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Jung SM, Loo SL, Howerton E, Contamin L, Smith CP, Carcelén EC, Yan K, Bents SJ, Levander J, Espino J, Lemaitre JC, Sato K, McKee CD, Hill AL, Chinazzi M, Davis JT, Mu K, Vespignani A, Rosenstrom ET, Rodriguez-Cartes SA, Ivy JS, Mayorga ME, Swann JL, España G, Cavany S, Moore SM, Perkins TA, Chen S, Paul R, Janies D, Thill JC, Srivastava A, Aawar MA, Bi K, Bandekar SR, Bouchnita A, Fox SJ, Meyers LA, Porebski P, Venkatramanan S, Adiga A, Hurt B, Klahn B, Outten J, Chen J, Mortveit H, Wilson A, Hoops S, Bhattacharya P, Machi D, Vullikanti A, Lewis B, Marathe M, Hochheiser H, Runge MC, Shea K, Truelove S, Viboud C, Lessler J. Potential impact of annual vaccination with reformulated COVID-19 vaccines: Lessons from the US COVID-19 scenario modeling hub. PLoS Med 2024; 21:e1004387. [PMID: 38630802 DOI: 10.1371/journal.pmed.1004387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 05/01/2024] [Accepted: 03/27/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Coronavirus Disease 2019 (COVID-19) continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Here, we present projections of COVID-19 hospitalizations and deaths in the United States for the next 2 years under 2 plausible assumptions about immune escape (20% per year and 50% per year) and 3 possible CDC recommendations for the use of annually reformulated vaccines (no recommendation, vaccination for those aged 65 years and over, vaccination for all eligible age groups based on FDA approval). METHODS AND FINDINGS The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023 and April 15, 2025 under 6 scenarios representing the intersection of considered levels of immune escape and vaccination. Annually reformulated vaccines are assumed to be 65% effective against symptomatic infection with strains circulating on June 15 of each year and to become available on September 1. Age- and state-specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. State and national projections from 8 modeling teams were ensembled to produce projections for each scenario and expected reductions in disease outcomes due to vaccination over the projection period. From April 15, 2023 to April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November to January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% projection interval (PI) [1,438,000, 4,270,000]) hospitalizations and 209,000 (90% PI [139,000, 461,000]) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% confidence interval (CI) [104,000, 355,000]) fewer hospitalizations and 33,000 (95% CI [12,000, 54,000]) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000-598,000) fewer hospitalizations and 49,000 (95% CI [29,000, 69,000]) fewer deaths. CONCLUSIONS COVID-19 is projected to be a significant public health threat over the coming 2 years. Broad vaccination has the potential to substantially reduce the burden of this disease, saving tens of thousands of lives each year.
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Affiliation(s)
- Sung-Mok Jung
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Sara L Loo
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Emily Howerton
- The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Lucie Contamin
- University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Claire P Smith
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Erica C Carcelén
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Katie Yan
- The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Samantha J Bents
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
| | - John Levander
- University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Jessi Espino
- University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Joseph C Lemaitre
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Koji Sato
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Clifton D McKee
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Alison L Hill
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Matteo Chinazzi
- Northeastern University, Boston, Massachusetts, United States of America
| | - Jessica T Davis
- Northeastern University, Boston, Massachusetts, United States of America
| | - Kunpeng Mu
- Northeastern University, Boston, Massachusetts, United States of America
| | | | - Erik T Rosenstrom
- North Carolina State University, Raleigh, North Carolina, United States of America
| | | | - Julie S Ivy
- North Carolina State University, Raleigh, North Carolina, United States of America
| | - Maria E Mayorga
- North Carolina State University, Raleigh, North Carolina, United States of America
| | - Julie L Swann
- North Carolina State University, Raleigh, North Carolina, United States of America
| | - Guido España
- University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Sean Cavany
- University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Sean M Moore
- University of Notre Dame, Notre Dame, Indiana, United States of America
| | - T Alex Perkins
- University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Shi Chen
- University of North Carolina at Charlotte, Charlotte, North Carolina, United States of America
| | - Rajib Paul
- University of North Carolina at Charlotte, Charlotte, North Carolina, United States of America
| | - Daniel Janies
- University of North Carolina at Charlotte, Charlotte, North Carolina, United States of America
| | - Jean-Claude Thill
- University of North Carolina at Charlotte, Charlotte, North Carolina, United States of America
| | - Ajitesh Srivastava
- University of Southern California, Los Angeles, California, United States of America
| | - Majd Al Aawar
- University of Southern California, Los Angeles, California, United States of America
| | - Kaiming Bi
- University of Texas at Austin, Austin, Texas, United States of America
| | | | - Anass Bouchnita
- University of Texas at El Paso, El Paso, Texas, United States of America
| | - Spencer J Fox
- University of Georgia, Athens, Georgia, 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
| | - Brian Klahn
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Joseph Outten
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Jiangzhuo Chen
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Henning Mortveit
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Amanda Wilson
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Stefan Hoops
- University of Virginia, Charlottesville, Virginia, United States of America
| | | | - Dustin Machi
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Anil Vullikanti
- 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
| | - Harry Hochheiser
- University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Michael C Runge
- U.S. Geological Survey, Laurel, Maryland, United States of America
| | - Katriona Shea
- The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Shaun Truelove
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Justin Lessler
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
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2
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Chen C, Kaur G, Adiga A, Espinoza B, Venkatramanan S, Warren A, Lewis B, Crow J, Singh R, Lorentz A, Toney D, Marathe M. Wastewater-based Epidemiology for COVID-19 Surveillance: A Survey. ArXiv 2024:arXiv:2403.15291v1. [PMID: 38562450 PMCID: PMC10984000] [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] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The pandemic of COVID-19 has imposed tremendous pressure on public health systems and social economic ecosystems over the past years. To alleviate its social impact, it is important to proactively track the prevalence of COVID-19 within communities. The traditional way to estimate the disease prevalence is to estimate from reported clinical test data or surveys. However, the coverage of clinical tests is often limited and the tests can be labor-intensive, requires reliable and timely results, and consistent diagnostic and reporting criteria. Recent studies revealed that patients who are diagnosed with COVID-19 often undergo fecal shedding of SARS-CoV-2 virus into wastewater, which makes wastewater-based epidemiology (WBE) for COVID-19 surveillance a promising approach to complement traditional clinical testing. In this paper, we survey the existing literature regarding WBE for COVID-19 surveillance and summarize the current advances in the area. Specifically, we have covered the key aspects of wastewater sampling, sample testing, and presented a comprehensive and organized summary of wastewater data analytical methods. Finally, we provide the open challenges on current wastewater-based COVID-19 surveillance studies, aiming to encourage new ideas to advance the development of effective wastewater-based surveillance systems for general infectious diseases.
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Affiliation(s)
- Chen Chen
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States
| | - Gursharn Kaur
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States
| | - Aniruddha Adiga
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States
| | - Baltazar Espinoza
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States
| | - Srinivasan Venkatramanan
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States
| | - Andrew Warren
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States
| | - Bryan Lewis
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States
| | - Justin Crow
- Virginia Department of Health, Richmond, 23219, United States
| | - Rekha Singh
- Virginia Department of Health, Richmond, 23219, United States
| | - Alexandra Lorentz
- Division of Consolidated Laboratory Services, Department of General Services, Richmond, 23219, United States
| | - Denise Toney
- Division of Consolidated Laboratory Services, Department of General Services, Richmond, 23219, United States
| | - Madhav Marathe
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States
- Department of Computer Science, University of Virginia, Charlottesville, 22904, United States
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3
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Porebski P, Venkatramanan S, Adiga A, Klahn B, Hurt B, Wilson ML, Chen J, Vullikanti A, Marathe M, Lewis B. Data-driven mechanistic framework with stratified immunity and effective transmissibility for COVID-19 scenario projections. Epidemics 2024; 47:100761. [PMID: 38555667 DOI: 10.1016/j.epidem.2024.100761] [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: 08/18/2023] [Revised: 01/30/2024] [Accepted: 03/07/2024] [Indexed: 04/02/2024] Open
Abstract
Scenario-based modeling frameworks have been widely used to support policy-making at state and federal levels in the United States during the COVID-19 response. While custom-built models can be used to support one-off studies, sustained updates to projections under changing pandemic conditions requires a robust, integrated, and adaptive framework. In this paper, we describe one such framework, UVA-adaptive, that was built to support the CDC-aligned Scenario Modeling Hub (SMH) across multiple rounds, as well as weekly/biweekly projections to Virginia Department of Health (VDH) and US Department of Defense during the COVID-19 response. Building upon an existing metapopulation framework, PatchSim, UVA-adaptive uses a calibration mechanism relying on adjustable effective transmissibility as a basis for scenario definition while also incorporating real-time datasets on case incidence, seroprevalence, variant characteristics, and vaccine uptake. Through the pandemic, our framework evolved by incorporating available data sources and was extended to capture complexities of multiple strains and heterogeneous immunity of the population. Here we present the version of the model that was used for the recent projections for SMH and VDH, describe the calibration and projection framework, and demonstrate that the calibrated transmissibility correlates with the evolution of the pathogen as well as associated societal dynamics.
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Affiliation(s)
- Przemyslaw Porebski
- Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, 22911, VA, USA.
| | | | - Aniruddha Adiga
- Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, 22911, VA, USA
| | - Brian Klahn
- Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, 22911, VA, USA
| | - Benjamin Hurt
- Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, 22911, VA, USA
| | - Mandy L Wilson
- Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, 22911, VA, USA
| | - Jiangzhuo Chen
- Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, 22911, VA, USA
| | - Anil Vullikanti
- Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, 22911, VA, USA; Department of Computer Science, University of Virginia, Charlottesville, 22904, VA, USA
| | - Madhav Marathe
- Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, 22911, VA, USA; Department of Computer Science, University of Virginia, Charlottesville, 22904, VA, USA
| | - Bryan Lewis
- Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, 22911, VA, USA
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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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5
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Howerton E, Contamin L, Mullany LC, Qin M, Reich NG, Bents S, Borchering RK, Jung SM, Loo SL, Smith CP, Levander J, Kerr J, Espino J, van Panhuis WG, Hochheiser H, Galanti M, Yamana T, Pei S, Shaman J, Rainwater-Lovett K, Kinsey M, Tallaksen K, Wilson S, Shin L, Lemaitre JC, Kaminsky J, Hulse JD, Lee EC, McKee CD, Hill A, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Pastore Y Piontti A, Vespignani A, Rosenstrom ET, Ivy JS, Mayorga ME, Swann JL, España G, Cavany S, Moore S, Perkins A, Hladish T, Pillai A, Ben Toh K, Longini I, Chen S, Paul R, Janies D, Thill JC, Bouchnita A, Bi K, Lachmann M, Fox SJ, Meyers LA, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Hurt B, Chen J, Mortveit H, Wilson A, Marathe M, Hoops S, Bhattacharya P, Machi D, Cadwell BL, Healy JM, Slayton RB, Johansson MA, Biggerstaff M, Truelove S, Runge MC, Shea K, Viboud C, Lessler J. Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty. Nat Commun 2023; 14:7260. [PMID: 37985664 PMCID: PMC10661184 DOI: 10.1038/s41467-023-42680-x] [Citation(s) in RCA: 1] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 10/17/2023] [Indexed: 11/22/2023] Open
Abstract
Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections.
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Affiliation(s)
- Emily Howerton
- The Pennsylvania State University, University Park, PA, USA.
| | | | - Luke C Mullany
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | | | | | - Samantha Bents
- National Institutes of Health Fogarty International Center, Bethesda, MD, USA
| | - Rebecca K Borchering
- The Pennsylvania State University, University Park, PA, USA
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Sung-Mok Jung
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Sara L Loo
- Johns Hopkins University, Baltimore, MD, USA
| | | | | | | | - J Espino
- University of Pittsburgh, Pittsburgh, PA, USA
| | | | | | | | | | - Sen Pei
- Columbia University, New York, NY, USA
| | | | | | - Matt Kinsey
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | - Kate Tallaksen
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | - Shelby Wilson
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | - Lauren Shin
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | | | | | | | | | | | - Alison Hill
- Johns Hopkins University, Baltimore, MD, USA
| | - Dean Karlen
- University of Victoria, Victoria, BC, Canada
| | | | | | - Kunpeng Mu
- Northeastern University, Boston, MA, USA
| | | | | | | | | | - Julie S Ivy
- North Carolina State University, Raleigh, NC, USA
| | | | | | | | - Sean Cavany
- University of Notre Dame, Notre Dame, IN, USA
| | - Sean Moore
- University of Notre Dame, Notre Dame, IN, USA
| | | | | | | | | | | | - Shi Chen
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Rajib Paul
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Daniel Janies
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | | | | | - Kaiming Bi
- University of Texas at Austin, Austin, TX, USA
| | | | | | | | | | | | | | | | - Bryan Lewis
- University of Virginia, Charlottesville, VA, USA
| | - Brian Klahn
- University of Virginia, Charlottesville, VA, USA
| | | | | | | | | | | | | | - Stefan Hoops
- University of Virginia, Charlottesville, VA, USA
| | | | - Dustin Machi
- University of Virginia, Charlottesville, VA, USA
| | - Betsy L Cadwell
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Jessica M Healy
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | | | | | | | | | - Michael C Runge
- U.S. Geological Survey Eastern Ecological Science Center, Laurel, MD, USA
| | - Katriona Shea
- The Pennsylvania State University, University Park, PA, USA
| | - Cécile Viboud
- National Institutes of Health Fogarty International Center, Bethesda, MD, USA.
| | - Justin Lessler
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Johns Hopkins University, Baltimore, MD, USA.
