1
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Manuel DG, Saran G, Lee I, Yusuf W, Thomson M, Mercier É, Pileggi V, McKay RM, Corchis-Scott R, Geng Q, Servos M, Inert H, Dhiyebi H, Yang IM, Harvey B, Rodenburg E, Millar C, Delatolla R. Wastewater-based surveillance of SARS-CoV-2: Short-term projection (forecasting), smoothing and outlier identification using Bayesian smoothing. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 949:174937. [PMID: 39067598 DOI: 10.1016/j.scitotenv.2024.174937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 06/24/2024] [Accepted: 07/19/2024] [Indexed: 07/30/2024]
Abstract
BACKGROUND Day-to-day variation in the measurement of SARS-CoV-2 in wastewater can challenge public health interpretation. We assessed a Bayesian smoothing and forecasting method previously used for surveillance and short-term projection of COVID-19 cases, hospitalizations, and deaths. METHODS SARS-CoV-2 viral measurement from the sewershed in Ottawa, Canada, sampled at the municipal wastewater treatment plant from July 1, 2020, to February 15, 2022, was used to assess and internally validate measurement averaging and prediction. External validation was performed using viral measurement data from influent wastewater samples from 15 wastewater treatment plants and municipalities across Ontario. RESULTS Plots of SARS-CoV-2 viral measurement over time using Bayesian smoothing visually represented distinct COVID-19 "waves" described by case and hospitalization data in both initial (Ottawa) and external validation in 15 Ontario communities. The time-varying growth rate of viral measurement in wastewater samples approximated the growth rate observed for cases and hospitalization. One-week predicted viral measurement approximated the observed viral measurement throughout the assessment period from December 23, 2020, to August 8, 2022. An uncalibrated model showed underprediction during rapid increases in viral measurement (positive growth) and overprediction during rapid decreases. After recalibration, the model showed a close approximation between observed and predicted estimates. CONCLUSION Bayesian smoothing of wastewater surveillance data of SARS-CoV-2 allows for accurate estimates of COVID-19 growth rates and one- and two-week forecasting of SARS-CoV-2 in wastewater for 16 municipalities in Ontario, Canada. Further assessment is warranted in other communities representing different sewersheds and environmental conditions.
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Affiliation(s)
- Douglas G Manuel
- The Ottawa Hospital Research Institute, 1053 Carling Ave, Ottawa, Ontario K1Y 4E9, Canada; University of Ottawa, Ottawa, 75 Laurier Ave E, ON K1N 6N5, Canada.
| | - Gauri Saran
- The Ottawa Hospital Research Institute, 1053 Carling Ave, Ottawa, Ontario K1Y 4E9, Canada
| | - Ivan Lee
- The Ottawa Hospital Research Institute, 1053 Carling Ave, Ottawa, Ontario K1Y 4E9, Canada
| | - Warsame Yusuf
- Public Health Agency of Canada, 110 Stone Rd W, Guelph, Ontario N1G 3W4, Canada.
| | - Mathew Thomson
- The Ottawa Hospital Research Institute, 1053 Carling Ave, Ottawa, Ontario K1Y 4E9, Canada.
| | | | - Vince Pileggi
- Ontario Ministry of the Environment, Conservation, and Parks, 777 Bay St, Toronto, Ontario M7A 2J3, Canada.
| | - R Michael McKay
- University of Windsor, 401 Sunset Avenue, Windsor, Ontario N9B 3P4, Canada.
| | | | - Qiudi Geng
- University of Windsor, 401 Sunset Avenue, Windsor, Ontario N9B 3P4, Canada.
| | - Mark Servos
- University of Waterloo, 200 University Ave W, Waterloo, Ontario N2L 3G1, Canada.
| | - Heather Inert
- University of Waterloo, 200 University Ave W, Waterloo, Ontario N2L 3G1, Canada
| | - Hadi Dhiyebi
- University of Waterloo, 200 University Ave W, Waterloo, Ontario N2L 3G1, Canada.
| | - Ivy M Yang
- University of Toronto, Department of Chemical Engineering and Applied Chemistry, 200 College Street, Toronto, Ontario, M5S 3E5, Canada.
| | - Bart Harvey
- City of Hamilton Public Health Services, 100 Main St W, Hamilton, Ontario L8P 1H6, Canada.
| | - Erin Rodenburg
- City of Hamilton Public Health Services, 100 Main St W, Hamilton, Ontario L8P 1H6, Canada.
| | - Catherine Millar
- Ottawa Public Health, 100 Constellation Dr, Nepean, Ontario K2G 6J8, Canada.
| | - Robert Delatolla
- University of Ottawa, Ottawa, 75 Laurier Ave E, ON K1N 6N5, Canada.
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2
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Tsang TK, Du Q, Cowling BJ, Viboud C. An adaptive weight ensemble approach to forecast influenza activity in an irregular seasonality context. Nat Commun 2024; 15:8625. [PMID: 39366942 PMCID: PMC11452387 DOI: 10.1038/s41467-024-52504-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Accepted: 09/11/2024] [Indexed: 10/06/2024] Open
Abstract
Forecasting influenza activity in tropical and subtropical regions, such as Hong Kong, is challenging due to irregular seasonality and high variability. We develop a diverse set of statistical, machine learning, and deep learning approaches to forecast influenza activity in Hong Kong 0 to 8 weeks ahead, leveraging a unique multi-year surveillance record spanning 32 epidemics from 1998 to 2019. We consider a simple average ensemble (SAE) of the top two individual models, and develop an adaptive weight blending ensemble (AWBE) that dynamically updates model contribution. All models outperform the baseline constant incidence model, reducing the root mean square error (RMSE) by 23%-29% and weighted interval score (WIS) by 25%-31% for 8-week ahead forecasts. The SAE model performed similarly to individual models, while the AWBE model reduces RMSE by 52% and WIS by 53%, outperforming individual models for forecasts in different epidemic trends (growth, plateau, decline) and during both winter and summer seasons. Using the post-COVID data (2023-2024) as another test period, the AWBE model still reduces RMSE by 39% and WIS by 45%. Our framework contributes to comparing and benchmarking models in ensemble forecasts, enhancing evidence for synthesizing multiple models in disease forecasting for geographies with irregular influenza seasonality.
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Affiliation(s)
- Tim K Tsang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong.
| | - Qiurui Du
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong
| | - Cécile Viboud
- Fogarty International Center National Institutes of Health, Bethesda, MD, USA
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3
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Grant R, Rubin M, Abbas M, Pittet D, Srinivasan A, Jernigan JA, Bell M, Samore M, Harbarth S, Slayton RB. Expanding the use of mathematical modeling in healthcare epidemiology and infection prevention and control. Infect Control Hosp Epidemiol 2024:1-6. [PMID: 39228083 DOI: 10.1017/ice.2024.97] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
During the coronavirus disease 2019 pandemic, mathematical modeling has been widely used to understand epidemiological burden, trends, and transmission dynamics, to facilitate policy decisions, and, to a lesser extent, to evaluate infection prevention and control (IPC) measures. This review highlights the added value of using conventional epidemiology and modeling approaches to address the complexity of healthcare-associated infections (HAI) and antimicrobial resistance. It demonstrates how epidemiological surveillance data and modeling can be used to infer transmission dynamics in healthcare settings and to forecast healthcare impact, how modeling can be used to improve the validity of interpretation of epidemiological surveillance data, how modeling can be used to estimate the impact of IPC interventions, and how modeling can be used to guide IPC and antimicrobial treatment and stewardship decision-making. There are several priority areas for expanding the use of modeling in healthcare epidemiology and IPC. Importantly, modeling should be viewed as complementary to conventional healthcare epidemiological approaches, and this requires collaboration and active coordination between IPC, healthcare epidemiology, and mathematical modeling groups.
