of Georgia, Athens, USA
| | - P R Stephens
- Odum School of Ecology, University of Georgia, 140 E. Green Street, Athens, GA 30602, USA.,Center for the Ecology of Infectious Diseases, University of Georgia, Athens, USA
| | - R Poulin
- Department of Zoology, University of Otago, PO Box 56, Dunedin 9054, New Zealand
| | - C L Nunn
- Department of Evolutionary Anthropology and Duke Global Health Institute, Duke University, Durham, NC 27708, USA
| | - T J Davies
- Department of Botany, University of British Columbia, 6270 University Blvd., Vancouver, BC, Canada V6T 1Z4.,Department of Forest & Conservation Sciences, University of British Columbia, 6270 University Blvd., Vancouver, BC, Canada V6T 1Z4
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49
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Abstract
Effective public health research and preparedness requires an accurate understanding of which virus species possess or are at risk of developing human transmissibility. Unfortunately, our ability to identify these viruses is limited by gaps in disease surveillance and an incomplete understanding of the process of viral adaptation. By fitting boosted regression trees to data on 224 human viruses and their associated traits, we developed a model that predicts the human transmission ability of zoonotic viruses with over 84% accuracy. This model identifies several viruses that may have an undocumented capacity for transmission between humans. Viral traits that predicted human transmissibility included infection of nonhuman primates, the absence of a lipid envelope, and detection in the human nervous system and respiratory tract. This predictive model can be used to prioritize high-risk viruses for future research and surveillance, and could inform an integrated early warning system for emerging infectious diseases.
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Affiliation(s)
- Joseph W. Walker
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Barbara A. Han
- Cary Institute for Ecosystem Studies, Millbrook, New York, United States of America
| | - Isabel M. Ott
- Southeastern Cooperative Wildlife Disease Study, College of Veterinary Medicine, University of Georgia, Athens, Georgia, United States of America
| | - John M. Drake
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
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50
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Affiliation(s)
- John M. Drake
- Odum School of Ecology; University of Georgia; Athens Georgia 30602 USA
- Center for the Ecology of Infectious Diseases; University of Georgia; Athens Georgia 30602 USA
- Department of Zoology; Oxford University; Oxford OX1 3PS UK
| | - Robert L. Richards
- Odum School of Ecology; University of Georgia; Athens Georgia 30602 USA
- Center for the Ecology of Infectious Diseases; University of Georgia; Athens Georgia 30602 USA
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