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6
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Jung SM, Loo SL, Howerton E, Contamin L, Smith CP, Carcelén EC, Yan K, Bents SJ, Levander J, Espino J, Lemaitre JC, Sato K, McKee CD, Hill AL, Chinazzi M, Davis JT, Mu K, Vespignani A, Rosenstrom ET, Rodriguez-Cartes SA, Ivy JS, Mayorga ME, Swann JL, España G, Cavany S, Moore SM, Perkins A, Chen S, Paul R, Janies D, Thill JC, Srivastava A, Al Aawar M, Bi K, Bandekar SR, Bouchnita A, Fox SJ, Meyers LA, Porebski P, Venkatramanan S, Adiga A, Hurt B, Klahn B, Outten J, Chen J, Mortveit H, Wilson A, Hoops S, Bhattacharya P, Machi D, Vullikanti A, Lewis B, Marathe M, Hochheiser H, Runge MC, Shea K, Truelove S, Viboud C, Lessler J. Potential impact of annual vaccination with reformulated COVID-19 vaccines: lessons from the U.S. COVID-19 Scenario Modeling Hub. medRxiv 2023:2023.10.26.23297581. [PMID: 37961207 PMCID: PMC10635209 DOI: 10.1101/2023.10.26.23297581] [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: 11/15/2023]
Abstract
Importance COVID-19 continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Objective To project COVID-19 hospitalizations and deaths from April 2023-April 2025 under two plausible assumptions about immune escape (20% per year and 50% per year) and three possible CDC recommendations for the use of annually reformulated vaccines (no vaccine recommendation, vaccination for those aged 65+, vaccination for all eligible groups). Design The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023-April 15, 2025 under six scenarios representing the intersection of considered levels of immune escape and vaccination. State and national projections from eight modeling teams were ensembled to produce projections for each scenario. Setting The entire United States. Participants None. Exposure Annually reformulated vaccines assumed to be 65% effective against strains circulating on June 15 of each year and to become available on September 1. Age and state specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. Main outcomes and measures Ensemble estimates of weekly and cumulative COVID-19 hospitalizations and deaths. Expected relative and absolute reductions in hospitalizations and deaths due to vaccination over the projection period. Results From April 15, 2023-April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November-January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% PI: 1,438,000-4,270,000) hospitalizations and 209,000 (90% PI: 139,000-461,000) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% CI: 104,000-355,000) fewer hospitalizations and 33,000 (95% CI: 12,000-54,000) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000-598,000) fewer hospitalizations and 49,000 (95% CI: 29,000-69,000) fewer deaths. Conclusion and Relevance COVID-19 is projected to be a significant public health threat over the coming two years. Broad vaccination has the potential to substantially reduce the burden of this disease.
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Affiliation(s)
- Sung-mok Jung
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Sara L. Loo
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Emily Howerton
- The Pennsylvania State University, State College, Pennsylvania
| | | | - Claire P. Smith
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Erica C. Carcelén
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Katie Yan
- The Pennsylvania State University, State College, Pennsylvania
| | - Samantha J. Bents
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland
| | | | - Jessi Espino
- University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Joseph C. Lemaitre
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Koji Sato
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Clif D. McKee
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Alison L. Hill
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | | | | | - Kunpeng Mu
- University of Massachusetts Amherst, Amherst, Massachusetts
| | | | | | | | - Julie S. Ivy
- North Carolina State University, Raleigh, North Carolina
| | | | - Julie L. Swann
- North Carolina State University, Raleigh, North Carolina
| | | | - Sean Cavany
- University of Notre Dame, Notre Dame, Indiana
| | | | | | - Shi Chen
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | - Rajib Paul
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | - Daniel Janies
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | - Jean-Claude Thill
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | | | - Majd Al Aawar
- University of Southern California, Los Angeles, California
| | - Kaiming Bi
- University of Texas at Austin, Austin, Texas
| | | | | | | | | | | | | | | | | | - Brian Klahn
- University of Virginia, Charlottesville, Virginia
| | | | | | | | | | - Stefan Hoops
- University of Virginia, Charlottesville, Virginia
| | | | - Dustin Machi
- University of Virginia, Charlottesville, Virginia
| | | | - Bryan Lewis
- University of Virginia, Charlottesville, Virginia
| | | | | | | | - Katriona Shea
- The Pennsylvania State University, State College, Pennsylvania
| | - Shaun Truelove
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland
| | - Justin Lessler
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
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7
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Howerton E, Contamin L, Mullany LC, Qin M, Reich NG, Bents S, Borchering RK, Jung SM, Loo SL, Smith CP, Levander J, Kerr J, Espino J, van Panhuis WG, Hochheiser H, Galanti M, Yamana T, Pei S, Shaman J, Rainwater-Lovett K, Kinsey M, Tallaksen K, Wilson S, Shin L, Lemaitre JC, Kaminsky J, Hulse JD, Lee EC, McKee C, Hill A, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Piontti APY, Vespignani A, Rosenstrom ET, Ivy JS, Mayorga ME, Swann JL, España G, Cavany S, Moore S, Perkins A, Hladish T, Pillai A, Toh KB, Longini I, Chen S, Paul R, Janies D, Thill JC, Bouchnita A, Bi K, Lachmann M, Fox S, Meyers LA, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Hurt B, Chen J, Mortveit H, Wilson A, Marathe M, Hoops S, Bhattacharya P, Machi D, Cadwell BL, Healy JM, Slayton RB, Johansson MA, Biggerstaff M, Truelove S, Runge MC, Shea K, Viboud C, Lessler J. Informing pandemic response in the face of uncertainty. An evaluation of the U.S. COVID-19 Scenario Modeling Hub. medRxiv 2023:2023.06.28.23291998. [PMID: 37461674 PMCID: PMC10350156 DOI: 10.1101/2023.06.28.23291998] [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] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/24/2023]
Abstract
Our ability to forecast epidemics more than a few weeks into the future is constrained by the complexity of disease systems, our limited ability to measure the current state of an epidemic, and uncertainties in how human action will affect transmission. Realistic longer-term projections (spanning more than a few weeks) may, however, be possible under defined scenarios that specify the future state of critical epidemic drivers, with the additional benefit that such scenarios can be used to anticipate the comparative effect of control measures. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make 6-month ahead projections of the number of SARS-CoV-2 cases, hospitalizations and deaths. The SMH released nearly 1.8 million national and state-level projections between February 2021 and November 2022. SMH performance varied widely as a function of both scenario validity and model calibration. Scenario assumptions were periodically invalidated by the arrival of unanticipated SARS-CoV-2 variants, but SMH still provided projections on average 22 weeks before changes in assumptions (such as virus transmissibility) invalidated scenarios and their corresponding projections. During these periods, before emergence of a novel variant, a linear opinion pool ensemble of contributed models was consistently more reliable than any single model, and projection interval coverage was near target levels for the most plausible scenarios (e.g., 79% coverage for 95% projection interval). SMH projections were used operationally to guide planning and policy at different stages of the pandemic, illustrating the value of the hub approach for long-term scenario projections.
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Affiliation(s)
| | | | | | | | | | - Samantha Bents
- National Institutes of Health Fogarty International Center (NIH)
| | | | | | - Sara L Loo
- Johns Hopkins University Infectious Disease Dynamics (JHU-IDD)
| | - Claire P Smith
- Johns Hopkins University Infectious Disease Dynamics (JHU-IDD)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Shi Chen
- University of North Carolina at Charlotte (UNCC)
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8
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Tozluoğlu Ç, Dhamal S, Yeh S, Sprei F, Liao Y, Marathe M, Barrett CL, Dubhashi D. A synthetic population of Sweden: datasets of agents, households, and activity-travel patterns. Data Brief 2023; 48:109209. [PMID: 37228419 PMCID: PMC10205447 DOI: 10.1016/j.dib.2023.109209] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/26/2023] [Accepted: 05/02/2023] [Indexed: 05/27/2023] Open
Abstract
A synthetic population is a simplified microscopic representation of an actual population. Statistically representative at the population level, it provides valuable inputs to simulation models (especially agent-based models) in research areas such as transportation, land use, economics, and epidemiology. This article describes the datasets from the Synthetic Sweden Mobility (SySMo) model using the state-of-art methodology, including machine learning (ML), iterative proportional fitting (IPF), and probabilistic sampling. The model provides a synthetic replica of over 10 million Swedish individuals (i.e., agents), their household characteristics, and activity-travel plans. This paper briefly explains the methodology for the three datasets: Person, Households, and Activity-travel patterns. Each agent contains socio-demographic attributes, such as age, gender, civil status, residential zone, personal income, car ownership, employment, etc. Each agent also has a household and corresponding attributes such as household size, number of children ≤ 6 years old, etc. These characteristics are the basis for the agents' daily activity-travel schedule, including type of activity, start-end time, duration, sequence, the location of each activity, and the travel mode between activities.
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Affiliation(s)
- Çağlar Tozluoğlu
- Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden
| | - Swapnil Dhamal
- Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden
| | - Sonia Yeh
- Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden
| | - Frances Sprei
- Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden
| | - Yuan Liao
- Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden
| | - Madhav Marathe
- Department of Computer Science, University of Virginia, Charlottesville, United States
| | | | - Devdatt Dubhashi
- Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden
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9
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Sherratt K, Gruson H, Grah R, Johnson H, Niehus R, Prasse B, Sandmann F, Deuschel J, Wolffram D, Abbott S, Ullrich A, Gibson G, Ray EL, Reich NG, Sheldon D, Wang Y, Wattanachit N, Wang L, Trnka J, Obozinski G, Sun T, Thanou D, Pottier L, Krymova E, Meinke JH, Barbarossa MV, Leithäuser N, Mohring J, Schneider J, Włazło J, Fuhrmann J, Lange B, Rodiah I, Baccam P, Gurung H, Stage S, Suchoski B, Budzinski J, Walraven R, Villanueva I, Tucek V, Smid M, Zajíček M, Pérez Álvarez C, Reina B, Bosse NI, Meakin SR, Castro L, Fairchild G, Michaud I, Osthus D, Alaimo Di Loro P, Maruotti A, Eclerová V, Kraus A, Kraus D, Pribylova L, Dimitris B, Li ML, Saksham S, Dehning J, Mohr S, Priesemann V, Redlarski G, Bejar B, Ardenghi G, Parolini N, Ziarelli G, Bock W, Heyder S, Hotz T, Singh DE, Guzman-Merino M, Aznarte JL, Moriña D, Alonso S, Álvarez E, López D, Prats C, Burgard JP, Rodloff A, Zimmermann T, Kuhlmann A, Zibert J, Pennoni F, Divino F, Català M, Lovison G, Giudici P, Tarantino B, Bartolucci F, Jona Lasinio G, Mingione M, Farcomeni A, Srivastava A, Montero-Manso P, Adiga A, Hurt B, Lewis B, Marathe M, Porebski P, Venkatramanan S, Bartczuk RP, Dreger F, Gambin A, Gogolewski K, Gruziel-Słomka M, Krupa B, Moszyński A, Niedzielewski K, Nowosielski J, Radwan M, Rakowski F, Semeniuk M, Szczurek E, Zieliński J, Kisielewski J, Pabjan B, Kirsten H, Kheifetz Y, Scholz M, Biecek P, Bodych M, Filinski M, Idzikowski R, Krueger T, Ozanski T, Bracher J, Funk S. Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations. eLife 2023; 12:81916. [PMID: 37083521 DOI: 10.7554/elife.81916] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 02/20/2023] [Indexed: 04/22/2023] Open
Abstract
Background: Short-term forecasts of infectious disease contribute to situational awareness and capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise forecasts' predictive performance by combining independent models into an ensemble. Here we report the performance of ensemble predictions of COVID-19 cases and deaths across Europe from March 2021 to March 2022. Methods: We created the European COVID-19 Forecast Hub, an online open-access platform where modellers upload weekly forecasts for 32 countries with results publicly visualised and evaluated. We created a weekly ensemble forecast from the equally-weighted average across individual models' predictive quantiles. We measured forecast accuracy using a baseline and relative Weighted Interval Score (rWIS). We retrospectively explored ensemble methods, including weighting by past performance. Results: We collected weekly forecasts from 48 models, of which we evaluated 29 models alongside the ensemble model. The ensemble had a consistently strong performance across countries over time, performing better on rWIS than 91% of forecasts for deaths (N=763 predictions from 20 models), and 83% forecasts for cases (N=886 predictions from 23 models). Performance remained stable over a 4-week horizon for death forecasts but declined with longer horizons for cases. Among ensemble methods, the most influential choice came from using a median average instead of the mean, regardless of weighting component models. Conclusions: Our results support combining independent models into an ensemble forecast to improve epidemiological predictions, and suggest that median averages yield better performance than methods based on means. We highlight that forecast consumers should place more weight on incident death forecasts than case forecasts at horizons greater than two weeks. Funding: European Commission, Ministerio de Ciencia, Innovación y Universidades, FEDER; Agència de Qualitat i Avaluació Sanitàries de Catalunya; Netzwerk Universitätsmedizin; Health Protection Research Unit; Wellcome Trust; European Centre for Disease Prevention and Control; Ministry of Science and Higher Education of Poland; Federal Ministry of Education and Research; Los Alamos National Laboratory; German Free State of Saxony; NCBiR; FISR 2020 Covid-19 I Fase; Spanish Ministry of Health / REACT-UE (FEDER); National Institutes of General Medical Sciences; Ministerio de Sanidad/ISCIII; PERISCOPE European H2020; PERISCOPE European H2021; InPresa; National Institutes of Health, NSF, US Centers for Disease Control and Prevention, Google, University of Virginia, Defense Threat Reduction Agency.