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Affiliation(s)
- Rebecca Grant
- Infection Control Programme and WHO Collaborating Centre for Infection Prevention and Control and Antimicrobial Resistance, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Michael Rubin
- Division of Epidemiology, University of Utah School Medicine, Salt Lake City, UT, USA
| | - Mohamed Abbas
- Infection Control Programme and WHO Collaborating Centre for Infection Prevention and Control and Antimicrobial Resistance, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Didier Pittet
- Infection Control Programme and WHO Collaborating Centre for Infection Prevention and Control and Antimicrobial Resistance, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Arjun Srinivasan
- Division of Healthcare Quality Promotion, U.S. Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - John A Jernigan
- Division of Healthcare Quality Promotion, U.S. Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Michael Bell
- Division of Healthcare Quality Promotion, U.S. Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Matthew Samore
- Division of Epidemiology, University of Utah School Medicine, Salt Lake City, UT, USA
| | - Stephan Harbarth
- Infection Control Programme and WHO Collaborating Centre for Infection Prevention and Control and Antimicrobial Resistance, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Rachel B Slayton
- Division of Healthcare Quality Promotion, U.S. Centers for Disease Control and Prevention, Atlanta, GA, USA
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4
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Loo SL, Chinazzi M, Srivastava A, Venkatramanan S, Truelove S, Viboud C. Preface: COVID-19 Scenario Modeling Hubs. Epidemics 2024; 48:100788. [PMID: 39209676 DOI: 10.1016/j.epidem.2024.100788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2024] [Revised: 08/21/2024] [Accepted: 08/21/2024] [Indexed: 09/04/2024] Open
Affiliation(s)
- Sara L Loo
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; International Vaccine Access Center, Baltimore, MD, USA
| | - Matteo Chinazzi
- The Roux Institute Northeastern University, Portland, MA, USA; Laboratory for the Modeling of Biological and Socio-technical Systems Network Science Institute Northeastern University, Boston, MA, USA
| | | | | | - Shaun Truelove
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; International Vaccine Access Center, Baltimore, MD, USA
| | - Cecile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
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5
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Fox SJ, Kim M, Meyers LA, Reich NG, Ray EL. Optimizing Disease Outbreak Forecast Ensembles. Emerg Infect Dis 2024; 30:1967-1969. [PMID: 39174027 PMCID: PMC11347000 DOI: 10.3201/eid3009.240026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/24/2024] Open
Abstract
On the basis of historical influenza and COVID-19 forecasts, we found that more than 3 forecast models are needed to ensure robust ensemble accuracy. Additional models can improve ensemble performance, but with diminishing accuracy returns. This understanding will assist with the design of current and future collaborative infectious disease forecasting efforts.
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6
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Cori A, Kucharski A. Inference of epidemic dynamics in the COVID-19 era and beyond. Epidemics 2024; 48:100784. [PMID: 39167954 DOI: 10.1016/j.epidem.2024.100784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 06/25/2024] [Accepted: 07/11/2024] [Indexed: 08/23/2024] Open
Abstract
The COVID-19 pandemic demonstrated the key role that epidemiology and modelling play in analysing infectious threats and supporting decision making in real-time. Motivated by the unprecedented volume and breadth of data generated during the pandemic, we review modern opportunities for analysis to address questions that emerge during a major modern epidemic. Following the broad chronology of insights required - from understanding initial dynamics to retrospective evaluation of interventions, we describe the theoretical foundations of each approach and the underlying intuition. Through a series of case studies, we illustrate real life applications, and discuss implications for future work.
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Affiliation(s)
- Anne Cori
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, United Kingdom.
| | - Adam Kucharski
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, United Kingdom.
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7
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Gerlee P, Thoreén H, Joöud AS, Lundh T, Spreco A, Nordlund A, Brezicka T, Britton T, Kjellberg M, Kaöllberg H, Tegnell A, Brouwers L, Timpka T. Evaluation and communication of pandemic scenarios. Lancet Digit Health 2024; 6:e543-e544. [PMID: 39059885 DOI: 10.1016/s2589-7500(24)00144-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 04/15/2024] [Accepted: 06/25/2024] [Indexed: 07/28/2024]
Affiliation(s)
- Philip Gerlee
- Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, 41296 Gothenburg, Sweden.
| | | | - Anna Saxne Joöud
- Department of Laboratory Medicine, Faculty of Medicine, Lund University, Lund, Sweden; Department of Clinical Sciences Lund, Lund University, Lund, Sweden; Department of Research and Development, Skaåne University Hospital, Lund, Sweden
| | - Torbjoörn Lundh
- Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, 41296 Gothenburg, Sweden
| | - Armin Spreco
- Department of Health, Medicine and Caring Sciences, Linkoöping University, Linkoöping, Sweden; Regional Management Office, Region Oöstergoötland, Linkoöping, Sweden
| | | | | | - Tom Britton
- Department of Mathematics, Stockholm University, Stockholm, Sweden
| | | | | | | | | | - Toomas Timpka
- Department of Health, Medicine and Caring Sciences, Linkoöping University, Linkoöping, Sweden; Regional Management Office, Region Oöstergoötland, Linkoöping, Sweden
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8
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Mathis SM, Webber AE, León TM, Murray EL, Sun M, White LA, Brooks LC, Green A, Hu AJ, Rosenfeld R, Shemetov D, Tibshirani RJ, McDonald DJ, Kandula S, Pei S, Yaari R, Yamana TK, Shaman J, Agarwal P, Balusu S, Gururajan G, Kamarthi H, Prakash BA, Raman R, Zhao Z, Rodríguez A, Meiyappan A, Omar S, Baccam P, Gurung HL, Suchoski BT, Stage SA, Ajelli M, Kummer AG, Litvinova M, Ventura PC, Wadsworth S, Niemi J, Carcelen E, Hill AL, Loo SL, McKee CD, Sato K, Smith C, Truelove S, Jung SM, Lemaitre JC, Lessler J, McAndrew T, Ye W, Bosse N, Hlavacek WS, Lin YT, Mallela A, Gibson GC, 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, Pastore Y Piontti A, 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, Suez E, Cojocaru MG, Thommes EW, Cramer EY, Gerding A, Stark A, Ray EL, Reich NG, Shandross L, Wattanachit N, Wang Y, Zorn MW, Aawar MA, Srivastava A, Meyers LA, Adiga A, Hurt B, Kaur G, Lewis BL, Marathe M, Venkatramanan S, Butler P, Farabow A, Ramakrishnan N, Muralidhar N, Reed C, Biggerstaff M, Borchering RK. Title evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations. Nat Commun 2024; 15:6289. [PMID: 39060259 PMCID: PMC11282251 DOI: 10.1038/s41467-024-50601-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 07/16/2024] [Indexed: 07/28/2024] Open
Abstract
Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021-22 and 2022-23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one-to-four weeks ahead. Forecast skill is evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperform 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 is 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 degrade over longer forecast horizons. In this work we demonstrate that while the FluSight ensemble was a robust predictor, even ensembles face challenges during periods of rapid change.
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Affiliation(s)
| | | | - Tomás M León
- California Department of Public Health, Richmond, CA, USA
| | - Erin L Murray
- California Department of Public Health, Richmond, CA, USA
| | - Monica Sun
- California Department of Public Health, Richmond, CA, USA
| | - Lauren A White
- California Department of Public Health, Richmond, CA, USA
| | - Logan C Brooks
- Carnegie Mellon University, Pittsburgh, PA, USA
- University of California, Berkeley, Berkeley, CA, USA
| | - Alden Green
- Carnegie Mellon University, Pittsburgh, PA, USA
| | | | | | | | - Ryan J Tibshirani
- Carnegie Mellon University, Pittsburgh, PA, USA
- University of California, Berkeley, Berkeley, CA, USA
| | | | | | - Sen Pei
- Columbia University, New York, NY, USA
| | | | | | - Jeffrey Shaman
- Columbia University, New York, NY, USA
- Columbia University School of Climate, New York, NY, USA
| | | | | | | | | | | | - Rishi Raman
- Georgia Institute of Technology, Atlanta, GA, USA
| | - Zhiyuan Zhao
- Georgia Institute of Technology, Atlanta, GA, USA
| | | | | | - Shalina Omar
- Guidehouse Advisory and Consulting Services, McClean, VA, USA
| | | | | | | | | | - Marco Ajelli
- Indiana University School of Public Health, Bloomington, IN, USA
| | | | - Maria Litvinova
- Indiana University School of Public Health, Bloomington, IN, USA
| | - Paulo C Ventura
- Indiana University School of Public Health, Bloomington, IN, USA
| | | | | | | | | | - Sara L Loo
- Johns Hopkins University, Baltimore, MD, USA
| | | | - Koji Sato
- Johns Hopkins University, Baltimore, MD, USA
| | | | | | - Sung-Mok Jung
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Justin Lessler
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | | | - Nikos Bosse
- London School of Health and Tropical Medicine, London, UK
| | | | - Yen Ting Lin
- Los Alamos National Laboratory, Los Alamos, NM, USA
| | | | | | - Ye Chen
- Northern Arizona University, Flagstaff, AZ, USA
| | | | | | | | - Amanda C Perofsky
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | | | - Fred Lu
- Northeastern University, Boston, MA, USA
| | | | | | | | | | - Kunpeng Mu
- Northeastern University, Boston, MA, USA
| | | | | | | | | | - Pete Riley
- Predictive Science Inc, San Diego, CA, USA
| | | | | | | | - V P Nagraj
- Signature Science, LLC, Charlottesville, VA, USA
| | | | | | | | | | | | | | | | - Edward W Thommes
- University of Guelph, Guelph, ON, Canada
- Sanofi, Toronto, ON, USA
| | | | - Aaron Gerding
- University of Massachusetts Amherst, Amherst, MA, USA
| | - Ariane Stark
- University of Massachusetts Amherst, Amherst, MA, USA
| | - Evan L Ray
- University of Massachusetts Amherst, Amherst, MA, USA
| | | | - Li Shandross
- University of Massachusetts Amherst, Amherst, MA, USA
| | | | - Yijin Wang
- University of Massachusetts Amherst, Amherst, MA, USA
| | - Martha W Zorn
- University of Massachusetts Amherst, Amherst, MA, USA
| | - Majd Al Aawar
- University of Southern California, Los Angeles, CA, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | - Carrie Reed
- Centers for Disease Control and Prevention, Atlanta, GA, USA
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9
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Yu R, Wang R. Learning dynamical systems from data: An introduction to physics-guided deep learning. Proc Natl Acad Sci U S A 2024; 121:e2311808121. [PMID: 38913886 PMCID: PMC11228478 DOI: 10.1073/pnas.2311808121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/26/2024] Open
Abstract
Modeling complex physical dynamics is a fundamental task in science and engineering. Traditional physics-based models are first-principled, explainable, and sample-efficient. However, they often rely on strong modeling assumptions and expensive numerical integration, requiring significant computational resources and domain expertise. While deep learning (DL) provides efficient alternatives for modeling complex dynamics, they require a large amount of labeled training data. Furthermore, its predictions may disobey the governing physical laws and are difficult to interpret. Physics-guided DL aims to integrate first-principled physical knowledge into data-driven methods. It has the best of both worlds and is well equipped to better solve scientific problems. Recently, this field has gained great progress and has drawn considerable interest across discipline Here, we introduce the framework of physics-guided DL with a special emphasis on learning dynamical systems. We describe the learning pipeline and categorize state-of-the-art methods under this framework. We also offer our perspectives on the open challenges and emerging opportunities.
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Affiliation(s)
- Rose Yu
- Department of Computer Science and Engineering, University of California, San Diego, CA 92093
| | - Rui Wang
- Department of Computer Science and Engineering, University of California, San Diego, CA 92093
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139
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10
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Bleichrodt A, Luo R, Kirpich A, Chowell G. Evaluating the forecasting performance of ensemble sub-epidemic frameworks and other time series models for the 2022-2023 mpox epidemic. ROYAL SOCIETY OPEN SCIENCE 2024; 11:240248. [PMID: 39076375 PMCID: PMC11285753 DOI: 10.1098/rsos.240248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 06/06/2024] [Accepted: 06/07/2024] [Indexed: 07/31/2024]
Abstract
During the 2022-2023 unprecedented mpox epidemic, near real-time short-term forecasts of the epidemic's trajectory were essential in intervention implementation and guiding policy. However, as case levels have significantly decreased, evaluating model performance is vital to advancing the field of epidemic forecasting. Using laboratory-confirmed mpox case data from the Centers for Disease Control and Prevention and Our World in Data teams, we generated retrospective sequential weekly forecasts for Brazil, Canada, France, Germany, Spain, the United Kingdom, the United States and at the global scale using an auto-regressive integrated moving average (ARIMA) model, generalized additive model, simple linear regression, Facebook's Prophet model, as well as the sub-epidemic wave and n-sub-epidemic modelling frameworks. We assessed forecast performance using average mean squared error, mean absolute error, weighted interval scores, 95% prediction interval coverage, skill scores and Winkler scores. Overall, the n-sub-epidemic modelling framework outcompeted other models across most locations and forecasting horizons, with the unweighted ensemble model performing best most frequently. The n-sub-epidemic and spatial-wave frameworks considerably improved in average forecasting performance relative to the ARIMA model (greater than 10%) for all performance metrics. Findings further support sub-epidemic frameworks for short-term forecasting epidemics of emerging and re-emerging infectious diseases.
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Affiliation(s)
- Amanda Bleichrodt
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Ruiyan Luo
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Alexander Kirpich
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Gerardo Chowell
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
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11
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Shandross L, Howerton E, Contamin L, Hochheiser H, Krystalli A, Reich NG, Ray EL. hubEnsembles: Ensembling Methods in R. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.24.24309416. [PMID: 38978658 PMCID: PMC11230315 DOI: 10.1101/2024.06.24.24309416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Combining predictions from multiple models into an ensemble is a widely used practice across many fields with demonstrated performance benefits. The R package hubEnsembles provides a flexible framework for ensembling various types of predictions, including point estimates and probabilistic predictions. A range of common methods for generating ensembles are supported, including weighted averages, quantile averages, and linear pools. The hubEnsembles package fits within a broader framework of open-source software and data tools called the "hubverse", which facilitates the development and management of collaborative modelling exercises.
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12
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Sudhakar T, Bhansali A, Walkington J, Puelz D. The disutility of compartmental model forecasts during the COVID-19 pandemic. FRONTIERS IN EPIDEMIOLOGY 2024; 4:1389617. [PMID: 38966521 PMCID: PMC11222405 DOI: 10.3389/fepid.2024.1389617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 05/27/2024] [Indexed: 07/06/2024]
Abstract
During the COVID-19 pandemic, several forecasting models were released to predict the spread of the virus along variables vital for public health policymaking. Of these, the susceptible-infected-recovered (SIR) compartmental model was the most common. In this paper, we investigated the forecasting performance of The University of Texas COVID-19 Modeling Consortium SIR model. We considered the following daily outcomes: hospitalizations, ICU patients, and deaths. We evaluated the overall forecasting performance, highlighted some stark forecast biases, and considered forecast errors conditional on different pandemic regimes. We found that this model tends to overforecast over the longer horizons and when there is a surge in viral spread. We bolstered these findings by linking them to faults with the SIR framework itself.
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Affiliation(s)
| | | | | | - David Puelz
- Salem Center for Policy, Department of Finance, & Department of Information, Risk, and Operations Management, Austin, TX, United States
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13
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Chowell G, Tariq A, Dahal S, Bleichrodt A, Luo R, Hyman JM. SpatialWavePredict: a tutorial-based primer and toolbox for forecasting growth trajectories using the ensemble spatial wave sub-epidemic modeling framework. BMC Med Res Methodol 2024; 24:131. [PMID: 38849766 PMCID: PMC11157887 DOI: 10.1186/s12874-024-02241-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 05/06/2024] [Indexed: 06/09/2024] Open
Abstract
BACKGROUND Dynamical mathematical models defined by a system of differential equations are typically not easily accessible to non-experts. However, forecasts based on these types of models can help gain insights into the mechanisms driving the process and may outcompete simpler phenomenological growth models. Here we introduce a friendly toolbox, SpatialWavePredict, to characterize and forecast the spatial wave sub-epidemic model, which captures diverse wave dynamics by aggregating multiple asynchronous growth processes and has outperformed simpler phenomenological growth models in short-term forecasts of various infectious diseases outbreaks including SARS, Ebola, and the early waves of the COVID-19 pandemic in the US. RESULTS This tutorial-based primer introduces and illustrates a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using an ensemble spatial wave sub-epidemic model based on ordinary differential equations. Scientists, policymakers, and students can use the toolbox to conduct real-time short-term forecasts. The five-parameter epidemic wave model in the toolbox aggregates linked overlapping sub-epidemics and captures a rich spectrum of epidemic wave dynamics, including oscillatory wave behavior and plateaus. An ensemble strategy aims to improve forecasting performance by combining the resulting top-ranked models. The toolbox provides a tutorial for forecasting time-series trajectories, including the full uncertainty distribution derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score. CONCLUSIONS We have developed the first comprehensive toolbox to characterize and forecast time-series data using an ensemble spatial wave sub-epidemic wave model. As an epidemic situation or contagion occurs, the tools presented in this tutorial can facilitate policymakers to guide the implementation of containment strategies and assess the impact of control interventions. We demonstrate the functionality of the toolbox with examples, including a tutorial video, and is illustrated using daily data on the COVID-19 pandemic in the USA.
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Affiliation(s)
- Gerardo Chowell
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA.
- Department of Applied Mathematics, Kyung Hee University, Yongin, 17104, Korea.
| | - Amna Tariq
- Department of Pediatrics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Sushma Dahal
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Amanda Bleichrodt
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Ruiyan Luo
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - James M Hyman
- Department of Mathematics, Tulane University, New Orleans, LA, USA
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14
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Runge MC, Shea K, Howerton E, Yan K, Hochheiser H, Rosenstrom E, Probert WJM, Borchering R, Marathe MV, Lewis B, Venkatramanan S, Truelove S, Lessler J, Viboud C. Scenario design for infectious disease projections: Integrating concepts from decision analysis and experimental design. Epidemics 2024; 47:100775. [PMID: 38838462 DOI: 10.1016/j.epidem.2024.100775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 04/04/2024] [Accepted: 05/20/2024] [Indexed: 06/07/2024] Open
Abstract
Across many fields, scenario modeling has become an important tool for exploring long-term projections and how they might depend on potential interventions and critical uncertainties, with relevance to both decision makers and scientists. In the past decade, and especially during the COVID-19 pandemic, the field of epidemiology has seen substantial growth in the use of scenario projections. Multiple scenarios are often projected at the same time, allowing important comparisons that can guide the choice of intervention, the prioritization of research topics, or public communication. The design of the scenarios is central to their ability to inform important questions. In this paper, we draw on the fields of decision analysis and statistical design of experiments to propose a framework for scenario design in epidemiology, with relevance also to other fields. We identify six different fundamental purposes for scenario designs (decision making, sensitivity analysis, situational awareness, horizon scanning, forecasting, and value of information) and discuss how those purposes guide the structure of scenarios. We discuss other aspects of the content and process of scenario design, broadly for all settings and specifically for multi-model ensemble projections. As an illustrative case study, we examine the first 17 rounds of scenarios from the U.S. COVID-19 Scenario Modeling Hub, then reflect on future advancements that could improve the design of scenarios in epidemiological settings.