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Affiliation(s)
- Katharine Sherratt
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Hugo Gruson
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Rok Grah
- European Centre for Disease Prevention and Control, Stockholm, Sweden
| | - Helen Johnson
- European Centre for Disease Prevention and Control, Stockholm, Sweden
| | - Rene Niehus
- European Centre for Disease Prevention and Control, Stockholm, Sweden
| | - Bastian Prasse
- European Centre for Disease Prevention and Control, Stockholm, Sweden
| | - Frank Sandmann
- European Centre for Disease Prevention and Control, Stockholm, Sweden
| | | | | | - Sam Abbott
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | | | - Graham Gibson
- University of Massachusetts Amherst, Amherst, United States
| | - Evan L Ray
- University of Massachusetts Amherst, Amherst, United States
| | | | - Daniel Sheldon
- University of Massachusetts Amherst, Amherst, United States
| | - Yijin Wang
- University of Massachusetts Amherst, Amherst, United States
| | | | - Lijing Wang
- Boston Children's Hospital, Boston, United States
| | - Jan Trnka
- Department of Biochemistry, Cell and Molecular Biology, Charles University, Prague, Czech Republic
| | | | - Tao Sun
- École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Dorina Thanou
- École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | | | | | | | | | - Neele Leithäuser
- Fraunhofer Institute for Industrial Mathematics, Kaiserslautern, Germany
| | - Jan Mohring
- Fraunhofer Institute for Industrial Mathematics, Kaiserslautern, Germany
| | - Johanna Schneider
- Fraunhofer Institute for Industrial Mathematics, Kaiserslautern, Germany
| | - Jaroslaw Włazło
- Fraunhofer Institute for Industrial Mathematics, Kaiserslautern, Germany
| | | | - Berit Lange
- Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Isti Rodiah
- Helmholtz Centre for Infection Research, Braunschweig, Germany
| | | | | | | | | | | | | | - Inmaculada Villanueva
- Institut d'Investigacions Biomediques August Pi i Sunyer, Universitat Pompeu Fabra, Barcelona, Spain
| | - Vit Tucek
- Institute of Computer Science, Prague, Czech Republic
| | - Martin Smid
- Institute of Information Theory and Automation, Prague, Czech Republic
| | - Milan Zajíček
- Institute of Information Theory and Automation, Prague, Czech Republic
| | | | | | - Nikos I Bosse
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Sophie R Meakin
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Lauren Castro
- Los Alamos National Laboratory, Los Alamos, United States
| | | | - Isaac Michaud
- Los Alamos National Laboratory, Los Alamos, United States
| | - Dave Osthus
- Los Alamos National Laboratory, Los Alamos, United States
| | | | | | | | | | | | | | | | | | - Soni Saksham
- Massachusetts Institute of Technology, Cambridge, United States
| | - Jonas Dehning
- Max-Planck-Institut fur Dynamik und Selbstorganisation, Göttingen, Germany
| | - Sebastian Mohr
- Max-Planck-Institut fur Dynamik und Selbstorganisation, Göttingen, Germany
| | - Viola Priesemann
- MPRG Priesemann, Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
| | | | | | | | | | | | - Wolfgang Bock
- Technical University of Kaiserlautern, Kaiserslautern, Germany
| | | | - Thomas Hotz
- Technische Universitat Ilmenau, Ilmenau, Germany
| | | | | | - Jose L Aznarte
- Universidad Nacional de Educacion a Distancia, Madrid, Spain
| | | | - Sergio Alonso
- Universitat Politecnica de Catalunya, Barcelona, Spain
| | - Enric Álvarez
- Universitat Politecnica de Catalunya, Barcelona, Spain
| | - Daniel López
- Universitat Politecnica de Catalunya, Barcelona, Spain
| | - Clara Prats
- Universitat Politecnica de Catalunya, Barcelona, Spain
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Benjamin Hurt
- University of Virginia, Charlottesville, United States
| | - Bryan Lewis
- University of Virginia, Charlottesville, United States
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Marcin Bodych
- Wroclaw University of Science and Technology, Wroclaw, Poland
| | - Maciej Filinski
- Wroclaw University of Science and Technology, Wroclaw, Poland
| | | | - Tyll Krueger
- Wroclaw University of Science and Technology, Wroclaw, Poland
| | - Tomasz Ozanski
- Wroclaw University of Science and Technology, Wroclaw, Poland
| | | | - Sebastian Funk
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
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10
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Hurt B, Hoque OB, Mokrzycki F, Mathew A, Xue M, Gabitsinashvili L, Mokrzycki H, Fischer R, Telesca N, Xue LA, Ritchie J, Zamfirescu-Pereira JD, Bernstein M, Whiting M, Marathe M. COVID-19 non-pharmaceutical interventions: data annotation for rapidly changing local policy information. Sci Data 2023; 10:126. [PMID: 36894597 PMCID: PMC9996560 DOI: 10.1038/s41597-023-01979-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 01/19/2023] [Indexed: 03/11/2023] Open
Abstract
Understanding the scope, prevalence, and impact of the COVID-19 pandemic response will be a rich ground for research for many years. Key to the response to COVID-19 was the non-pharmaceutical intervention (NPI) measures, such as mask mandates or stay-in-place orders. For future pandemic preparedness, it is critical to understand the impact and scope of these interventions. Given the ongoing nature of the pandemic, existing NPI studies covering only the initial portion provide only a narrow view of the impact of NPI measures. This paper describes a dataset of NPI measures taken by counties in the U.S. state of Virginia that include measures taken over the first two years of the pandemic beginning in March 2020. This data enables analyses of NPI measures over a long time period that can produce impact analyses on both the individual NPI effectiveness in slowing the pandemic spread, and the impact of various NPI measures on the behavior and conditions of the different counties and state.
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Affiliation(s)
- Benjamin Hurt
- Network Systems Science and Advanced Computing, Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, USA.
| | - Oishee Bintey Hoque
- Network Systems Science and Advanced Computing, Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, USA.
| | - Finn Mokrzycki
- Network Systems Science and Advanced Computing, Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, USA.
| | - Anjali Mathew
- Network Systems Science and Advanced Computing, Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, USA
| | - Maryann Xue
- Network Systems Science and Advanced Computing, Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, USA
| | - Luka Gabitsinashvili
- Network Systems Science and Advanced Computing, Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, USA
| | - Haile Mokrzycki
- Network Systems Science and Advanced Computing, Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, USA
| | - Ranya Fischer
- Network Systems Science and Advanced Computing, Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, USA
| | - Nicholas Telesca
- Network Systems Science and Advanced Computing, Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, USA
| | - Lauren Aurelia Xue
- Network Systems Science and Advanced Computing, Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, USA
| | - Jacob Ritchie
- Dept of Computer Science, Stanford University, Stanford, USA
| | - J D Zamfirescu-Pereira
- Dept of Electrical Engineering and Computer Science, University of California, Berkeley, USA
| | | | - Mark Whiting
- Department of Computer and Information Sciences, University of Pennsylvania, Philadelphia, USA
| | - Madhav Marathe
- Network Systems Science and Advanced Computing, Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, USA.
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11
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Bhattacharya P, Chen J, Hoops S, Machi D, Lewis B, Venkatramanan S, Wilson ML, Klahn B, Adiga A, Hurt B, Outten J, Adiga A, Warren A, Baek YY, Porebski P, Marathe A, Xie D, Swarup S, Vullikanti A, Mortveit H, Eubank S, Barrett CL, Marathe M. Data-driven scalable pipeline using national agent-based models for real-time pandemic response and decision support. Int J High Perform Comput Appl 2023; 37:4-27. [PMID: 38603425 PMCID: PMC9596688 DOI: 10.1177/10943420221127034] [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] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
This paper describes an integrated, data-driven operational pipeline based on national agent-based models to support federal and state-level pandemic planning and response. The pipeline consists of (i) an automatic semantic-aware scheduling method that coordinates jobs across two separate high performance computing systems; (ii) a data pipeline to collect, integrate and organize national and county-level disaggregated data for initialization and post-simulation analysis; (iii) a digital twin of national social contact networks made up of 288 Million individuals and 12.6 Billion time-varying interactions covering the US states and DC; (iv) an extension of a parallel agent-based simulation model to study epidemic dynamics and associated interventions. This pipeline can run 400 replicates of national runs in less than 33 h, and reduces the need for human intervention, resulting in faster turnaround times and higher reliability and accuracy of the results. Scientifically, the work has led to significant advances in real-time epidemic sciences.
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Affiliation(s)
- Parantapa Bhattacharya
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Jiangzhuo Chen
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Stefan Hoops
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Dustin Machi
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Bryan Lewis
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | | | - Mandy L Wilson
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Brian Klahn
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Aniruddha Adiga
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Benjamin Hurt
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Joseph Outten
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Abhijin Adiga
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Andrew Warren
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Young Yun Baek
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Przemyslaw Porebski
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Achla Marathe
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
- Dept. of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Dawen Xie
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Samarth Swarup
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Anil Vullikanti
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
- Dept. of Computer Science, University of Virginia, Charlottesville, VA, USA
| | - Henning Mortveit
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
- Dept. of Eng. Systems and Environment, University of Virginia, Charlottesville, VA, USA
| | - Stephen Eubank
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
- Dept. of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Christopher L Barrett
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
- Dept. of Computer Science, University of Virginia, Charlottesville, VA, USA
| | - Madhav Marathe
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
- Dept. of Computer Science, University of Virginia, Charlottesville, VA, USA
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12
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Borchering RK, Mullany LC, Howerton E, Chinazzi M, Smith CP, Qin M, Reich NG, Contamin L, Levander J, Kerr J, Espino J, Hochheiser H, Lovett K, Kinsey M, Tallaksen K, Wilson S, Shin L, Lemaitre JC, Hulse JD, Kaminsky J, Lee EC, Hill AL, Davis JT, Mu K, Xiong X, Pastore y Piontti A, Vespignani A, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Hurt B, Chen J, Mortveit H, Wilson A, Marathe M, Hoops S, Bhattacharya P, Machi D, Chen S, Paul R, Janies D, Thill JC, Galanti M, Yamana T, Pei S, Shaman J, España G, Cavany S, Moore S, Perkins A, Healy JM, Slayton RB, Johansson MA, Biggerstaff M, Shea K, Truelove SA, Runge MC, Viboud C, Lessler J. Impact of SARS-CoV-2 vaccination of children ages 5-11 years on COVID-19 disease burden and resilience to new variants in the United States, November 2021-March 2022: A multi-model study. Lancet Reg Health Am 2023; 17:100398. [PMID: 36437905 PMCID: PMC9679449 DOI: 10.1016/j.lana.2022.100398] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [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: 06/21/2022] [Revised: 09/21/2022] [Accepted: 10/27/2022] [Indexed: 11/23/2022]
Abstract
Background The COVID-19 Scenario Modeling Hub convened nine modeling teams to project the impact of expanding SARS-CoV-2 vaccination to children aged 5-11 years on COVID-19 burden and resilience against variant strains. Methods Teams contributed state- and national-level weekly projections of cases, hospitalizations, and deaths in the United States from September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of 1) vaccination (or not) of children aged 5-11 years (starting November 1, 2021), and 2) emergence (or not) of a variant more transmissible than the Delta variant (emerging November 15, 2021). Individual team projections were linearly pooled. The effect of childhood vaccination on overall and age-specific outcomes was estimated using meta-analyses. Findings Assuming that a new variant would not emerge, all-age COVID-19 outcomes were projected to decrease nationally through mid-March 2022. In this setting, vaccination of children 5-11 years old was associated with reductions in projections for all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880-0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834-0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797-1.020) compared with scenarios without childhood vaccination. Vaccine benefits increased for scenarios including a hypothesized more transmissible variant, assuming similar vaccine effectiveness. Projected relative reductions in cumulative outcomes were larger for children than for the entire population. State-level variation was observed. Interpretation Given the scenario assumptions (defined before the emergence of Omicron), expanding vaccination to children 5-11 years old would provide measurable direct benefits, as well as indirect benefits to the all-age U.S. population, including resilience to more transmissible variants. Funding Various (see acknowledgments).
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Affiliation(s)
| | - Luke C. Mullany
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | - Emily Howerton
- The Pennsylvania State University, University Park, PA, USA
| | | | | | | | | | | | | | | | - J. Espino
- University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Kaitlin Lovett
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | - Matt Kinsey
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | - Kate Tallaksen
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | - Shelby Wilson
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | - Lauren Shin
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | | | | | | | | | | | | | - Kunpeng Mu
- Northeastern University, Boston, MA, USA
| | | | | | | | | | | | | | | | - Bryan Lewis
- University of Virginia, Charlottesville, VA, USA
| | - Brian Klahn
- University of Virginia, Charlottesville, VA, USA
| | | | | | | | | | | | | | - Stefan Hoops
- University of Virginia, Charlottesville, VA, USA
| | | | - Dustin Machi
- University of Virginia, Charlottesville, VA, USA
| | - Shi Chen
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Rajib Paul
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Daniel Janies
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | | | | | | | - Sen Pei
- Columbia University, New York, NY, USA
| | | | | | - Sean Cavany
- University of Notre Dame, Notre Dame, IN, USA
| | - Sean Moore
- University of Notre Dame, Notre Dame, IN, USA
| | | | - Jessica M. Healy
- CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Rachel B. Slayton
- CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Michael A. Johansson
- CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Matthew Biggerstaff
- CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Katriona Shea
- The Pennsylvania State University, University Park, PA, USA
| | | | | | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Justin Lessler
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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13
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Truelove S, Smith CP, Qin M, Mullany LC, Borchering RK, Lessler J, Shea K, Howerton E, Contamin L, Levander J, Kerr J, Hochheiser H, Kinsey M, Tallaksen K, Wilson S, Shin L, Rainwater-Lovett K, Lemairtre JC, Dent J, Kaminsky J, Lee EC, Perez-Saez J, Hill A, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Pastore y Piontti A, Vespignani A, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Orr M, Harrison G, Hurt B, Chen J, Vullikanti A, Marathe M, Hoops S, Bhattacharya P, Machi D, Chen S, Paul R, Janies D, Thill JC, Galanti M, Yamana TK, Pei S, Shaman JL, Healy JM, Slayton RB, Biggerstaff M, Johansson MA, Runge MC, Viboud C. Projected resurgence of COVID-19 in the United States in July-December 2021 resulting from the increased transmissibility of the Delta variant and faltering vaccination. eLife 2022; 11:e73584. [PMID: 35726851 PMCID: PMC9232215 DOI: 10.7554/elife.73584] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 06/03/2022] [Indexed: 01/01/2023] Open
Abstract
In Spring 2021, the highly transmissible SARS-CoV-2 Delta variant began to cause increases in cases, hospitalizations, and deaths in parts of the United States. At the time, with slowed vaccination uptake, this novel variant was expected to increase the risk of pandemic resurgence in the US in summer and fall 2021. As part of the COVID-19 Scenario Modeling Hub, an ensemble of nine mechanistic models produced 6-month scenario projections for July-December 2021 for the United States. These projections estimated substantial resurgences of COVID-19 across the US resulting from the more transmissible Delta variant, projected to occur across most of the US, coinciding with school and business reopening. The scenarios revealed that reaching higher vaccine coverage in July-December 2021 reduced the size and duration of the projected resurgence substantially, with the expected impacts was largely concentrated in a subset of states with lower vaccination coverage. Despite accurate projection of COVID-19 surges occurring and timing, the magnitude was substantially underestimated 2021 by the models compared with the of the reported cases, hospitalizations, and deaths occurring during July-December, highlighting the continued challenges to predict the evolving COVID-19 pandemic. Vaccination uptake remains critical to limiting transmission and disease, particularly in states with lower vaccination coverage. Higher vaccination goals at the onset of the surge of the new variant were estimated to avert over 1.5 million cases and 21,000 deaths, although may have had even greater impacts, considering the underestimated resurgence magnitude from the model.