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Affiliation(s)
- Michael C Runge
- U.S. Geological Survey, Eastern Ecological Science Center at the Patuxent Research Refuge, Laurel, MD, USA.
| | - Katriona Shea
- The Pennsylvania State University, University Park, PA, USA
| | - Emily Howerton
- The Pennsylvania State University, University Park, PA, USA
| | - Katie Yan
- The Pennsylvania State University, University Park, PA, USA
| | | | | | | | | | | | - Bryan Lewis
- University of Virginia, Charlottesville, VA, USA
| | | | | | - Justin Lessler
- The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
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15
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Sherratt K, Srivastava A, Ainslie K, Singh DE, Cublier A, Marinescu MC, Carretero J, Garcia AC, Franco N, Willem L, Abrams S, Faes C, Beutels P, Hens N, Müller S, Charlton B, Ewert R, Paltra S, Rakow C, Rehmann J, Conrad T, Schütte C, Nagel K, Abbott S, Grah R, Niehus R, Prasse B, Sandmann F, Funk S. Characterising information gains and losses when collecting multiple epidemic model outputs. Epidemics 2024; 47:100765. [PMID: 38643546 PMCID: PMC11196924 DOI: 10.1016/j.epidem.2024.100765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 01/25/2024] [Accepted: 03/26/2024] [Indexed: 04/23/2024] Open
Abstract
BACKGROUND Collaborative comparisons and combinations of epidemic models are used as policy-relevant evidence during epidemic outbreaks. In the process of collecting multiple model projections, such collaborations may gain or lose relevant information. Typically, modellers contribute a probabilistic summary at each time-step. We compared this to directly collecting simulated trajectories. We aimed to explore information on key epidemic quantities; ensemble uncertainty; and performance against data, investigating potential to continuously gain information from a single cross-sectional collection of model results. METHODS We compared projections from the European COVID-19 Scenario Modelling Hub. Five teams modelled incidence in Belgium, the Netherlands, and Spain. We compared July 2022 projections by incidence, peaks, and cumulative totals. We created a probabilistic ensemble drawn from all trajectories, and compared to ensembles from a median across each model's quantiles, or a linear opinion pool. We measured the predictive accuracy of individual trajectories against observations, using this in a weighted ensemble. We repeated this sequentially against increasing weeks of observed data. We evaluated these ensembles to reflect performance with varying observed data. RESULTS By collecting modelled trajectories, we showed policy-relevant epidemic characteristics. Trajectories contained a right-skewed distribution well represented by an ensemble of trajectories or a linear opinion pool, but not models' quantile intervals. Ensembles weighted by performance typically retained the range of plausible incidence over time, and in some cases narrowed this by excluding some epidemic shapes. CONCLUSIONS We observed several information gains from collecting modelled trajectories rather than quantile distributions, including potential for continuously updated information from a single model collection. The value of information gains and losses may vary with each collaborative effort's aims, depending on the needs of projection users. Understanding the differing information potential of methods to collect model projections can support the accuracy, sustainability, and communication of collaborative infectious disease modelling efforts.
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Affiliation(s)
| | | | - Kylie Ainslie
- Dutch National Institute of Public Health and the Environment (RIVM), Bilthoven, Netherlands; School of Public Health, University of Hong Kong, Hong Kong Special Administrative Region
| | | | | | | | | | | | | | | | - Steven Abrams
- University of Antwerp, Antwerp, Belgium; UHasselt, Hasselt, Belgium
| | | | | | - Niel Hens
- University of Antwerp, Antwerp, Belgium; UHasselt, Hasselt, Belgium
| | | | | | | | | | | | | | - Tim Conrad
- Zuse Institute Berlin (ZIB), Berlin, Germany
| | | | - Kai Nagel
- Technische Universität Berlin, Berlin, Germany
| | - Sam Abbott
- London School of Hygiene & Tropical Medicine, London, UK
| | | | | | | | | | - Sebastian Funk
- London School of Hygiene & Tropical Medicine, London, UK
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16
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Chowell G, Dahal S, Bleichrodt A, Tariq A, Hyman JM, Luo R. SubEpiPredict: A tutorial-based primer and toolbox for fitting and forecasting growth trajectories using the ensemble n-sub-epidemic modeling framework. Infect Dis Model 2024; 9:411-436. [PMID: 38385022 PMCID: PMC10879680 DOI: 10.1016/j.idm.2024.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 02/01/2024] [Accepted: 02/01/2024] [Indexed: 02/23/2024] Open
Abstract
An ensemble n-sub-epidemic modeling framework that integrates sub-epidemics to capture complex temporal dynamics has demonstrated powerful forecasting capability in previous works. This modeling framework can characterize complex epidemic patterns, including plateaus, epidemic resurgences, and epidemic waves characterized by multiple peaks of different sizes. In this tutorial paper, we introduce and illustrate SubEpiPredict, a user-friendly MATLAB toolbox for fitting and forecasting time series data using an ensemble n-sub-epidemic modeling framework. The toolbox can be used for model fitting, forecasting, and evaluation of model performance of the calibration and forecasting periods using metrics such as the weighted interval score (WIS). We also provide a detailed description of these methods including the concept of the n-sub-epidemic model, constructing ensemble forecasts from the top-ranking models, etc. For the illustration of the toolbox, we utilize publicly available daily COVID-19 death data at the national level for the United States. The MATLAB toolbox introduced in this paper can be very useful for a wider group of audiences, including policymakers, and can be easily utilized by those without extensive coding and modeling backgrounds.
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Affiliation(s)
- Gerardo Chowell
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
- Department of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Sushma Dahal
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Amanda Bleichrodt
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Amna Tariq
- Department of Pediatrics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - James M. Hyman
- Department of Mathematics, Center for Computational Science, Tulane University, New Orleans, LA, USA
| | - Ruiyan Luo
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
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17
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Wade-Malone LK, Howerton E, Probert WJM, Runge MC, Viboud C, Shea K. When do we need multiple infectious disease models? Agreement between projection rank and magnitude in a multi-model setting. Epidemics 2024; 47:100767. [PMID: 38714099 DOI: 10.1016/j.epidem.2024.100767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 03/27/2024] [Accepted: 04/08/2024] [Indexed: 05/09/2024] Open
Abstract
Mathematical models are useful for public health planning and response to infectious disease threats. However, different models can provide differing results, which can hamper decision making if not synthesized appropriately. To address this challenge, multi-model hubs convene independent modeling groups to generate ensembles, known to provide more accurate predictions of future outcomes. Yet, these hubs are resource intensive, and how many models are sufficient in a hub is not known. Here, we compare the benefit of predictions from multiple models in different contexts: (1) decision settings that depend on predictions of quantitative outcomes (e.g., hospital capacity planning), where assessments of the benefits of multi-model ensembles have largely focused; and (2) decisions settings that require the ranking of alternative epidemic scenarios (e.g., comparing outcomes under multiple possible interventions and biological uncertainties). We develop a mathematical framework to mimic a multi-model prediction setting, and use this framework to quantify how frequently predictions from different models agree. We further explore multi-model agreement using real-world, empirical data from 14 rounds of U.S. COVID-19 Scenario Modeling Hub projections. Our results suggest that the value of multiple models could be different in different decision contexts, and if only a few models are available, focusing on the rank of alternative epidemic scenarios could be more robust than focusing on quantitative outcomes. Although additional exploration of the sufficient number of models for different contexts is still needed, our results indicate that it may be possible to identify decision contexts where it is robust to rely on fewer models, a finding that can inform the use of modeling resources during future public health crises.
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Affiliation(s)
- La Keisha Wade-Malone
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
| | - Emily Howerton
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA.
| | | | - Michael C Runge
- US Geological Survey, Eastern Ecological Science Center at the Patuxent Research Refuge, Laurel, MD, USA
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Katriona Shea
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
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18
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McAndrew T, Gibson GC, Braun D, Srivastava A, Brown K. Chimeric Forecasting: An experiment to leverage human judgment to improve forecasts of infectious disease using simulated surveillance data. Epidemics 2024; 47:100756. [PMID: 38452456 DOI: 10.1016/j.epidem.2024.100756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 12/06/2023] [Accepted: 02/26/2024] [Indexed: 03/09/2024] Open
Abstract
Forecasts of infectious agents provide public health officials advanced warning about the intensity and timing of the spread of disease. Past work has found that accuracy and calibration of forecasts is weakest when attempting to predict an epidemic peak. Forecasts from a mechanistic model would be improved if there existed accurate information about the timing and intensity of an epidemic. We presented 3000 humans with simulated surveillance data about the number of incident hospitalizations from a current and two past seasons, and asked that they predict the peak time and intensity of the underlying epidemic. We found that in comparison to two control models, a model including human judgment produced more accurate forecasts of peak time and intensity of hospitalizations during an epidemic. Chimeric models have the potential to improve our ability to predict targets of public health interest which may in turn reduce infectious disease burden.
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Affiliation(s)
- Thomas McAndrew
- Department of Community and Population Health, College of Health, Lehigh University, Bethlehem PA, United States of America.
| | - Graham C Gibson
- Statistical Sciences, Los Alamos National Laboratory, Los Alamos, NM, United States of America
| | - David Braun
- Department of Psychology College of Arts and Science, Lehigh University, Bethlehem PA, United States of America
| | - Abhishek Srivastava
- P.C. Rossin College of Engineering & Applied Science, Lehigh University, Bethlehem PA, United States of America
| | - Kate Brown
- Department of Community and Population Health, College of Health, Lehigh University, Bethlehem PA, United States of America
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19
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Moore S, Cavany S, Perkins TA, España GFC. Projecting the future impact of emerging SARS-CoV-2 variants under uncertainty: Modeling the initial Omicron outbreak. Epidemics 2024; 47:100759. [PMID: 38452455 DOI: 10.1016/j.epidem.2024.100759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 01/26/2024] [Accepted: 03/01/2024] [Indexed: 03/09/2024] Open
Abstract
Over the past several years, the emergence of novel SARS-CoV-2 variants has led to multiple waves of increased COVID-19 incidence. When the Omicron variant emerged, there was considerable concern about its potential impact in the winter of 2021-2022 due to its increased fitness. However, there was also considerable uncertainty regarding its likely impact due to questions about its relative transmissibility, severity, and degree of immune escape. We sought to evaluate the ability of an agent-based model to forecast incidence in the context of this emerging pathogen variant. To project COVID-19 cases and deaths in Indiana, we calibrated our model to COVID-19 hospitalizations, deaths, and test-positivity rates through November 2021, and then projected COVID-19 incidence through April 2022 under four different scenarios that covered the plausible ranges of Omicron's severity, transmissibility, and degree of immune escape. Our initial projections from December 2021 through March 2022 indicated that under a pessimistic scenario with high disease severity, the peak in weekly COVID-19 deaths in Indiana would be larger than the previous peak in December 2020. However, retrospective analyses indicate that Omicron's severity was closer to the optimistic scenario, and even though cases and hospitalizations reached a new peak, fewer deaths occurred than during the previous peak. According to our results, Omicron's rapid spread was consistent with a combination of higher transmissibility and immune escape relative to earlier variants. Our updated projections starting in January 2022 accurately predicted that cases would peak in mid-January and decline rapidly over the next several months. The performance of our projections shows that following the emergence of a new pathogen variant, models can help quantify the potential range of outbreak magnitudes and trajectories. Agent-based models are particularly useful in these scenarios because they can efficiently track individual vaccination and infection histories with multiple variants with varying degrees of cross-protection.
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Affiliation(s)
- Sean Moore
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, United States.
| | - Sean Cavany
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, United States
| | - T Alex Perkins
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Guido Felipe Camargo España
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, United States
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20
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Lemaitre JC, Loo SL, Kaminsky J, Lee EC, McKee C, Smith C, Jung SM, Sato K, Carcelen E, Hill A, Lessler J, Truelove S. flepiMoP: The evolution of a flexible infectious disease modeling pipeline during the COVID-19 pandemic. Epidemics 2024; 47:100753. [PMID: 38492544 DOI: 10.1016/j.epidem.2024.100753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 01/13/2024] [Accepted: 02/23/2024] [Indexed: 03/18/2024] Open
Abstract
The COVID-19 pandemic led to an unprecedented demand for projections of disease burden and healthcare utilization under scenarios ranging from unmitigated spread to strict social distancing policies. In response, members of the Johns Hopkins Infectious Disease Dynamics Group developed flepiMoP (formerly called the COVID Scenario Modeling Pipeline), a comprehensive open-source software pipeline designed for creating and simulating compartmental models of infectious disease transmission and inferring parameters through these models. The framework has been used extensively to produce short-term forecasts and longer-term scenario projections of COVID-19 at the state and county level in the US, for COVID-19 in other countries at various geographic scales, and more recently for seasonal influenza. In this paper, we highlight how the flepiMoP has evolved throughout the COVID-19 pandemic to address changing epidemiological dynamics, new interventions, and shifts in policy-relevant model outputs. As the framework has reached a mature state, we provide a detailed overview of flepiMoP's key features and remaining limitations, thereby distributing flepiMoP and its documentation as a flexible and powerful tool for researchers and public health professionals to rapidly build and deploy large-scale complex infectious disease models for any pathogen and demographic setup.
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Affiliation(s)
- Joseph C Lemaitre
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Sara L Loo
- Johns Hopkins University International Vaccine Access Center, Department of International Health, Baltimore, MD, USA
| | - Joshua Kaminsky
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Elizabeth C Lee
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Clifton McKee
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Claire Smith
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Sung-Mok Jung
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Koji Sato
- Johns Hopkins University International Vaccine Access Center, Department of International Health, Baltimore, MD, USA
| | - Erica Carcelen
- Johns Hopkins University International Vaccine Access Center, Department of International Health, Baltimore, MD, USA
| | - Alison Hill
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Justin Lessler
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Shaun Truelove
- Johns Hopkins University International Vaccine Access Center, Department of International Health, Baltimore, MD, USA; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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21
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Ma L, Qiu Z, Van Mieghem P, Kitsak M. Reporting delays: A widely neglected impact factor in COVID-19 forecasts. PNAS NEXUS 2024; 3:pgae204. [PMID: 38846778 PMCID: PMC11156234 DOI: 10.1093/pnasnexus/pgae204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 05/13/2024] [Indexed: 06/09/2024]
Abstract
Epidemic forecasts are only as good as the accuracy of epidemic measurements. Is epidemic data, particularly COVID-19 epidemic data, clean, and devoid of noise? The complexity and variability inherent in data collection and reporting suggest otherwise. While we cannot evaluate the integrity of the COVID-19 epidemic data in a holistic fashion, we can assess the data for the presence of reporting delays. In our work, through the analysis of the first COVID-19 wave, we find substantial reporting delays in the published epidemic data. Motivated by the desire to enhance epidemic forecasts, we develop a statistical framework to detect, uncover, and remove reporting delays in the infectious, recovered, and deceased epidemic time series. Using our framework, we expose and analyze reporting delays in eight regions significantly affected by the first COVID-19 wave. Further, we demonstrate that removing reporting delays from epidemic data by using our statistical framework may decrease the error in epidemic forecasts. While our statistical framework can be used in combination with any epidemic forecast method that intakes infectious, recovered, and deceased data, to make a basic assessment, we employed the classical SIRD epidemic model. Our results indicate that the removal of reporting delays from the epidemic data may decrease the forecast error by up to 50%. We anticipate that our framework will be indispensable in the analysis of novel COVID-19 strains and other existing or novel infectious diseases.
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Affiliation(s)
- Long Ma
- Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology, Delft, GA 2600, The Netherlands
| | - Zhihao Qiu
- Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology, Delft, GA 2600, The Netherlands
| | - Piet Van Mieghem
- Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology, Delft, GA 2600, The Netherlands
| | - Maksim Kitsak
- Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology, Delft, GA 2600, The Netherlands
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22
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Charniga K, Madewell ZJ, Masters NB, Asher J, Nakazawa Y, Spicknall IH. Nowcasting and forecasting the 2022 U.S. mpox outbreak: Support for public health decision making and lessons learned. Epidemics 2024; 47:100755. [PMID: 38452454 DOI: 10.1016/j.epidem.2024.100755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 01/14/2024] [Accepted: 02/26/2024] [Indexed: 03/09/2024] Open
Abstract
In June of 2022, the U.S. Centers for Disease Control and Prevention (CDC) Mpox Response wanted timely answers to important epidemiological questions which can now be answered more effectively through infectious disease modeling. Infectious disease models have shown to be valuable tools for decision making during outbreaks; however, model complexity often makes communicating the results and limitations of models to decision makers difficult. We performed nowcasting and forecasting for the 2022 mpox outbreak in the United States using the R package EpiNow2. We generated nowcasts/forecasts at the national level, by Census region, and for jurisdictions reporting the greatest number of mpox cases. Modeling results were shared for situational awareness within the CDC Mpox Response and publicly on the CDC website. We retrospectively evaluated forecast predictions at four key phases (early, exponential growth, peak, and decline) during the outbreak using three metrics, the weighted interval score, mean absolute error, and prediction interval coverage. We compared the performance of EpiNow2 with a naïve Bayesian generalized linear model (GLM). The EpiNow2 model had less probabilistic error than the GLM during every outbreak phase except for the early phase. We share our experiences with an existing tool for nowcasting/forecasting and highlight areas of improvement for the development of future tools. We also reflect on lessons learned regarding data quality issues and adapting modeling results for different audiences.