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Affiliation(s)
- Shaun Truelove
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
| | - Claire P Smith
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
| | - Michelle Qin
- Harvard UniversityCambridge, MassachusettsUnited States
| | - Luke C Mullany
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
- Johns Hopkins University Applied Physics LaboratoryLaurelUnited States
| | | | - Justin Lessler
- University of North Carolina at Chapel HillChapel HillUnited States
| | - Katriona Shea
- Pennsylvania State UniversityUniversity ParkUnited States
| | - Emily Howerton
- Pennsylvania State UniversityUniversity ParkUnited States
| | | | | | | | | | - Matt Kinsey
- Johns Hopkins University Applied Physics LaboratoryLaurelUnited States
| | - Kate Tallaksen
- Johns Hopkins University Applied Physics LaboratoryLaurelUnited States
| | - Shelby Wilson
- Johns Hopkins University Applied Physics LaboratoryLaurelUnited States
| | - Lauren Shin
- Johns Hopkins University Applied Physics LaboratoryLaurelUnited States
| | | | | | - Juan Dent
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
| | - Joshua Kaminsky
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
| | - Elizabeth C Lee
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
| | - Javier Perez-Saez
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
| | - Alison Hill
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
| | | | | | | | - Kunpeng Mu
- Northeastern UniversityBostonUnited States
| | | | | | | | | | | | | | | | - Bryan Lewis
- University of VirginiaCharlottesvilleUnited States
| | - Brian Klahn
- University of VirginiaCharlottesvilleUnited States
| | | | - Mark Orr
- University of VirginiaCharlottesvilleUnited States
| | | | | | | | | | | | - Stefan Hoops
- University of VirginiaCharlottesvilleUnited States
| | | | - Dustin Machi
- University of VirginiaCharlottesvilleUnited States
| | - Shi Chen
- University of North Carolina at CharlotteCharlotteUnited States
| | - Rajib Paul
- University of North Carolina at CharlotteCharlotteUnited States
| | - Daniel Janies
- University of North Carolina at CharlotteCharlotteUnited States
| | | | | | | | - Sen Pei
- Columbia UniversityNew YorkUnited States
| | | | | | | | | | | | | | - Cecile Viboud
- Fogarty International Center, National Institutes of HealthBethesdaUnited States
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14
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Borchering RK, Mullany LC, Howerton E, Chinazzi M, Smith CP, Qin M, Reich NG, Contamin L, Levander J, Kerr J, Espino J, Hochheiser H, Lovett K, Kinsey M, Tallaksen K, Wilson S, Shin L, Lemaitre JC, Hulse JD, Kaminsky J, Lee EC, Davis JT, Mu K, Xiong X, Piontti APY, Vespignani A, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Hurt B, Chen J, Mortveit H, Wilson A, Marathe M, Hoops S, Bhattacharya P, Machi D, Chen S, Paul R, Janies D, Thill JC, Galanti M, Yamana T, Pei S, Shaman J, Espana G, Cavany S, Moore S, Perkins A, Healy JM, Slayton RB, Johansson MA, Biggerstaff M, Shea K, Truelove SA, Runge MC, Viboud C, Lessler J. Impact of SARS-CoV-2 vaccination of children ages 5-11 years on COVID-19 disease burden and resilience to new variants in the United States, November 2021-March 2022: a multi-model study. medRxiv 2022:2022.03.08.22271905. [PMID: 35313593 PMCID: PMC8936106 DOI: 10.1101/2022.03.08.22271905] [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] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Background SARS-CoV-2 vaccination of persons aged 12 years and older has reduced disease burden in the United States. The COVID-19 Scenario Modeling Hub convened multiple modeling teams in September 2021 to project the impact of expanding vaccine administration to children 5-11 years old on anticipated COVID-19 burden and resilience against variant strains. Methods Nine modeling teams contributed state- and national-level projections for weekly counts of cases, hospitalizations, and deaths in the United States for the period September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of: 1) presence vs. absence of vaccination of children ages 5-11 years starting on November 1, 2021; and 2) continued dominance of the Delta variant vs. emergence of a hypothetical more transmissible variant on November 15, 2021. Individual team projections were combined using linear pooling. The effect of childhood vaccination on overall and age-specific outcomes was estimated by meta-analysis approaches. Findings Absent a new variant, COVID-19 cases, hospitalizations, and deaths among all ages were projected to decrease nationally through mid-March 2022. Under a set of specific assumptions, models projected that vaccination of children 5-11 years old was associated with reductions in all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880-0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834-0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797-1.020) compared with scenarios where children were not vaccinated. This projected effect of vaccinating children 5-11 years old increased in the presence of a more transmissible variant, assuming no change in vaccine effectiveness by variant. Larger relative reductions in cumulative cases, hospitalizations, and deaths were observed for children than for the entire U.S. population. Substantial state-level variation was projected in epidemic trajectories, vaccine benefits, and variant impacts. Conclusions Results from this multi-model aggregation study suggest that, under a specific set of scenario assumptions, expanding vaccination to children 5-11 years old would provide measurable direct benefits to this age group and indirect benefits to the all-age U.S. population, including resilience to more transmissible variants.
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Affiliation(s)
| | | | | | | | - Claire P Smith
- Johns Hopkins University Infectious Disease Dynamics (JHU-IDD)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Shi Chen
- University of North Carolina at Charlotte (UNCC)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Cécile Viboud
- National Institutes of Health Fogarty International Center
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15
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Winger A, Warren A, Hoops S, Marathe M, Lee DW, Kindwall-Keller T. Computational Modeling of Immune System Interactions during Cytokine Release Syndrome (CRS) and Immune Effector Cell Associated Neurotoxicity Syndrome (ICANS) after Chimeric Antigen Receptor (CAR) T-Cell Therapy. Transplant Cell Ther 2022. [DOI: 10.1016/s2666-6367(22)00336-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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16
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Li GZ, Li A, Marathe M, Srinivasan A, Tsepenekas L, Vullikanti A. Deploying Vaccine Distribution Sites for Improved Accessibility and Equity to Support Pandemic Response. ArXiv 2022:arXiv:2202.04705v1. [PMID: 35169596 PMCID: PMC8845506] [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] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In response to COVID-19, many countries have mandated social distancing and banned large group gatherings in order to slow down the spread of SARS-CoV-2. These social interventions along with vaccines remain the best way forward to reduce the spread of SARS CoV-2. In order to increase vaccine accessibility, states such as Virginia have deployed mobile vaccination centers to distribute vaccines across the state. When choosing where to place these sites, there are two important factors to take into account: accessibility and equity. We formulate a combinatorial problem that captures these factors and then develop efficient algorithms with theoretical guarantees on both of these aspects. Furthermore, we study the inherent hardness of the problem, and demonstrate strong impossibility results. Finally, we run computational experiments on real-world data to show the efficacy of our methods.
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Affiliation(s)
- George Z. Li
- Department of Computer Science, University of Maryland
| | - Ann Li
- Biocomplexity Institute and Initiative, University of Virginia
| | - Madhav Marathe
- Biocomplexity Institute and Initiative, University of Virginia
- Department of Computer Science, University of Virginia
| | | | | | - Anil Vullikanti
- Biocomplexity Institute and Initiative, University of Virginia
- Department of Computer Science, University of Virginia
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17
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Espinoza B, Marathe M, Swarup S, Thakur M. Asymptomatic individuals can increase the final epidemic size under adaptive human behavior. Sci Rep 2021; 11:19744. [PMID: 34611199 PMCID: PMC8492713 DOI: 10.1038/s41598-021-98999-2] [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: 02/08/2021] [Accepted: 09/14/2021] [Indexed: 02/08/2023] Open
Abstract
Infections produced by non-symptomatic (pre-symptomatic and asymptomatic) individuals have been identified as major drivers of COVID-19 transmission. Non-symptomatic individuals, unaware of the infection risk they pose to others, may perceive themselves-and be perceived by others-as not presenting a risk of infection. Yet, many epidemiological models currently in use do not include a behavioral component, and do not address the potential consequences of risk misperception. To study the impact of behavioral adaptations to the perceived infection risk, we use a mathematical model that incorporates the behavioral decisions of individuals, based on a projection of the system's future state over a finite planning horizon. We found that individuals' risk misperception in the presence of non-symptomatic individuals may increase or reduce the final epidemic size. Moreover, under behavioral response the impact of non-symptomatic infections is modulated by symptomatic individuals' behavior. Finally, we found that there is an optimal planning horizon that minimizes the final epidemic size.
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Affiliation(s)
- Baltazar Espinoza
- Biocomplexity Institute and Initiative, Network Systems Science and Advanced Computing Division, University of Virginia, Charlottesville, VA, USA.
| | - Madhav Marathe
- Biocomplexity Institute and Initiative, Network Systems Science and Advanced Computing Division, University of Virginia, Charlottesville, VA, USA
| | - Samarth Swarup
- Biocomplexity Institute and Initiative, Network Systems Science and Advanced Computing Division, University of Virginia, Charlottesville, VA, USA
| | - Mugdha Thakur
- Biocomplexity Institute and Initiative, Network Systems Science and Advanced Computing Division, University of Virginia, Charlottesville, VA, USA
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18
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Sadilek A, Liu L, Nguyen D, Kamruzzaman M, Serghiou S, Rader B, Ingerman A, Mellem S, Kairouz P, Nsoesie EO, MacFarlane J, Vullikanti A, Marathe M, Eastham P, Brownstein JS, Arcas BAY, Howell MD, Hernandez J. Privacy-first health research with federated learning. NPJ Digit Med 2021; 4:132. [PMID: 34493770 PMCID: PMC8423792 DOI: 10.1038/s41746-021-00489-2] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 07/21/2021] [Indexed: 11/29/2022] Open
Abstract
Privacy protection is paramount in conducting health research. However, studies often rely on data stored in a centralized repository, where analysis is done with full access to the sensitive underlying content. Recent advances in federated learning enable building complex machine-learned models that are trained in a distributed fashion. These techniques facilitate the calculation of research study endpoints such that private data never leaves a given device or healthcare system. We show-on a diverse set of single and multi-site health studies-that federated models can achieve similar accuracy, precision, and generalizability, and lead to the same interpretation as standard centralized statistical models while achieving considerably stronger privacy protections and without significantly raising computational costs. This work is the first to apply modern and general federated learning methods that explicitly incorporate differential privacy to clinical and epidemiological research-across a spectrum of units of federation, model architectures, complexity of learning tasks and diseases. As a result, it enables health research participants to remain in control of their data and still contribute to advancing science-aspects that used to be at odds with each other.
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Affiliation(s)
| | | | - Dung Nguyen
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA
- Department of Computer Science, University of Virginia, Charlottesville, VA, USA
| | - Methun Kamruzzaman
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA
| | | | - Benjamin Rader
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA
- Department of Epidemiology, Boston University, Boston, MA, USA
| | | | | | | | | | | | - Anil Vullikanti
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA
- Department of Computer Science, University of Virginia, Charlottesville, VA, USA
| | - Madhav Marathe
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA
- Department of Computer Science, University of Virginia, Charlottesville, VA, USA
| | | | - John S Brownstein
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
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19
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Truelove S, Smith CP, Qin M, Mullany LC, Borchering RK, Lessler J, Shea K, Howerton E, Contamin L, Levander J, Salerno J, Hochheiser H, Kinsey M, Tallaksen K, Wilson S, Shin L, Rainwater-Lovett K, Lemaitre JC, Dent J, Kaminsky J, Lee EC, Perez-Saez J, Hill A, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Piontti APY, Vespignani A, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Schlitt J, Corbett P, Telionis PA, Wang L, Peddireddy AS, Hurt B, Chen J, Vullikanti A, Marathe M, Hoops S, Bhattacharya P, Machi D, Chen S, Paul R, Janies D, Thill JC, Galanti M, Yamana T, Pei S, Shaman J, Reich NG, Healy JM, Slayton RB, Biggerstaff M, Johansson MA, Runge MC, Viboud C. Projected resurgence of COVID-19 in the United States in July-December 2021 resulting from the increased transmissibility of the Delta variant and faltering vaccination. medRxiv 2021:2021.08.28.21262748. [PMID: 34494030 PMCID: PMC8423228 DOI: 10.1101/2021.08.28.21262748] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
WHAT IS ALREADY KNOWN ABOUT THIS TOPIC? The highly transmissible SARS-CoV-2 Delta variant has begun to cause increases in cases, hospitalizations, and deaths in parts of the United States. With slowed vaccination uptake, this novel variant is expected to increase the risk of pandemic resurgence in the US in July-December 2021. WHAT IS ADDED BY THIS REPORT? Data from nine mechanistic models project substantial resurgences of COVID-19 across the US resulting from the more transmissible Delta variant. These resurgences, which have now been observed in most states, were projected to occur across most of the US, coinciding with school and business reopening. Reaching higher vaccine coverage in July-December 2021 reduces the size and duration of the projected resurgence substantially. The expected impact of the outbreak is largely concentrated in a subset of states with lower vaccination coverage. WHAT ARE THE IMPLICATIONS FOR PUBLIC HEALTH PRACTICE? Renewed efforts to increase vaccination uptake are critical to limiting transmission and disease, particularly in states with lower current vaccination coverage. Reaching higher vaccination goals in the coming months can potentially avert 1.5 million cases and 21,000 deaths and improve the ability to safely resume social contacts, and educational and business activities. Continued or renewed non-pharmaceutical interventions, including masking, can also help limit transmission, particularly as schools and businesses reopen.
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Affiliation(s)
- Shaun Truelove
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Claire P Smith
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | | | - Luke C Mullany
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Johns Hopkins University Applied Physics Laboratories, Laurel, Maryland
| | | | | | - Katriona Shea
- The Pennsylvania State University, State College, Pennsylvania
| | - Emily Howerton
- The Pennsylvania State University, State College, Pennsylvania
| | | | | | | | | | - Matt Kinsey
- Johns Hopkins University Applied Physics Laboratories, Laurel, Maryland
| | - Kate Tallaksen
- Johns Hopkins University Applied Physics Laboratories, Laurel, Maryland
| | - Shelby Wilson
- Johns Hopkins University Applied Physics Laboratories, Laurel, Maryland
| | - Lauren Shin
- Johns Hopkins University Applied Physics Laboratories, Laurel, Maryland
| | | | | | - Juan Dent
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Joshua Kaminsky
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Elizabeth C Lee
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Javier Perez-Saez
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Alison Hill
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Dean Karlen
- University of Victoria, Victoria, British Columbia, Canada
| | | | | | - Kunpeng Mu
- Northeastern University, Boston, Massachusetts
| | | | | | | | | | | | | | | | - Bryan Lewis
- University of Virginia, Charlottesville, Virginia
| | - Brian Klahn
- University of Virginia, Charlottesville, Virginia
| | | | | | | | | | - Lijing Wang
- University of Virginia, Charlottesville, Virginia
| | | | | | | | | | | | - Stefan Hoops
- University of Virginia, Charlottesville, Virginia
| | | | - Dustin Machi
- University of Virginia, Charlottesville, Virginia
| | - Shi Chen
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | - Rajib Paul
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | - Daniel Janies
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | - Jean-Claude Thill
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | | | | | | | | | | | | | | | | | | | | | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland
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20
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Borchering RK, Viboud C, Howerton E, Smith CP, Truelove S, Runge MC, Reich NG, Contamin L, Levander J, Salerno J, van Panhuis W, Kinsey M, Tallaksen K, Obrecht RF, Asher L, Costello C, Kelbaugh M, Wilson S, Shin L, Gallagher ME, Mullany LC, Rainwater-Lovett K, Lemaitre JC, Dent J, Grantz KH, Kaminsky J, Lauer SA, Lee EC, Meredith HR, Perez-Saez J, Keegan LT, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Pastore Y Piontti A, Vespignani A, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Schlitt J, Corbett P, Telionis PA, Wang L, Peddireddy AS, Hurt B, Chen J, Vullikanti A, Marathe M, Healy JM, Slayton RB, Biggerstaff M, Johansson MA, Shea K, Lessler J. Modeling of Future COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Rates and Nonpharmaceutical Intervention Scenarios - United States, April-September 2021. MMWR Morb Mortal Wkly Rep 2021. [PMID: 33988185 DOI: 10.15585/mmwr.mm7019e3externalicon] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/27/2023]
Abstract
After a period of rapidly declining U.S. COVID-19 incidence during January-March 2021, increases occurred in several jurisdictions (1,2) despite the rapid rollout of a large-scale vaccination program. This increase coincided with the spread of more transmissible variants of SARS-CoV-2, the virus that causes COVID-19, including B.1.1.7 (1,3) and relaxation of COVID-19 prevention strategies such as those for businesses, large-scale gatherings, and educational activities. To provide long-term projections of potential trends in COVID-19 cases, hospitalizations, and deaths, COVID-19 Scenario Modeling Hub teams used a multiple-model approach comprising six models to assess the potential course of COVID-19 in the United States across four scenarios with different vaccination coverage rates and effectiveness estimates and strength and implementation of nonpharmaceutical interventions (NPIs) (public health policies, such as physical distancing and masking) over a 6-month period (April-September 2021) using data available through March 27, 2021 (4). Among the four scenarios, an accelerated decline in NPI adherence (which encapsulates NPI mandates and population behavior) was shown to undermine vaccination-related gains over the subsequent 2-3 months and, in combination with increased transmissibility of new variants, could lead to surges in cases, hospitalizations, and deaths. A sharp decline in cases was projected by July 2021, with a faster decline in the high-vaccination scenarios. High vaccination rates and compliance with public health prevention measures are essential to control the COVID-19 pandemic and to prevent surges in hospitalizations and deaths in the coming months.