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Affiliation(s)
- Kelly Charniga
- Division of High-Consequence Pathogens and Pathology, National Center for Emerging and Zoonotic Infectious Diseases, CDC, USA.
| | | | | | - Jason Asher
- Center for Forecasting and Outbreak Analytics, CDC, USA
| | - Yoshinori Nakazawa
- Division of High-Consequence Pathogens and Pathology, National Center for Emerging and Zoonotic Infectious Diseases, CDC, USA
| | - Ian H Spicknall
- Division of Sexually Transmitted Disease Prevention, National Center for HIV, Viral Hepatitis, STD, & TB Prevention, CDC, USA
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23
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Mihaljevic JR, Chief C, Malik M, Oshinubi K, Doerry E, Gel E, Hepp C, Lant T, Mehrotra S, Sabo S. An inaugural forum on epidemiological modeling for public health stakeholders in Arizona. Front Public Health 2024; 12:1357908. [PMID: 38883190 PMCID: PMC11176426 DOI: 10.3389/fpubh.2024.1357908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 05/13/2024] [Indexed: 06/18/2024] Open
Abstract
Epidemiological models-which help us understand and forecast the spread of infectious disease-can be valuable tools for public health. However, barriers exist that can make it difficult to employ epidemiological models routinely within the repertoire of public health planning. These barriers include technical challenges associated with constructing the models, challenges in obtaining appropriate data for model parameterization, and problems with clear communication of modeling outputs and uncertainty. To learn about the unique barriers and opportunities within the state of Arizona, we gathered a diverse set of 48 public health stakeholders for a day-and-a-half forum. Our research group was motivated specifically by our work building software for public health-relevant modeling and by our earnest desire to collaborate closely with stakeholders to ensure that our software tools are practical and useful in the face of evolving public health needs. Here we outline the planning and structure of the forum, and we highlight as a case study some of the lessons learned from breakout discussions. While unique barriers exist for implementing modeling for public health, there is also keen interest in doing so across diverse sectors of State and Local government, although issues of equal and fair access to modeling knowledge and technologies remain key issues for future development. We found this forum to be useful for building relationships and informing our software development, and we plan to continue such meetings annually to create a continual feedback loop between academic molders and public health practitioners.
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Affiliation(s)
- Joseph R Mihaljevic
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, United States
| | - Carmenlita Chief
- Center for Health Equity Research, College of Health and Human Services, Northern Arizona University, Flagstaff, AZ, United States
| | - Mehreen Malik
- Interdisciplinary Health Program, College of Health and Human Services, Northern Arizona University, Flagstaff, AZ, United States
| | - Kayode Oshinubi
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, United States
| | - Eck Doerry
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, United States
| | - Esma Gel
- Department of Supply Chain Management and Analytics, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Crystal Hepp
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, United States
- Pathogen and Microbiome Division, Translational Genomics Research Institute, Flagstaff, AZ, United States
| | - Tim Lant
- Office of the Vice President for Research, Knowledge Enterprise, Arizona State University, Tempe, AZ, United States
| | - Sanjay Mehrotra
- Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, United States
- Center for Engineering and Health, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Samantha Sabo
- Center for Health Equity Research, College of Health and Human Services, Northern Arizona University, Flagstaff, AZ, United States
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24
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Bova ML, McGee SA, Elliott KR, Ubiera JI. Predicting COVID-19 and respiratory illness: results of the 2022-2023 Armed Forces Health Surveillance Division forecasting challenge. MSMR 2024; 31:24-30. [PMID: 38857495 PMCID: PMC11192153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/12/2024]
Abstract
Since 2019, the Integrated Biosurveillance Branch of the Armed Forces Health Surveillance Division has conducted an annual forecasting challenge during influenza season to predict short-term respiratory disease activity among Military Health System beneficiaries. Weekly case and encounter observed data were used to generate 1- through 4-week advanced forecasts of disease activity. To create unified combinations of model inputs for evaluation across multiple spatial resolutions, 8 individual models were used to calculate 3 ensemble models. Forecast accuracy compared to the observed activity for each model was evaluated by calculating a weighted interval score. Weekly 1- through 4-week ahead forecasts for each ensemble model were generally higher than observed data, especially during periods of peak activity, with peaks in forecasted activity occurring later than observed peaks. The larger the forecasting horizon, the more pronounced the gap between forecasted peak and observed peak. The results showed that several models accurately predicted COVID-19 cases and respiratory encounters with enough lead time for public health response by senior leaders.
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Affiliation(s)
- Mark L Bova
- Armed Forces Health Surveillance Division, Integrated Biosurveillance Branch, Silver Spring, MD
| | - Sasha A McGee
- Armed Forces Health Surveillance Division, Integrated Biosurveillance Branch, Silver Spring, MD
| | - Kathleen R Elliott
- Armed Forces Health Surveillance Division, Integrated Biosurveillance Branch, Silver Spring, MD
| | - Juan I Ubiera
- Armed Forces Health Surveillance Division, Integrated Biosurveillance Branch, Silver Spring, MD
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25
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Villanueva I, Conesa D, Català M, López Cano C, Perramon-Malavez A, Molinuevo D, de Rioja VL, López D, Alonso S, Cardona PJ, Montañola-Sales C, Prats C, Alvarez-Lacalle E. Country-report pattern corrections of new cases allow accurate 2-week predictions of COVID-19 evolution with the Gompertz model. Sci Rep 2024; 14:10775. [PMID: 38730261 PMCID: PMC11087483 DOI: 10.1038/s41598-024-61233-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 05/01/2024] [Indexed: 05/12/2024] Open
Abstract
Accurate short-term predictions of COVID-19 cases with empirical models allow Health Officials to prepare for hospital contingencies in a two-three week window given the delay between case reporting and the admission of patients in a hospital. We investigate the ability of Gompertz-type empiric models to provide accurate prediction up to two and three weeks to give a large window of preparation in case of a surge in virus transmission. We investigate the stability of the prediction and its accuracy using bi-weekly predictions during the last trimester of 2020 and 2021. Using data from 2020, we show that understanding and correcting for the daily reporting structure of cases in the different countries is key to accomplish accurate predictions. Furthermore, we found that filtering out predictions that are highly unstable to changes in the parameters of the model, which are roughly 20%, reduces strongly the number of predictions that are way-off. The method is then tested for robustness with data from 2021. We found that, for this data, only 1-2% of the one-week predictions were off by more than 50%. This increased to 3% for two-week predictions, and only for three-week predictions it reached 10%.
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Affiliation(s)
- I Villanueva
- Department of Physics, Universitat Politècnica de Catalunya (BarcelonaTech), 08860, Castelldefels, Spain
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08018, Barcelona, Spain
| | - D Conesa
- Department of Physics, Universitat Politècnica de Catalunya (BarcelonaTech), 08860, Castelldefels, Spain
| | - M Català
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, UK
| | - C López Cano
- Department of Physics, Universitat Politècnica de Catalunya (BarcelonaTech), 08860, Castelldefels, Spain
| | - A Perramon-Malavez
- Department of Physics, Universitat Politècnica de Catalunya (BarcelonaTech), 08860, Castelldefels, Spain
| | - D Molinuevo
- Medical Image Processing Lab, École Polytechnique Fédérale de Laussane, Geneva, Switzerland
| | - V L de Rioja
- Department of Physics, Universitat Politècnica de Catalunya (BarcelonaTech), 08860, Castelldefels, Spain
| | - D López
- Department of Physics, Universitat Politècnica de Catalunya (BarcelonaTech), 08860, Castelldefels, Spain
| | - S Alonso
- Department of Physics, Universitat Politècnica de Catalunya (BarcelonaTech), 08860, Castelldefels, Spain
| | - P J Cardona
- Microbiology Department, Laboratori Clínic Metropolitana Nord, Hospital Universitari Germans Trias i Pujol, Institut Universitari Germans Trias i Pujol (IGTP), Badalona, Catalonia, Spain
- Departament of Genetics and Microbiology, Universitat Autònoma de Barcelona, Cerdanyola, Catalonia, Spain
- Biomedical Research Networking Centre in Respiratory Diseases CIBERES, Instituto de Salud Carlos III, Madrid, Spain
| | - C Montañola-Sales
- Department of Quantitative Methods, IQS School of Management, Universitat Ramon Llull, 08017, Barcelona, Spain
| | - C Prats
- Department of Physics, Universitat Politècnica de Catalunya (BarcelonaTech), 08860, Castelldefels, Spain
- Comparative Medicine and Bioimage Centre of Catalonia (CMCiB), Fundació Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol, 08916, Badalona, Spain
| | - E Alvarez-Lacalle
- Department of Physics, Universitat Politècnica de Catalunya (BarcelonaTech), 08860, Castelldefels, Spain.