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21
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Borchering RK, Viboud C, Howerton E, Smith CP, Truelove S, Runge MC, Reich NG, Contamin L, Levander J, Salerno J, van Panhuis W, Kinsey M, Tallaksen K, Obrecht RF, Asher L, Costello C, Kelbaugh M, Wilson S, Shin L, Gallagher ME, Mullany LC, Rainwater-Lovett K, Lemaitre JC, Dent J, Grantz KH, Kaminsky J, Lauer SA, Lee EC, Meredith HR, Perez-Saez J, Keegan LT, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Pastore y Piontti A, Vespignani A, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Schlitt J, Corbett P, Telionis PA, Wang L, Peddireddy AS, Hurt B, Chen J, Vullikanti A, Marathe M, Healy JM, Slayton RB, Biggerstaff M, Johansson MA, Shea K, Lessler J. Modeling of Future COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Rates and Nonpharmaceutical Intervention Scenarios - United States, April-September 2021. MMWR Morb Mortal Wkly Rep 2021; 70:719-724. [PMID: 33988185 PMCID: PMC8118153 DOI: 10.15585/mmwr.mm7019e3] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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22
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Adiga A, Wang L, Hurt B, Peddireddy A, Porebski P, Venkatramanan S, Lewis B, Marathe M. All Models Are Useful: Bayesian Ensembling for Robust High Resolution COVID-19 Forecasting. medRxiv 2021:2021.03.12.21253495. [PMID: 33758893 PMCID: PMC7987052 DOI: 10.1101/2021.03.12.21253495] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
UNLABELLED Timely, high-resolution forecasts of infectious disease incidence are useful for policy makers in deciding intervention measures and estimating healthcare resource burden. In this paper, we consider the task of forecasting COVID-19 confirmed cases at the county level for the United States. Although multiple methods have been explored for this task, their performance has varied across space and time due to noisy data and the inherent dynamic nature of the pandemic. We present a forecasting pipeline which incorporates probabilistic forecasts from multiple statistical, machine learning and mechanistic methods through a Bayesian ensembling scheme, and has been operational for nearly 6 months serving local, state and federal policymakers in the United States. While showing that the Bayesian ensemble is at least as good as the individual methods, we also show that each individual method contributes significantly for different spatial regions and time points. We compare our model's performance with other similar models being integrated into CDC-initiated COVID-19 Forecast Hub, and show better performance at longer forecast horizons. Finally, we also describe how such forecasts are used to increase lead time for training mechanistic scenario projections. Our work demonstrates that such a real-time high resolution forecasting pipeline can be developed by integrating multiple methods within a performance-based ensemble to support pandemic response. ACM REFERENCE FORMAT Aniruddha Adiga, Lijing Wang, Benjamin Hurt, Akhil Peddireddy, Przemys-law Porebski,, Srinivasan Venkatramanan, Bryan Lewis, Madhav Marathe. 2021. All Models Are Useful: Bayesian Ensembling for Robust High Resolution COVID-19 Forecasting. In Proceedings of ACM Conference (Conference'17) . ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn.
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23
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Espinoza B, Marathe M, Swarup S, Thakur M. Adaptive Human Behavior in Epidemics: the Impact of Risk Misperception on the Spread of Epidemics. Res Sq 2021:rs.3.rs-220733. [PMID: 33655240 PMCID: PMC7924275 DOI: 10.21203/rs.3.rs-220733/v1] [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] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Infections produced by pre-symptomatic and asymptomatic (non-symptomatic) individuals have been identified as major drivers of COVID-19 transmission. Non-symptomatic individuals unaware of the infection risk they pose to others, may perceive themselves --and being perceived by others-- as not representing risk of infection. Yet many epidemiological models currently in use do not include a behavioral component, and do not address the potential consequences of risk misperception. To study the impact of behavioral adaptations to the perceived infection risk, we use a mathematical model that incorporates individuals' behavioral decisions based on a projection of the future system's state over a finite planning horizon. We found that individuals' risk misperception in the presence of asymptomatic individuals may increase or reduce the final epidemic size. Moreover, under behavioral response the impact of asymptomatic infections is modulated by symptomatic individuals' behavior. Finally, we found that there is an optimal planning horizon that minimizes the final epidemic size.
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Affiliation(s)
- Baltazar Espinoza
- Biocomplexity Institute and Initiative, Network Systems Science and Advanced Computing Division, University of Virginia, Virginia, USA
| | - Madhav Marathe
- Biocomplexity Institute and Initiative, Network Systems Science and Advanced Computing Division, University of Virginia, Virginia, USA
| | - Samarth Swarup
- Biocomplexity Institute and Initiative, Network Systems Science and Advanced Computing Division, University of Virginia, Virginia, USA
| | - Mugdha Thakur
- Biocomplexity Institute and Initiative, Network Systems Science and Advanced Computing Division, University of Virginia, Virginia, USA
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24
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Chen J, Hoops S, Marathe A, Mortveit H, Lewis B, Venkatramanan S, Haddadan A, Bhattacharya P, Adiga A, Vullikanti A, Srinivasan A, Wilson M, Ehrlich G, Fenster M, Eubank S, Barrett C, Marathe M. Prioritizing allocation of COVID-19 vaccines based on social contacts increases vaccination effectiveness. medRxiv 2021:2021.02.04.21251012. [PMID: 33564778 PMCID: PMC7872370 DOI: 10.1101/2021.02.04.21251012] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/28/2023]
Abstract
We study allocation of COVID-19 vaccines to individuals based on the structural properties of their underlying social contact network. Even optimistic estimates suggest that most countries will likely take 6 to 24 months to vaccinate their citizens. These time estimates and the emergence of new viral strains urge us to find quick and effective ways to allocate the vaccines and contain the pandemic. While current approaches use combinations of age-based and occupation-based prioritizations, our strategy marks a departure from such largely aggregate vaccine allocation strategies. We propose a novel approach motivated by recent advances in (i) science of real-world networks that point to efficacy of certain vaccination strategies and (ii) digital technologies that improve our ability to estimate some of these structural properties. Using a realistic representation of a social contact network for the Commonwealth of Virginia, combined with accurate surveillance data on spatiotemporal cases and currently accepted models of within- and between-host disease dynamics, we study how a limited number of vaccine doses can be strategically distributed to individuals to reduce the overall burden of the pandemic. We show that allocation of vaccines based on individuals' degree (number of social contacts) and total social proximity time is significantly more effective than the currently used age-based allocation strategy in terms of number of infections, hospitalizations and deaths. Our results suggest that in just two months, by March 31, 2021, compared to age-based allocation, the proposed degree-based strategy can result in reducing an additional 56-110k infections, 3.2- 5.4k hospitalizations, and 700-900 deaths just in the Commonwealth of Virginia. Extrapolating these results for the entire US, this strategy can lead to 3-6 million fewer infections, 181-306k fewer hospitalizations, and 51-62k fewer deaths compared to age-based allocation. The overall strategy is robust even: (i) if the social contacts are not estimated correctly; (ii) if the vaccine efficacy is lower than expected or only a single dose is given; (iii) if there is a delay in vaccine production and deployment; and (iv) whether or not non-pharmaceutical interventions continue as vaccines are deployed. For reasons of implementability, we have used degree, which is a simple structural measure and can be easily estimated using several methods, including the digital technology available today. These results are significant, especially for resource-poor countries, where vaccines are less available, have lower efficacy, and are more slowly distributed.
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25
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Talekar A, Shriram S, Vaidhiyan N, Aggarwal G, Chen J, Venkatramanan S, Wang L, Adiga A, Sadilek A, Tendulkar A, Marathe M, Sundaresan R, Tambe M. Cohorting to isolate asymptomatic spreaders: An agent-based simulation study on the Mumbai Suburban Railway. ArXiv 2020:arXiv:2012.12839v2. [PMID: 33398245 PMCID: PMC7781320] [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] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Revised: 12/24/2020] [Indexed: 11/06/2022]
Abstract
The Mumbai Suburban Railways, locals, are a key transit infrastructure of the city and is crucial for resuming normal economic activity. Due to high density during transit, the potential risk of disease transmission is high, and the government has taken a wait and see approach to resume normal operations. To reduce disease transmission, policymakers can enforce reduced crowding and mandate wearing of masks. Cohorting - forming groups of travelers that always travel together, is an additional policy to reduce disease transmission on locals without severe restrictions. Cohorting allows us to: (i) form traveler bubbles, thereby decreasing the number of distinct interactions over time; (ii) potentially quarantine an entire cohort if a single case is detected, making contact tracing more efficient, and (iii) target cohorts for testing and early detection of symptomatic as well as asymptomatic cases. Studying impact of cohorts using compartmental models is challenging because of the ensuing representational complexity. Agent-based models provide a natural way to represent cohorts along with the representation of the cohort members with the larger social network. This paper describes a novel multi-scale agent-based model to study the impact of cohorting strategies on COVID-19 dynamics in Mumbai. We achieve this by modeling the Mumbai urban region using a detailed agent-based model comprising of 12.4 million agents. Individual cohorts and their inter-cohort interactions as they travel on locals are modeled using local mean field approximations. The resulting multi-scale model in conjunction with a detailed disease transmission and intervention simulator is used to assess various cohorting strategies. The results provide a quantitative trade-off between cohort size and its impact on disease dynamics and well being. The results show that cohorts can provide significant benefit in terms of reduced transmission without significantly impacting ridership and or economic & social activity.
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26
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Wang L, Ben X, Adiga A, Sadilek A, Tendulkar A, Venkatramanan S, Vullikanti A, Aggarwal G, Talekar A, Chen J, Lewis B, Swarup S, Kapoor A, Tambe M, Marathe M. Using Mobility Data to Understand and Forecast COVID19 Dynamics. medRxiv 2020:2020.12.13.20248129. [PMID: 33354685 PMCID: PMC7755147 DOI: 10.1101/2020.12.13.20248129] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Disease dynamics, human mobility, and public policies co-evolve during a pandemic such as COVID-19. Understanding dynamic human mobility changes and spatial interaction patterns are crucial for understanding and forecasting COVID-19 dynamics. We introduce a novel graph-based neural network(GNN) to incorporate global aggregated mobility flows for a better understanding of the impact of human mobility on COVID-19 dynamics as well as better forecasting of disease dynamics. We propose a recurrent message passing graph neural network that embeds spatio-temporal disease dynamics and human mobility dynamics for daily state-level new confirmed cases forecasting. This work represents one of the early papers on the use of GNNs to forecast COVID-19 incidence dynamics and our methods are competitive to existing methods. We show that the spatial and temporal dynamic mobility graph leveraged by the graph neural network enables better long-term forecasting performance compared to baselines.
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27
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Chen J, Vullikanti A, Santos J, Venkatramanan S, Hoops S, Mortveit H, Lewis B, You W, Eubank S, Marathe M, Barrett C, Marathe A. Epidemiological and Economic Impact of COVID-19 in the US. medRxiv 2020. [PMID: 33269363 DOI: 10.1101/2020.11.28.20239517] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
This research measures the epidemiological and economic impact of COVID-19 spread in the US under different mitigation scenarios, comprising of non-pharmaceutical interventions. A detailed disease model of COVID-19 is combined with a model of the US economy to estimate the direct impact of labor supply shock to each sector arising from morbidity, mortality, and lock down, as well as the indirect impact caused by the interdependencies between sectors. During a lockdown, estimates of jobs that are workable from home in each sector are used to modify the shock to labor supply. Results show trade-offs between economic losses, and lives saved and infections averted are non-linear in compliance to social distancing and the duration of lockdown. Sectors that are worst hit are not the labor-intensive sectors such as Agriculture and Construction, but the ones with high valued jobs such as Professional Services, even after the teleworkability of jobs is accounted for. Additionally, the findings show that a low compliance to interventions can be overcome by a longer shutdown period and vice versa to arrive at similar epidemiological impact but their net effect on economic loss depends on the interplay between the marginal gains from averting infections and deaths, versus the marginal loss from having healthy workers stay at home during the shutdown.
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28
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Adiga A, Chen J, Marathe M, Mortveit H, Venkatramanan S, Vullikanti A. Data-Driven Modeling for Different Stages of Pandemic Response. J Indian Inst Sci 2020; 100:901-915. [PMID: 33223629 PMCID: PMC7667282 DOI: 10.1007/s41745-020-00206-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [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] [Received: 08/27/2020] [Accepted: 09/15/2020] [Indexed: 12/12/2022]
Abstract
Some of the key questions of interest during the COVID-19 pandemic (and all outbreaks) include: where did the disease start, how is it spreading, who are at risk, and how to control the spread. There are a large number of complex factors driving the spread of pandemics, and, as a result, multiple modeling techniques play an increasingly important role in shaping public policy and decision-making. As different countries and regions go through phases of the pandemic, the questions and data availability also change. Especially of interest is aligning model development and data collection to support response efforts at each stage of the pandemic. The COVID-19 pandemic has been unprecedented in terms of real-time collection and dissemination of a number of diverse datasets, ranging from disease outcomes, to mobility, behaviors, and socio-economic factors. The data sets have been critical from the perspective of disease modeling and analytics to support policymakers in real time. In this overview article, we survey the data landscape around COVID-19, with a focus on how such datasets have aided modeling and response through different stages so far in the pandemic. We also discuss some of the current challenges and the needs that will arise as we plan our way out of the pandemic.
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Affiliation(s)
- Aniruddha Adiga
- Biocomplexity Institute and Initiative, Charlottesville, USA
| | - Jiangzhuo Chen
- Biocomplexity Institute and Initiative, Charlottesville, USA
| | - Madhav Marathe
- Biocomplexity Institute and Initiative, Charlottesville, USA.,Department of Computer Science, University of Virginia, Charlottesville, USA
| | - Henning Mortveit
- Biocomplexity Institute and Initiative, Charlottesville, USA.,Department of Systems Engineering and Environment, University of Virginia, Charlottesville, USA
| | | | - Anil Vullikanti
- Biocomplexity Institute and Initiative, Charlottesville, USA.,Department of Computer Science, University of Virginia, Charlottesville, USA
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29
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Peddireddy AS, Xie D, Patil P, Wilson ML, Machi D, Venkatramanan S, Klahn B, Porebski P, Bhattacharya P, Dumbre S, Raymond E, Marathe M. From 5Vs to 6Cs: Operationalizing Epidemic Data Management with COVID-19 Surveillance. medRxiv 2020:2020.10.27.20220830. [PMID: 33140060 PMCID: PMC7605571 DOI: 10.1101/2020.10.27.20220830] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
The COVID-19 pandemic brought to the forefront an unprecedented need for experts, as well as citizens, to visualize spatio-temporal disease surveillance data. Web application dashboards were quickly developed to fill this gap, including those built by JHU, WHO, and CDC, but all of these dashboards supported a particular niche view of the pandemic (ie, current status or specific regions). In this paper, we describe our work developing our own COVID-19 Surveillance Dashboard, available at https://nssac.bii.virginia.edu/covid-19/dashboard/, which offers a universal view of the pandemic while also allowing users to focus on the details that interest them. From the beginning, our goal was to provide a simple visual way to compare, organize, and track near-real-time surveillance data as the pandemic progresses. Our dashboard includes a number of advanced features for zooming, filtering, categorizing and visualizing multiple time series on a single canvas. In developing this dashboard, we have also identified 6 key metrics we call the 6Cs standard which we propose as a standard for the design and evaluation of real-time epidemic science dashboards. Our dashboard was one of the first released to the public, and remains one of the most visited and highly used. Our group uses it to support federal, state and local public health authorities, and it is used by people worldwide to track the pandemic evolution, build their own dashboards, and support their organizations as they plan their responses to the pandemic. We illustrate the utility of our dashboard by describing how it can be used to support data story-telling - an important emerging area in data science.
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Affiliation(s)
| | - Dawen Xie
- Biocomplexity Institute & Initiative, University of Virginia
| | | | - Mandy L Wilson
- Biocomplexity Institute & Initiative, University of Virginia
| | - Dustin Machi
- Biocomplexity Institute & Initiative, University of Virginia
| | | | - Brian Klahn
- Biocomplexity Institute & Initiative, University of Virginia
| | | | | | | | - Erin Raymond
- Biocomplexity Institute & Initiative, University of Virginia
| | - Madhav Marathe
- Biocomplexity Institute & Initiative, University of Virginia
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30
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Chen J, Vullikanti A, Hoops S, Mortveit H, Lewis B, Venkatramanan S, You W, Eubank S, Marathe M, Barrett C, Marathe A. Medical costs of keeping the US economy open during COVID-19. Sci Rep 2020; 10:18422. [PMID: 33116179 PMCID: PMC7595181 DOI: 10.1038/s41598-020-75280-6] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 10/12/2020] [Indexed: 11/17/2022] Open
Abstract
We use an individual based model and national level epidemic simulations to estimate the medical costs of keeping the US economy open during COVID-19 pandemic under different counterfactual scenarios. We model an unmitigated scenario and 12 mitigation scenarios which differ in compliance behavior to social distancing strategies and in the duration of the stay-home order. Under each scenario we estimate the number of people who are likely to get infected and require medical attention, hospitalization, and ventilators. Given the per capita medical cost for each of these health states, we compute the total medical costs for each scenario and show the tradeoffs between deaths, costs, infections, compliance and the duration of stay-home order. We also consider the hospital bed capacity of each Hospital Referral Region (HRR) in the US to estimate the deficit in beds each HRR will likely encounter given the demand for hospital beds. We consider a case where HRRs share hospital beds among the neighboring HRRs during a surge in demand beyond the available beds and the impact it has in controlling additional deaths.
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Affiliation(s)
- Jiangzhuo Chen
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA, 22904, USA
| | - Anil Vullikanti
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA, 22904, USA.,Department of Computer Science, University of Virginia, Charlottesville, USA
| | - Stefan Hoops
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA, 22904, USA
| | - Henning Mortveit
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA, 22904, USA.,Department of Engineering Systems and Environment, University of Virginia, Charlottesville, USA
| | - Bryan Lewis
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA, 22904, USA
| | - Srinivasan Venkatramanan
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA, 22904, USA
| | - Wen You
- Department of Public Health Sciences, University of Virginia, Charlottesville, USA
| | - Stephen Eubank
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA, 22904, USA.,Department of Public Health Sciences, University of Virginia, Charlottesville, USA
| | - Madhav Marathe
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA, 22904, USA.,Department of Computer Science, University of Virginia, Charlottesville, USA
| | - Chris Barrett
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA, 22904, USA.,Department of Computer Science, University of Virginia, Charlottesville, USA
| | - Achla Marathe
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA, 22904, USA. .,Department of Public Health Sciences, University of Virginia, Charlottesville, USA.
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31
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Adiga A, Chen J, Marathe M, Mortveit H, Venkatramanan S, Vullikanti A. Data-driven modeling for different stages of pandemic response. ArXiv 2020:arXiv:2009.10018v1. [PMID: 32995364 PMCID: PMC7523119] [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] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Some of the key questions of interest during the COVID-19 pandemic (and all outbreaks) include: where did the disease start, how is it spreading, who is at risk, and how to control the spread. There are a large number of complex factors driving the spread of pandemics, and, as a result, multiple modeling techniques play an increasingly important role in shaping public policy and decision making. As different countries and regions go through phases of the pandemic, the questions and data availability also changes. Especially of interest is aligning model development and data collection to support response efforts at each stage of the pandemic. The COVID-19 pandemic has been unprecedented in terms of real-time collection and dissemination of a number of diverse datasets, ranging from disease outcomes, to mobility, behaviors, and socio-economic factors. The data sets have been critical from the perspective of disease modeling and analytics to support policymakers in real-time. In this overview article, we survey the data landscape around COVID-19, with a focus on how such datasets have aided modeling and response through different stages so far in the pandemic. We also discuss some of the current challenges and the needs that will arise as we plan our way out of the pandemic.
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Affiliation(s)
| | | | - Madhav Marathe
- Biocomplexity Institute and Inititiative
- Department of Computer Science, University of Virginia
| | - Henning Mortveit
- Biocomplexity Institute and Inititiative
- Department of Systems Engineering and Environment
| | | | - Anil Vullikanti
- Biocomplexity Institute and Inititiative
- Department of Computer Science, University of Virginia
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32
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Sambaturu P, Bhattacharya P, Chen J, Lewis B, Marathe M, Venkatramanan S, Vullikanti A. An Automated Approach for Finding Spatio-Temporal Patterns of Seasonal Influenza in the United States: Algorithm Validation Study. JMIR Public Health Surveill 2020; 6:e12842. [PMID: 32701458 PMCID: PMC7501584 DOI: 10.2196/12842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Revised: 06/16/2019] [Accepted: 04/03/2020] [Indexed: 11/26/2022] Open
Abstract
Background Agencies such as the Centers for Disease Control and Prevention (CDC) currently release influenza-like illness incidence data, along with descriptive summaries of simple spatio-temporal patterns and trends. However, public health researchers, government agencies, as well as the general public, are often interested in deeper patterns and insights into how the disease is spreading, with additional context. Analysis by domain experts is needed for deriving such insights from incidence data. Objective Our goal was to develop an automated approach for finding interesting spatio-temporal patterns in the spread of a disease over a large region, such as regions which have specific characteristics (eg, high incidence in a particular week, those which showed a sudden change in incidence) or regions which have significantly different incidence compared to earlier seasons. Methods We developed techniques from the area of transactional data mining for characterizing and finding interesting spatio-temporal patterns in disease spread in an automated manner. A key part of our approach involved using the principle of minimum description length for representing a given target set in terms of combinations of attributes (referred to as clauses); we considered both positive and negative clauses, relaxed descriptions which approximately represent the set, and used integer programming to find such descriptions. Finally, we designed an automated approach, which examines a large space of sets corresponding to different spatio-temporal patterns, and ranks them based on the ratio of their size to their description length (referred to as the compression ratio). Results We applied our methods using minimum description length to find spatio-temporal patterns in the spread of seasonal influenza in the United States using state level influenza-like illness activity indicator data from the CDC. We observed that the compression ratios were over 2.5 for 50% of the chosen sets, when approximate descriptions and negative clauses were allowed. Sets with high compression ratios (eg, over 2.5) corresponded to interesting patterns in the spatio-temporal dynamics of influenza-like illness. Our approach also outperformed description by solution in terms of the compression ratio. Conclusions Our approach, which is an unsupervised machine learning method, can provide new insights into patterns and trends in the disease spread in an automated manner. Our results show that the description complexity is an effective approach for characterizing sets of interest, which can be easily extended to other diseases and regions beyond influenza in the US. Our approach can also be easily adapted for automated generation of narratives.
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Affiliation(s)
| | | | - Jiangzhuo Chen
- University of Virginia, Charlottesville, VA, United States
| | - Bryan Lewis
- University of Virginia, Charlottesville, VA, United States
| | - Madhav Marathe
- University of Virginia, Charlottesville, VA, United States
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Venkatramanan S, Wu S, Shi B, Marathe A, Marathe M, Eubank S, Sah LP, Giri AP, Colavito LA, Nitin KS, Sridhar V, Asokan R, Muniappan R, Norton G, Adiga A. Modeling Commodity Flow in the Context of Invasive Species Spread: Study of Tuta absoluta in Nepal. Crop Prot 2020; 135:104736. [PMID: 32742052 PMCID: PMC7394466 DOI: 10.1016/j.cropro.2019.02.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Trade and transport of goods is widely accepted as a primary pathway for the introduction and dispersal of invasive species. However, understanding commodity flows remains a challenge owing to its complex nature, unavailability of quality data, and lack of systematic modeling methods. A robust network-based approach is proposed to model seasonal flow of agricultural produce and examine its role in pest spread. It is applied to study the spread of Tuta absoluta, a devastating pest of tomato in Nepal. Further, the long-term establishment potential of the pest and its economic impact on the country are assessed. Our analysis indicates that regional trade plays an important role in the spread of T. absoluta. The economic impact of this invasion could range from USD 17-25 million. The proposed approach is generic and particularly suited for data-poor scenarios.
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Affiliation(s)
- S Venkatramanan
- Biocomplexity Institute & Initiative, University of Virginia
| | - S Wu
- Department of Computer Science, Virginia Tech
| | - B Shi
- Department of Economics, Virginia Tech
| | - A Marathe
- Biocomplexity Institute & Initiative, University of Virginia
- Department of Public Health Sciences, University of Virginia
| | - M Marathe
- Biocomplexity Institute & Initiative, University of Virginia
- Department of Computer Science, University of Virginia
| | - S Eubank
- Biocomplexity Institute & Initiative, University of Virginia
- Department of Public Health Sciences, University of Virginia
| | - L P Sah
- Feed the Future Integrated Pest Management Innovation Lab
- Feed the Future Asian Vegetable and Mango Innovation Lab
- International Development Enterprises, Nepal
| | - A P Giri
- Feed the Future Integrated Pest Management Innovation Lab
- Feed the Future Asian Vegetable and Mango Innovation Lab
- International Development Enterprises, Nepal
| | - L A Colavito
- Feed the Future Integrated Pest Management Innovation Lab
- Feed the Future Asian Vegetable and Mango Innovation Lab
- International Development Enterprises, Nepal
| | - K S Nitin
- Indian Institute of Horticultural Research
| | - V Sridhar
- Indian Institute of Horticultural Research
| | - R Asokan
- Indian Institute of Horticultural Research
| | - R Muniappan
- Feed the Future Integrated Pest Management Innovation Lab
| | - G Norton
- Department of Agriculture and Applied Economics, Virginia Tech
| | - A Adiga
- Biocomplexity Institute & Initiative, University of Virginia
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Adiga A, Wang L, Sadilek A, Tendulkar A, Venkatramanan S, Vullikanti A, Aggarwal G, Talekar A, Ben X, Chen J, Lewis B, Swarup S, Tambe M, Marathe M. Interplay of global multi-scale human mobility, social distancing, government interventions, and COVID-19 dynamics. medRxiv 2020. [PMID: 32577671 DOI: 10.1101/2020.06.05.20123760] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
This work quantifies mobility changes observed during the different phases of the pandemic world-wide at multiple resolutions -- county, state, country -- using an anonymized aggregate mobility map that captures population flows between geographic cells of size 5 km 2 . As we overlay the global mobility map with epidemic incidence curves and dates of government interventions, we observe that as case counts rose, mobility fell and has since then seen a slow but steady increase in flows. Further, in order to understand mixing within a region, we propose a new metric to quantify the effect of social distancing on the basis of mobility.Taking two very different countries sampled from the global spectrum, We analyze in detail the mobility patterns of the United States (US) and India. We then carry out a counterfactual analysis of delaying the lockdown and show that a one week delay would have doubled the reported number of cases in the US and India. Finally, we quantify the effect of college students returning back to school for the fall semester on COVID-19 dynamics in the surrounding community. We employ the data from a recent university outbreak (reported on August 16, 2020) to infer possible R eff values and mobility flows combined with daily prevalence data and census data to obtain an estimate of new cases that might arrive on a college campus. We find that maintaining social distancing at existing levels would be effective in mitigating the extra seeding of cases. However, potential behavioral change and increased social interaction amongst students (30% increase in R eff ) along with extra seeding can increase the number of cases by 20% over a period of one month in the encompassing county. To our knowledge, this work is the first to model in near real-time, the interplay of human mobility, epidemic dynamics and public policies across multiple spatial resolutions and at a global scale.