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26
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Manley H, Bayley T, Danelian G, Burton L, Finnie T, Charlett A, Watkins NA, Birrell P, De Angelis D, Keeling M, Funk S, Medley G, Pellis L, Baguelin M, Ackland GJ, Hutchinson J, Riley S, Panovska-Griffiths J. Combining models to generate consensus medium-term projections of hospital admissions, occupancy and deaths relating to COVID-19 in England. ROYAL SOCIETY OPEN SCIENCE 2024; 11:231832. [PMID: 39076350 PMCID: PMC11285879 DOI: 10.1098/rsos.231832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/29/2024] [Accepted: 03/09/2024] [Indexed: 07/31/2024]
Abstract
Mathematical modelling has played an important role in offering informed advice during the COVID-19 pandemic. In England, a cross government and academia collaboration generated medium-term projections (MTPs) of possible epidemic trajectories over the future 4-6 weeks from a collection of epidemiological models. In this article, we outline this collaborative modelling approach and evaluate the accuracy of the combined and individual model projections against the data over the period November 2021-December 2022 when various Omicron subvariants were spreading across England. Using a number of statistical methods, we quantify the predictive performance of the model projections for both the combined and individual MTPs, by evaluating the point and probabilistic accuracy. Our results illustrate that the combined MTPs, produced from an ensemble of heterogeneous epidemiological models, were a closer fit to the data than the individual models during the periods of epidemic growth or decline, with the 90% confidence intervals widest around the epidemic peaks. We also show that the combined MTPs increase the robustness and reduce the biases associated with a single model projection. Learning from our experience of ensemble modelling during the COVID-19 epidemic, our findings highlight the importance of developing cross-institutional multi-model infectious disease hubs for future outbreak control.
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Affiliation(s)
| | | | | | | | | | | | | | - Paul Birrell
- UK Health Security Agency, London, UK
- MRC Biostatistics Unit, University of Cambridge, , UK
| | - Daniela De Angelis
- UK Health Security Agency, London, UK
- MRC Biostatistics Unit, University of Cambridge, , UK
| | - Matt Keeling
- Department of Mathematics, University of Warwick, Coventry, UK
| | - Sebastian Funk
- London School of Hygiene and Tropical Medicine, London, UK
| | - Graham Medley
- London School of Hygiene and Tropical Medicine, London, UK
| | | | | | | | | | | | - Jasmina Panovska-Griffiths
- UK Health Security Agency, London, UK
- Queen’s College, University of Oxford, Oxford, UK
- The Big Data Institute and the Pandemic Sciences Institute, University of Oxford, Oxford, UK
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27
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Lopez VK, Cramer EY, Pagano R, Drake JM, O’Dea EB, Adee M, Ayer T, Chhatwal J, Dalgic OO, Ladd MA, Linas BP, Mueller PP, Xiao J, Bracher J, Castro Rivadeneira AJ, Gerding A, Gneiting T, Huang Y, Jayawardena D, Kanji AH, Le K, Mühlemann A, Niemi J, Ray EL, Stark A, Wang Y, Wattanachit N, Zorn MW, Pei S, Shaman J, Yamana TK, Tarasewicz SR, Wilson DJ, Baccam S, Gurung H, Stage S, Suchoski B, Gao L, Gu Z, Kim M, Li X, Wang G, Wang L, Wang Y, Yu S, Gardner L, Jindal S, Marshall M, Nixon K, Dent J, Hill AL, Kaminsky J, Lee EC, Lemaitre JC, Lessler J, Smith CP, Truelove S, Kinsey M, Mullany LC, Rainwater-Lovett K, Shin L, Tallaksen K, Wilson S, Karlen D, Castro L, Fairchild G, Michaud I, Osthus D, Bian J, Cao W, Gao Z, Lavista Ferres J, Li C, Liu TY, Xie X, Zhang S, Zheng S, Chinazzi M, Davis JT, Mu K, Pastore y Piontti A, Vespignani A, Xiong X, Walraven R, Chen J, Gu Q, Wang L, Xu P, Zhang W, Zou D, Gibson GC, Sheldon D, Srivastava A, Adiga A, Hurt B, Kaur G, Lewis B, Marathe M, Peddireddy AS, Porebski P, Venkatramanan S, Wang L, Prasad PV, Walker JW, Webber AE, Slayton RB, Biggerstaff M, Reich NG, Johansson MA. Challenges of COVID-19 Case Forecasting in the US, 2020-2021. PLoS Comput Biol 2024; 20:e1011200. [PMID: 38709852 PMCID: PMC11098513 DOI: 10.1371/journal.pcbi.1011200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 05/16/2024] [Accepted: 04/01/2024] [Indexed: 05/08/2024] Open
Abstract
During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.
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Affiliation(s)
- Velma K. Lopez
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Estee Y. Cramer
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Robert Pagano
- Unaffiliated, Tucson, Arizona, United States of America
| | - John M. Drake
- University of Georgia, Athens, Georgia, United States of America
| | - Eamon B. O’Dea
- University of Georgia, Athens, Georgia, United States of America
| | - Madeline Adee
- Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Turgay Ayer
- Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Jagpreet Chhatwal
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Ozden O. Dalgic
- Value Analytics Labs, Boston, Massachusetts, United States of America
| | - Mary A. Ladd
- Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Benjamin P. Linas
- Boston University School of Medicine, Boston, Massachusetts, United States of America
| | - Peter P. Mueller
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Jade Xiao
- Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Johannes Bracher
- Chair of Econometrics and Statistics, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | | | - Aaron Gerding
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Tilmann Gneiting
- Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
| | - Yuxin Huang
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Dasuni Jayawardena
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Abdul H. Kanji
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Khoa Le
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Anja Mühlemann
- Institute of Mathematical Statistics and Actuarial Science, University of Bern, Bern, Switzerland
| | - Jarad Niemi
- Iowa State University, Ames, Iowa, United States of America
| | - Evan L. Ray
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Ariane Stark
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Yijin Wang
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Nutcha Wattanachit
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Martha W. Zorn
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Sen Pei
- Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | - Jeffrey Shaman
- Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | - Teresa K. Yamana
- Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | - Samuel R. Tarasewicz
- Federal Reserve Bank of San Francisco, San Francisco, California, United States of America
| | - Daniel J. Wilson
- Federal Reserve Bank of San Francisco, San Francisco, California, United States of America
| | - Sid Baccam
- IEM, Bel Air, Maryland, United States of America
| | - Heidi Gurung
- IEM, Bel Air, Maryland, United States of America
| | - Steve Stage
- IEM, Baton Rouge, Louisiana, United States of America
| | | | - Lei Gao
- George Mason University, Fairfax, Virginia, United States of America
| | - Zhiling Gu
- Iowa State University, Ames, Iowa, United States of America
| | - Myungjin Kim
- Kyungpook National University, Bukgu, Daegu, Republic of Korea
| | - Xinyi Li
- Clemson University, Clemson, South Carolina, United States of America
| | - Guannan Wang
- College of William & Mary, Williamsburg, Virginia, United States of America
| | - Lily Wang
- George Mason University, Fairfax, Virginia, United States of America
| | - Yueying Wang
- Amazon, Seattle, Washington, United States of America
| | - Shan Yu
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Lauren Gardner
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Sonia Jindal
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | | | - Kristen Nixon
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Juan Dent
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Alison L. Hill
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Joshua Kaminsky
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Elizabeth C. Lee
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | | | - Justin Lessler
- Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Claire P. Smith
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Shaun Truelove
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Matt Kinsey
- Johns Hopkins University Applied Physics Lab, Baltimore, Maryland, United States of America
| | - Luke C. Mullany
- Johns Hopkins University Applied Physics Lab, Baltimore, Maryland, United States of America
| | | | - Lauren Shin
- Johns Hopkins University Applied Physics Lab, Baltimore, Maryland, United States of America
| | - Katharine Tallaksen
- Johns Hopkins University Applied Physics Lab, Baltimore, Maryland, United States of America
| | - Shelby Wilson
- Johns Hopkins University Applied Physics Lab, Baltimore, Maryland, United States of America
| | - Dean Karlen
- University of Victoria and TRIUMF, Victoria, British Columbia, Canada
| | - Lauren Castro
- Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Geoffrey Fairchild
- Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Isaac Michaud
- Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Dave Osthus
- Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Jiang Bian
- Microsoft, Redmond, Washington, United States of America
| | - Wei Cao
- Microsoft, Redmond, Washington, United States of America
| | - Zhifeng Gao
- Microsoft, Redmond, Washington, United States of America
| | | | - Chaozhuo Li
- Microsoft, Redmond, Washington, United States of America
| | - Tie-Yan Liu
- Microsoft, Redmond, Washington, United States of America
| | - Xing Xie
- Microsoft, Redmond, Washington, United States of America
| | - Shun Zhang
- Microsoft, Redmond, Washington, United States of America
| | - Shun Zheng
- Microsoft, Redmond, Washington, United States of America
| | - Matteo Chinazzi
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Jessica T. Davis
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Kunpeng Mu
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Ana Pastore y Piontti
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Xinyue Xiong
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | | | - Jinghui Chen
- University of California, Los Angeles, Los Angeles, California, United States of America
| | - Quanquan Gu
- University of California, Los Angeles, Los Angeles, California, United States of America
| | - Lingxiao Wang
- University of California, Los Angeles, Los Angeles, California, United States of America
| | - Pan Xu
- University of California, Los Angeles, Los Angeles, California, United States of America
| | - Weitong Zhang
- University of California, Los Angeles, Los Angeles, California, United States of America
| | - Difan Zou
- University of California, Los Angeles, Los Angeles, California, United States of America
| | - Graham Casey Gibson
- Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Daniel Sheldon
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Ajitesh Srivastava
- University of Southern California, Los Angeles, California, United States of America
| | - Aniruddha Adiga
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Benjamin Hurt
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Gursharn Kaur
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Bryan Lewis
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Madhav Marathe
- University of Virginia, Charlottesville, Virginia, United States of America
| | | | | | | | - Lijing Wang
- New Jersey Institute of Technology, Newark, New Jersey, United States of America
| | - Pragati V. Prasad
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Jo W. Walker
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Alexander E. Webber
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Rachel B. Slayton
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Matthew Biggerstaff
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Nicholas G. Reich
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Michael A. Johansson
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
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28
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Chowell G, Bleichrodt A, Luo R. Parameter estimation and forecasting with quantified uncertainty for ordinary differential equation models using QuantDiffForecast: A MATLAB toolbox and tutorial. Stat Med 2024; 43:1826-1848. [PMID: 38378161 PMCID: PMC11031352 DOI: 10.1002/sim.10036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 01/17/2024] [Accepted: 01/31/2024] [Indexed: 02/22/2024]
Abstract
Mathematical models based on systems of ordinary differential equations (ODEs) are frequently applied in various scientific fields to assess hypotheses, estimate key model parameters, and generate predictions about the system's state. To support their application, we present a comprehensive, easy-to-use, and flexible MATLAB toolbox, QuantDiffForecast, and associated tutorial to estimate parameters and generate short-term forecasts with quantified uncertainty from dynamical models based on systems of ODEs. We provide software ( https://github.com/gchowell/paramEstimation_forecasting_ODEmodels/) and detailed guidance on estimating parameters and forecasting time-series trajectories that are characterized using ODEs with quantified uncertainty through a parametric bootstrapping approach. It includes functions that allow the user to infer model parameters and assess forecasting performance for different ODE models specified by the user, using different estimation methods and error structures in the data. The tutorial is intended for a diverse audience, including students training in dynamic systems, and will be broadly applicable to estimate parameters and generate forecasts from models based on ODEs. The functions included in the toolbox are illustrated using epidemic models with varying levels of complexity applied to data from the 1918 influenza pandemic in San Francisco. A tutorial video that demonstrates the functionality of the toolbox is included.