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Chen J, Vullikanti A, Hoops S, Mortveit H, Lewis B, Venkatramanan S, You W, Eubank S, Marathe M, Barrett C, Marathe A. Medical Costs of Keeping the US Economy Open During COVID-19. medRxiv 2020. [PMID: 32743613 DOI: 10.1101/2020.07.17.20156232] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
We use an individual based model and national level epidemic simulations to estimate the medical costs of keeping the US economy open during COVID-19 pandemic under different counterfactual scenarios. We model an unmitigated scenario and 12 mitigation scenarios which differ in compliance behavior to social distancing strategies and to the duration of the stay-home order. Under each scenario we estimate the number of people who are likely to get infected and require medical attention, hospitalization, and ventilators. Given the per capita medical cost for each of these health states, we compute the total medical costs for each scenario and show the tradeoffs between deaths, costs, infections, compliance and the duration of stay-home order. We also consider the hospital bed capacity of each Hospital Referral Region (HRR) in the US to estimate the deficit in beds each HRR will likely encounter given the demand for hospital beds. We consider a case where HRRs share hospital beds among the neighboring HRRs during a surge in demand beyond the available beds and the impact it has in controlling additional deaths.
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Eubank S, Eckstrand I, Lewis B, Venkatramanan S, Marathe M, Barrett CL. Commentary on Ferguson, et al., "Impact of Non-pharmaceutical Interventions (NPIs) to Reduce COVID-19 Mortality and Healthcare Demand". Bull Math Biol 2020; 82:52. [PMID: 32270376 PMCID: PMC7140590 DOI: 10.1007/s11538-020-00726-x] [Citation(s) in RCA: 149] [Impact Index Per Article: 37.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 03/25/2020] [Indexed: 01/13/2023]
Abstract
A recent manuscript (Ferguson et al. in Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand, Imperial College COVID-19 Response Team, London, 2020. https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdf) from Imperial College modelers examining ways to mitigate and control the spread of COVID-19 has attracted much attention. In this paper, we will discuss a coarse taxonomy of models and explore the context and significance of the Imperial College and other models in contributing to the analysis of COVID-19.
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Affiliation(s)
- S Eubank
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, USA. .,Department of Public Health Sciences, University of Virginia, Charlottesville, USA.
| | - I Eckstrand
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, USA
| | - B Lewis
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, USA
| | - S Venkatramanan
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, USA
| | - M Marathe
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, USA.,Department of Computer Science, University of Virginia, Charlottesville, USA
| | - C L Barrett
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, USA.,Department of Computer Science, University of Virginia, Charlottesville, USA
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Adiga A, Venkatramanan S, Schlitt J, Peddireddy A, Dickerman A, Bura A, Warren A, Klahn BD, Mao C, Xie D, Machi D, Raymond E, Meng F, Barrow G, Mortveit H, Chen J, Walke J, Goldstein J, Wilson ML, Orr M, Porebski P, Telionis PA, Beckman R, Hoops S, Eubank S, Baek YY, Lewis B, Marathe M, Barrett C. Evaluating the impact of international airline suspensions on the early global spread of COVID-19. medRxiv 2020:2020.02.20.20025882. [PMID: 32511466 PMCID: PMC7255786 DOI: 10.1101/2020.02.20.20025882] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/13/2023]
Abstract
Global airline networks play a key role in the global importation of emerging infectious diseases. Detailed information on air traffic between international airports has been demonstrated to be useful in retrospectively validating and prospectively predicting case emergence in other countries. In this paper, we use a well-established metric known as effective distance on the global air traffic data from IATA to quantify risk of emergence for different countries as a consequence of direct importation from China, and compare it against arrival times for the first 24 countries. Using this model trained on official first reports from WHO, we estimate time of arrival (ToA) for all other countries. We then incorporate data on airline suspensions to recompute the effective distance and assess the effect of such cancellations in delaying the estimated arrival time for all other countries. Finally we use the infectious disease vulnerability indices to explain some of the estimated reporting delays.
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Rangarajan P, Mody SK, Marathe M. Forecasting dengue and influenza incidences using a sparse representation of Google trends, electronic health records, and time series data. PLoS Comput Biol 2019; 15:e1007518. [PMID: 31751346 PMCID: PMC6894887 DOI: 10.1371/journal.pcbi.1007518] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 12/05/2019] [Accepted: 10/29/2019] [Indexed: 12/20/2022] Open
Abstract
Dengue and influenza-like illness (ILI) are two of the leading causes of viral infection in the world and it is estimated that more than half the world’s population is at risk for developing these infections. It is therefore important to develop accurate methods for forecasting dengue and ILI incidences. Since data from multiple sources (such as dengue and ILI case counts, electronic health records and frequency of multiple internet search terms from Google Trends) can improve forecasts, standard time series analysis methods are inadequate to estimate all the parameter values from the limited amount of data available if we use multiple sources. In this paper, we use a computationally efficient implementation of the known variable selection method that we call the Autoregressive Likelihood Ratio (ARLR) method. This method combines sparse representation of time series data, electronic health records data (for ILI) and Google Trends data to forecast dengue and ILI incidences. This sparse representation method uses an algorithm that maximizes an appropriate likelihood ratio at every step. Using numerical experiments, we demonstrate that our method recovers the underlying sparse model much more accurately than the lasso method. We apply our method to dengue case count data from five countries/states: Brazil, Mexico, Singapore, Taiwan, and Thailand and to ILI case count data from the United States. Numerical experiments show that our method outperforms existing time series forecasting methods in forecasting the dengue and ILI case counts. In particular, our method gives a 18 percent forecast error reduction over a leading method that also uses data from multiple sources. It also performs better than other methods in predicting the peak value of the case count and the peak time. Dengue and influenza-like illness (ILI) are leading causes of viral infection in the world and hence it is important to develop accurate methods for forecasting their incidence. We use Autoregressive Likelihood Ratio method, which is a computationally efficient implementation of the variable selection method, in order to obtain a sparse (non-lasso) representation of time series, Google Trends and electronic health records (for ILI) data. This method is used to forecast dengue incidence in five countries/states and ILI incidence in USA. We show that this method outperforms existing time series methods in forecasting these diseases. The method is general and can also be used to forecast other diseases.
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Affiliation(s)
- Prashant Rangarajan
- Departments of Computer Science and Mathematics, Birla Institute of Technology and Science, Pilani, India
| | - Sandeep K. Mody
- Department of Mathematics, Indian Institute of Science, Bangalore, India
| | - Madhav Marathe
- Department of Computer Science, Network, Simulation Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, Virginia, United States of America
- * E-mail:
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McNitt J, Chungbaek YY, Mortveit H, Marathe M, Campos MR, Desneux N, Brévault T, Muniappan R, Adiga A. Assessing the multi-pathway threat from an invasive agricultural pest: Tuta absoluta in Asia. Proc Biol Sci 2019; 286:20191159. [PMID: 31615355 PMCID: PMC6834037 DOI: 10.1098/rspb.2019.1159] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 09/20/2019] [Indexed: 11/12/2022] Open
Abstract
Modern food systems facilitate rapid dispersal of pests and pathogens through multiple pathways. The complexity of spread dynamics and data inadequacy make it challenging to model the phenomenon and also to prepare for emerging invasions. We present a generic framework to study the spatio-temporal spread of invasive species as a multi-scale propagation process over a time-varying network accounting for climate, biology, seasonal production, trade and demographic information. Machine learning techniques are used in a novel manner to capture model variability and analyse parameter sensitivity. We applied the framework to understand the spread of a devastating pest of tomato, Tuta absoluta, in South and Southeast Asia, a region at the frontier of its current range. Analysis with respect to historical invasion records suggests that even with modest self-mediated spread capabilities, the pest can quickly expand its range through domestic city-to-city vegetable trade. Our models forecast that within 5-7 years, Tuta absoluta will invade all major vegetable growing areas of mainland Southeast Asia assuming unmitigated spread. Monitoring high-consumption areas can help in early detection, and targeted interventions at major production areas can effectively reduce the rate of spread.
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Affiliation(s)
| | - Young Yun Chungbaek
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Henning Mortveit
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Madhav Marathe
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Mateus R. Campos
- French National Institute for Agricultural Research, Avignon, Provence-Alpes-Côte d’Azu, France
| | - Nicolas Desneux
- Université Côte d'Azur, INRA, CNRS, UMR ISA, 06000, Nice, France
| | - Thierry Brévault
- BIOPASS, CIRAD-IRD-ISRA-UCAD, Dakar, Senegal
- CIRAD, UPR AIDA, Centre de Recherche ISRA-IRD, Dakar, Senegal
- AIDA, Université de Montpellier, CIRAD, Montpellier, France
| | - Rangaswamy Muniappan
- Feed the Future Integrated Pest Management Innovation Laboratory, Virginia Tech, Blacksburg VA, USA
| | - Abhijin Adiga
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
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Affiliation(s)
- Prithwish Chakraborty
- Discovery Analytics Center, Virginia Tech, Blacksburg, Virginia, United States of America
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Bryan Lewis
- Biocomplexity Institute, University of Virginia, Charlottesville, Virginia, United States of America
| | - Stephen Eubank
- Network Dynamics and Simulation Science Laboratory, Biocomplexity Institute, Virginia Tech, Blacksburg, Virginia, United States of America
| | - John S. Brownstein
- Children's Hospital Informatics Program, Boston Children’s Hospital, Massachusetts, United States of America
- Department of Pediatrics, Harvard Medical School, Massachusetts, United States of America
| | - Madhav Marathe
- Biocomplexity Institute, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Computer Science, University of Virginia, Charlottesville, Virginia, United States of America
| | - Naren Ramakrishnan
- Discovery Analytics Center, Virginia Tech, Blacksburg, Virginia, United States of America
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia, United States of America
- * E-mail:
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Abstract
We study the feedback processes between individual behavior, disease prevalence, interventions and social networks during an influenza pandemic when a limited stockpile of antivirals is shared between the private and the public sectors. An economic model that uses prevalence-elastic demand for interventions is combined with a detailed social network and a disease propagation model to understand the feedback mechanism between epidemic dynamics, market behavior, individual perceptions, and the social network. An urban and a rural region are simulated to assess the robustness of results. Results show that an optimal split between the private and public sectors can be reached to contain the disease but the accessibility of antivirals from the private sector is skewed towards the richest income quartile. Also, larger allocations to the private sector result in wastage where individuals who do not need it are able to purchase it but who need it cannot afford it. Disease prevalence increases with household size and total contact time but not by degree in the social network, whereas wastage of antivirals decreases with degree and contact time. The best utilization of drugs is achieved when individuals with high contact time use them, who tend to be the school-aged children of large families.
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Affiliation(s)
- Jiangzhuo Chen
- Network Dynamics and Simulation Science Laboratory, Virginia Tech, Blacksburg, VA, 24061, USA.
| | - Achla Marathe
- Network Dynamics and Simulation Science Laboratory, Virginia Tech, Blacksburg, VA, 24061, USA
- Department of Agricultural and Applied Economics, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Madhav Marathe
- Network Dynamics and Simulation Science Laboratory, Virginia Tech, Blacksburg, VA, 24061, USA
- Department of Computer Science, Virginia Tech, Blacksburg, VA, 24061, USA
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Adiga A, Chu S, Eubank S, Kuhlman CJ, Lewis B, Marathe A, Marathe M, Nordberg EK, Swarup S, Vullikanti A, Wilson ML. Disparities in spread and control of influenza in slums of Delhi: findings from an agent-based modelling study. BMJ Open 2018; 8:e017353. [PMID: 29358419 PMCID: PMC5780711 DOI: 10.1136/bmjopen-2017-017353] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
OBJECTIVES This research studies the role of slums in the spread and control of infectious diseases in the National Capital Territory of India, Delhi, using detailed social contact networks of its residents. METHODS We use an agent-based model to study the spread of influenza in Delhi through person-to-person contact. Two different networks are used: one in which slum and non-slum regions are treated the same, and the other in which 298 slum zones are identified. In the second network, slum-specific demographics and activities are assigned to the individuals whose homes reside inside these zones. The main effects of integrating slums are that the network has more home-related contacts due to larger family sizes and more outside contacts due to more daily activities outside home. Various vaccination and social distancing interventions are applied to control the spread of influenza. RESULTS Simulation-based results show that when slum attributes are ignored, the effectiveness of vaccination can be overestimated by 30%-55%, in terms of reducing the peak number of infections and the size of the epidemic, and in delaying the time to peak infection. The slum population sustains greater infection rates under all intervention scenarios in the network that treats slums differently. Vaccination strategy performs better than social distancing strategies in slums. CONCLUSIONS Unique characteristics of slums play a significant role in the spread of infectious diseases. Modelling slums and estimating their impact on epidemics will help policy makers and regulators more accurately prioritise allocation of scarce medical resources and implement public health policies.
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Affiliation(s)
- Abhijin Adiga
- Network Dynamics and Simulation Sciences Laboratory, Biocomplexity Institute, Virginia Tech, Blacksburg, Virginia, USA
| | - Shuyu Chu
- Network Dynamics and Simulation Sciences Laboratory, Biocomplexity Institute, Virginia Tech, Blacksburg, Virginia, USA
| | - Stephen Eubank
- Network Dynamics and Simulation Sciences Laboratory, Biocomplexity Institute, Virginia Tech, Blacksburg, Virginia, USA
| | - Christopher J Kuhlman
- Network Dynamics and Simulation Sciences Laboratory, Biocomplexity Institute, Virginia Tech, Blacksburg, Virginia, USA
| | - Bryan Lewis
- Network Dynamics and Simulation Sciences Laboratory, Biocomplexity Institute, Virginia Tech, Blacksburg, Virginia, USA
| | - Achla Marathe
- Network Dynamics and Simulation Sciences Laboratory, Biocomplexity Institute, Virginia Tech, Blacksburg, Virginia, USA
| | - Madhav Marathe
- Network Dynamics and Simulation Sciences Laboratory, Biocomplexity Institute, Virginia Tech, Blacksburg, Virginia, USA
| | - Eric K Nordberg
- Network Dynamics and Simulation Sciences Laboratory, Biocomplexity Institute, Virginia Tech, Blacksburg, Virginia, USA
| | - Samarth Swarup
- Network Dynamics and Simulation Sciences Laboratory, Biocomplexity Institute, Virginia Tech, Blacksburg, Virginia, USA
| | - Anil Vullikanti
- Network Dynamics and Simulation Sciences Laboratory, Biocomplexity Institute, Virginia Tech, Blacksburg, Virginia, USA
| | - Mandy L Wilson
- Network Dynamics and Simulation Sciences Laboratory, Biocomplexity Institute, Virginia Tech, Blacksburg, Virginia, USA
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Marathe M, Tanner K, Jones J, Russon H, Morgan A. Assessment of a new small cell lung cancer pathway at New Cross Hospital, Wolverhampton. Lung Cancer 2018. [DOI: 10.1016/s0169-5002(18)30055-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Bhuiyan H, Khan M, Chen J, Marathe M. Parallel Algorithms for Switching Edges in Heterogeneous Graphs. J Parallel Distrib Comput 2017; 104:19-35. [PMID: 28757680 PMCID: PMC5526649 DOI: 10.1016/j.jpdc.2016.12.005] [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] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
An edge switch is an operation on a graph (or network) where two edges are selected randomly and one of their end vertices are swapped with each other. Edge switch operations have important applications in graph theory and network analysis, such as in generating random networks with a given degree sequence, modeling and analyzing dynamic networks, and in studying various dynamic phenomena over a network. The recent growth of real-world networks motivates the need for efficient parallel algorithms. The dependencies among successive edge switch operations and the requirement to keep the graph simple (i.e., no self-loops or parallel edges) as the edges are switched lead to significant challenges in designing a parallel algorithm. Addressing these challenges requires complex synchronization and communication among the processors leading to difficulties in achieving a good speedup by parallelization. In this paper, we present distributed memory parallel algorithms for switching edges in massive networks. These algorithms provide good speedup and scale well to a large number of processors. A harmonic mean speedup of 73.25 is achieved on eight different networks with 1024 processors. One of the steps in our edge switch algorithms requires the computation of multinomial random variables in parallel. This paper presents the first non-trivial parallel algorithm for the problem, achieving a speedup of 925 using 1024 processors.
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Affiliation(s)
- Hasanuzzaman Bhuiyan
- Department of Computer Science, Virginia Tech, 2202 Kraft Drive, Blacksburg, VA 24061, USA
- Network Dynamics and Simulation Science Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA
| | - Maleq Khan
- Department of Electrical Engineering and Computer Science, Texas A&M University - Kingsville, Kingsville, TX 78363, USA
| | - Jiangzhuo Chen
- Network Dynamics and Simulation Science Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA
| | - Madhav Marathe
- Department of Computer Science, Virginia Tech, 2202 Kraft Drive, Blacksburg, VA 24061, USA
- Network Dynamics and Simulation Science Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA
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Tabataba FS, Chakraborty P, Ramakrishnan N, Venkatramanan S, Chen J, Lewis B, Marathe M. A framework for evaluating epidemic forecasts. BMC Infect Dis 2017; 17:345. [PMID: 28506278 PMCID: PMC5433189 DOI: 10.1186/s12879-017-2365-1] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [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: 07/28/2016] [Accepted: 03/29/2017] [Indexed: 11/16/2022] Open
Abstract
Background Over the past few decades, numerous forecasting methods have been proposed in the field of epidemic forecasting. Such methods can be classified into different categories such as deterministic vs. probabilistic, comparative methods vs. generative methods, and so on. In some of the more popular comparative methods, researchers compare observed epidemiological data from the early stages of an outbreak with the output of proposed models to forecast the future trend and prevalence of the pandemic. A significant problem in this area is the lack of standard well-defined evaluation measures to select the best algorithm among different ones, as well as for selecting the best possible configuration for a particular algorithm. Results In this paper we present an evaluation framework which allows for combining different features, error measures, and ranking schema to evaluate forecasts. We describe the various epidemic features (Epi-features) included to characterize the output of forecasting methods and provide suitable error measures that could be used to evaluate the accuracy of the methods with respect to these Epi-features. We focus on long-term predictions rather than short-term forecasting and demonstrate the utility of the framework by evaluating six forecasting methods for predicting influenza in the United States. Our results demonstrate that different error measures lead to different rankings even for a single Epi-feature. Further, our experimental analyses show that no single method dominates the rest in predicting all Epi-features when evaluated across error measures. As an alternative, we provide various Consensus Ranking schema that summarize individual rankings, thus accounting for different error measures. Since each Epi-feature presents a different aspect of the epidemic, multiple methods need to be combined to provide a comprehensive forecast. Thus we call for a more nuanced approach while evaluating epidemic forecasts and we believe that a comprehensive evaluation framework, as presented in this paper, will add value to the computational epidemiology community. Electronic supplementary material The online version of this article (doi:10.1186/s12879-017-2365-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Farzaneh Sadat Tabataba
- Computer Science Department, Virginia Tech, 2202 Kraft Drive, Blacksburg/Virginia, 24060, USA. .,Network Dynamics and Simulation Science Laboratory (NDSSL), Biocomplexity Institute, Virginia Tech, 1015 Life Science Cir, Blacksburg/Virginia, 24061, USA.
| | - Prithwish Chakraborty
- Computer Science Department, Virginia Tech, 2202 Kraft Drive, Blacksburg/Virginia, 24060, USA
| | - Naren Ramakrishnan
- Computer Science Department, Virginia Tech, 2202 Kraft Drive, Blacksburg/Virginia, 24060, USA.,Network Dynamics and Simulation Science Laboratory (NDSSL), Biocomplexity Institute, Virginia Tech, 1015 Life Science Cir, Blacksburg/Virginia, 24061, USA
| | - Srinivasan Venkatramanan
- Network Dynamics and Simulation Science Laboratory (NDSSL), Biocomplexity Institute, Virginia Tech, 1015 Life Science Cir, Blacksburg/Virginia, 24061, USA
| | - Jiangzhuo Chen
- Network Dynamics and Simulation Science Laboratory (NDSSL), Biocomplexity Institute, Virginia Tech, 1015 Life Science Cir, Blacksburg/Virginia, 24061, USA
| | - Bryan Lewis
- Network Dynamics and Simulation Science Laboratory (NDSSL), Biocomplexity Institute, Virginia Tech, 1015 Life Science Cir, Blacksburg/Virginia, 24061, USA
| | - Madhav Marathe
- Computer Science Department, Virginia Tech, 2202 Kraft Drive, Blacksburg/Virginia, 24060, USA.,Network Dynamics and Simulation Science Laboratory (NDSSL), Biocomplexity Institute, Virginia Tech, 1015 Life Science Cir, Blacksburg/Virginia, 24061, USA
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Venkatramanan S, Lewis B, Chen J, Higdon D, Vullikanti A, Marathe M. Using data-driven agent-based models for forecasting emerging infectious diseases. Epidemics 2017; 22:43-49. [PMID: 28256420 DOI: 10.1016/j.epidem.2017.02.010] [Citation(s) in RCA: 65] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2016] [Revised: 01/30/2017] [Accepted: 02/17/2017] [Indexed: 11/19/2022] Open
Abstract
Producing timely, well-informed and reliable forecasts for an ongoing epidemic of an emerging infectious disease is a huge challenge. Epidemiologists and policy makers have to deal with poor data quality, limited understanding of the disease dynamics, rapidly changing social environment and the uncertainty on effects of various interventions in place. Under this setting, detailed computational models provide a comprehensive framework for integrating diverse data sources into a well-defined model of disease dynamics and social behavior, potentially leading to better understanding and actions. In this paper, we describe one such agent-based model framework developed for forecasting the 2014-2015 Ebola epidemic in Liberia, and subsequently used during the Ebola forecasting challenge. We describe the various components of the model, the calibration process and summarize the forecast performance across scenarios of the challenge. We conclude by highlighting how such a data-driven approach can be refined and adapted for future epidemics, and share the lessons learned over the course of the challenge.
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Affiliation(s)
- Srinivasan Venkatramanan
- Network Dynamics and Simulation Science Laboratory, Biocomplexity Institute of Virginia Tech, United States.
| | - Bryan Lewis
- Network Dynamics and Simulation Science Laboratory, Biocomplexity Institute of Virginia Tech, United States.
| | - Jiangzhuo Chen
- Network Dynamics and Simulation Science Laboratory, Biocomplexity Institute of Virginia Tech, United States.
| | - Dave Higdon
- Social and Decision Analytics Laboratory, Biocomplexity Institute of Virginia Tech, United States; Department of Statistics, Virginia Tech, United States.
| | - Anil Vullikanti
- Network Dynamics and Simulation Science Laboratory, Biocomplexity Institute of Virginia Tech, United States; Department of Computer Science, Virginia Tech, United States.
| | - Madhav Marathe
- Network Dynamics and Simulation Science Laboratory, Biocomplexity Institute of Virginia Tech, United States; Department of Computer Science, Virginia Tech, United States.
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Abstract
As increasingly large-scale multiagent simulations are being implemented, new methods are becoming necessary to make sense of the results of these simulations. Even concisely summarizing the results of a given simulation run is a challenge. Here we pose this as the problem of simulation summarization: how to extract the causally-relevant descriptions of the trajectories of the agents in the simulation. We present a simple algorithm to compress agent trajectories through state space by identifying the state transitions which are relevant to determining the distribution of outcomes at the end of the simulation. We present a toy-example to illustrate the working of the algorithm, and then apply it to a complex simulation of a major disaster in an urban area.
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Affiliation(s)
- Nidhi Parikh
- Network Dynamics and Simulation Science Lab, Biocomplexity Institute of Virginia Tech, Virginia Tech, Blacksburg, VA, USA
| | - Madhav Marathe
- Network Dynamics and Simulation Science Lab, Biocomplexity Institute of Virginia Tech, Virginia Tech, Blacksburg, VA, USA
| | - Samarth Swarup
- Network Dynamics and Simulation Science Lab, Biocomplexity Institute of Virginia Tech, Virginia Tech, Blacksburg, VA, USA
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Deodhar S, Bisset K, Chen J, Barrett C, Wilson M, Marathe M. EpiCaster: An Integrated Web Application For Situation Assessment and Forecasting of Global Epidemics. ACM BCB 2015; 2015:156-165. [PMID: 27796009 DOI: 10.1145/2808719.2808735] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Public health decision makers need access to high resolution situation assessment tools for understanding the extent of various epidemics in different regions of the world. In addition, they need insights into the future course of epidemics by way of forecasts. Such forecasts are essential for planning the allocation of limited resources and for implementing several policy-level and behavioral intervention strategies. The need for such forecasting systems became evident in the wake of the recent Ebola outbreak in West Africa. We have developed EpiCaster, an integrated Web application for situation assessment and forecasting of various epidemics, such as Flu and Ebola, that are prevalent in different regions of the world. Using EpiCaster, users can assess the magnitude and severity of different epidemics at highly resolved spatio-temporal levels. EpiCaster provides time-varying heat maps and graphical plots to view trends in the disease dynamics. EpiCaster also allows users to visualize data gathered through surveillance mechanisms, such as Google Flu Trends (GFT) and the World Health Organization (WHO). The forecasts provided by EpiCaster are generated using different epidemiological models, and the users can select the models through the interface to filter the corresponding forecasts. EpiCaster also allows the users to study epidemic propagation in the presence of a number of intervention strategies specific to certain diseases. Here we describe the modeling techniques, methodologies and computational infrastructure that EpiCaster relies on to support large-scale predictive analytics for situation assessment and forecasting of global epidemics.
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Affiliation(s)
- Suruchi Deodhar
- Network Dynamics and Simulation Science Laboratory, VBI, Virginia Tech, Blacksburg, VA
| | - Keith Bisset
- Network Dynamics and Simulation Science Laboratory, VBI, Virginia Tech, Blacksburg, VA
| | - Jiangzhuo Chen
- Network Dynamics and Simulation Science Laboratory, VBI, Virginia Tech, Blacksburg, VA
| | - Chris Barrett
- Network Dynamics and Simulation Science Laboratory, VBI, Virginia Tech, Blacksburg, VA
| | - Mandy Wilson
- Network Dynamics and Simulation Science Laboratory, VBI, Virginia Tech, Blacksburg, VA
| | - Madhav Marathe
- Network Dynamics and Simulation Science Laboratory, VBI, Virginia Tech, Blacksburg, VA
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Abstract
BACKGROUND An Ebola outbreak of unparalleled size is currently affecting several countries in West Africa, and international efforts to control the outbreak are underway. However, the efficacy of these interventions, and their likely impact on an Ebola epidemic of this size, is unknown. Forecasting and simulation of these interventions may inform public health efforts. METHODS We use existing data from Liberia and Sierra Leone to parameterize a mathematical model of Ebola and use this model to forecast the progression of the epidemic, as well as the efficacy of several interventions, including increased contact tracing, improved infection control practices, the use of a hypothetical pharmaceutical intervention to improve survival in hospitalized patients. FINDINGS Model forecasts until Dec. 31, 2014 show an increasingly severe epidemic with no sign of having reached a peak. Modeling results suggest that increased contact tracing, improved infection control, or a combination of the two can have a substantial impact on the number of Ebola cases, but these interventions are not sufficient to halt the progress of the epidemic. The hypothetical pharmaceutical intervention, while impacting mortality, had a smaller effect on the forecasted trajectory of the epidemic. INTERPRETATION Near-term, practical interventions to address the ongoing Ebola epidemic may have a beneficial impact on public health, but they will not result in the immediate halting, or even obvious slowing of the epidemic. A long-term commitment of resources and support will be necessary to address the outbreak.
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Affiliation(s)
- Caitlin M Rivers
- Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, USA
| | - Eric T Lofgren
- Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, USA
| | - Madhav Marathe
- Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, USA
| | - Stephen Eubank
- Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, USA
| | - Bryan L Lewis
- Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, USA
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50
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Abstract
Background: An Ebola outbreak of unparalleled size is currently affecting several countries in West Africa, and international efforts to control the outbreak are underway. However, the efficacy of these interventions, and their likely impact on an Ebola epidemic of this size, is unknown. Forecasting and simulation of these interventions may inform public health efforts.
Methods: We use existing data from Liberia and Sierra Leone to parameterize a mathematical model of Ebola and use this model to forecast the progression of the epidemic, as well as the efficacy of several interventions, including increased contact tracing, improved infection control practices, the use of a hypothetical pharmaceutical intervention to improve survival in hospitalized patients.
Findings: Model forecasts until Dec. 31, 2014 show an increasingly severe epidemic with no sign of having reached a peak. Modeling results suggest that increased contact tracing, improved infection control, or a combination of the two can have a substantial impact on the number of Ebola cases, but these interventions are not sufficient to halt the progress of the epidemic. The hypothetical pharmaceutical intervention, while impacting mortality, had a smaller effect on the forecasted trajectory of the epidemic.
Interpretation: Near-term, practical interventions to address the ongoing Ebola epidemic may have a beneficial impact on public health, but they will not result in the immediate halting, or even obvious slowing of the epidemic. A long-term commitment of resources and support will be necessary to address the outbreak.
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Affiliation(s)
- Caitlin M Rivers
- Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, USA
| | - Eric T Lofgren
- Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, USA
| | - Madhav Marathe
- Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, USA
| | - Stephen Eubank
- Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, USA
| | - Bryan L Lewis
- Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, USA
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