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Affiliation(s)
- Gerardo Chowell
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Amanda Bleichrodt
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Ruiyan Luo
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
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29
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Borchering RK, Biggerstaff M, Brammer L, Budd A, Garg S, Fry AM, Iuliano AD, Reed C. Responding to the Return of Influenza in the United States by Applying Centers for Disease Control and Prevention Surveillance, Analysis, and Modeling to Inform Understanding of Seasonal Influenza. JMIR Public Health Surveill 2024; 10:e54340. [PMID: 38587882 PMCID: PMC11036179 DOI: 10.2196/54340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 02/12/2024] [Accepted: 02/13/2024] [Indexed: 04/09/2024] Open
Abstract
We reviewed the tools that have been developed to characterize and communicate seasonal influenza activity in the United States. Here we focus on systematic surveillance and applied analytics, including seasonal burden and disease severity estimation, short-term forecasting, and longer-term modeling efforts. For each set of activities, we describe the challenges and opportunities that have arisen because of the COVID-19 pandemic. In conclusion, we highlight how collaboration and communication have been and will continue to be key components of reliable and actionable influenza monitoring, forecasting, and modeling activities.
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Affiliation(s)
- Rebecca K Borchering
- National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Matthew Biggerstaff
- National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Lynnette Brammer
- National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Alicia Budd
- National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Shikha Garg
- National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Alicia M Fry
- Fulton County Board of Health, Atlanta, GA, United States
| | - A Danielle Iuliano
- National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Carrie Reed
- National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, United States
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30
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Usmani M, Brumfield KD, Magers B, Zhou A, Oh C, Mao Y, Brown W, Schmidt A, Wu CY, Shisler JL, Nguyen TH, Huq A, Colwell R, Jutla A. Building Environmental and Sociological Predictive Intelligence to Understand the Seasonal Threat of SARS-CoV-2 in Human Populations. Am J Trop Med Hyg 2024; 110:518-528. [PMID: 38320317 PMCID: PMC10919182 DOI: 10.4269/ajtmh.23-0077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 11/03/2023] [Indexed: 02/08/2024] Open
Abstract
Current modeling practices for environmental and sociological modulated infectious diseases remain inadequate to forecast the risk of outbreak(s) in human populations, partly due to a lack of integration of disciplinary knowledge, limited availability of disease surveillance datasets, and overreliance on compartmental epidemiological modeling methods. Harvesting data knowledge from virus transmission (aerosols) and detection (wastewater) of SARS-CoV-2, a heuristic score-based environmental predictive intelligence system was developed that calculates the risk of COVID-19 in the human population. Seasonal validation of the algorithm was uniquely associated with wastewater surveillance of the virus, providing a lead time of 7-14 days before a county-level outbreak. Using county-scale disease prevalence data from the United States, the algorithm could predict COVID-19 risk with an overall accuracy ranging between 81% and 98%. Similarly, using wastewater surveillance data from Illinois and Maryland, the SARS-CoV-2 detection rate was greater than 80% for 75% of the locations during the same time the risk was predicted to be high. Results suggest the importance of a holistic approach across disciplinary boundaries that can potentially allow anticipatory decision-making policies of saving lives and maximizing the use of available capacity and resources.
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Affiliation(s)
- Moiz Usmani
- GeoHealth and Hydrology Laboratory, Department of Environmental Engineering Sciences, University of Florida, Gainesville, Florida
| | - Kyle D. Brumfield
- Maryland Pathogen Research Institute, University of Maryland, College Park, Maryland
- University of Maryland Institute for Advanced Computer Studies, University of Maryland, College Park, Maryland
| | - Bailey Magers
- GeoHealth and Hydrology Laboratory, Department of Environmental Engineering Sciences, University of Florida, Gainesville, Florida
| | - Aijia Zhou
- Department of Civil and Environmental Engineering, University of Illinois at Urbana–Champaign, Urbana, Illinois
| | - Chamteut Oh
- Department of Environmental Engineering Sciences, University of Florida, Gainesville, Florida
| | - Yuqing Mao
- Department of Civil and Environmental Engineering, University of Illinois at Urbana–Champaign, Urbana, Illinois
| | - William Brown
- Department of Pathobiology, University of Illinois at Urbana–Champaign, Urbana, Illinois
| | - Arthur Schmidt
- Department of Civil and Environmental Engineering, University of Illinois at Urbana–Champaign, Urbana, Illinois
| | - Chang-Yu Wu
- Department of Environmental Engineering Sciences, University of Florida, Gainesville, Florida
- Department of Chemical, Environmental and Materials Engineering, University of Miami, Florida
| | - Joanna L. Shisler
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, Illinois
| | - Thanh H. Nguyen
- Department of Civil and Environmental Engineering, University of Illinois at Urbana–Champaign, Urbana, Illinois
| | - Anwar Huq
- Maryland Pathogen Research Institute, University of Maryland, College Park, Maryland
- University of Maryland Institute for Advanced Computer Studies, University of Maryland, College Park, Maryland
| | - Rita Colwell
- Maryland Pathogen Research Institute, University of Maryland, College Park, Maryland
- University of Maryland Institute for Advanced Computer Studies, University of Maryland, College Park, Maryland
| | - Antarpreet Jutla
- GeoHealth and Hydrology Laboratory, Department of Environmental Engineering Sciences, University of Florida, Gainesville, Florida
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Loo SL, Howerton E, Contamin L, Smith CP, Borchering RK, Mullany LC, Bents S, Carcelen E, Jung SM, Bogich T, van Panhuis WG, Kerr J, Espino J, Yan K, Hochheiser H, Runge MC, Shea K, Lessler J, Viboud C, Truelove S. The US COVID-19 and Influenza Scenario Modeling Hubs: Delivering long-term projections to guide policy. Epidemics 2024; 46:100738. [PMID: 38184954 DOI: 10.1016/j.epidem.2023.100738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 10/02/2023] [Accepted: 12/18/2023] [Indexed: 01/09/2024] Open
Abstract
Between December 2020 and April 2023, the COVID-19 Scenario Modeling Hub (SMH) generated operational multi-month projections of COVID-19 burden in the US to guide pandemic planning and decision-making in the context of high uncertainty. This effort was born out of an attempt to coordinate, synthesize and effectively use the unprecedented amount of predictive modeling that emerged throughout the COVID-19 pandemic. Here we describe the history of this massive collective research effort, the process of convening and maintaining an open modeling hub active over multiple years, and attempt to provide a blueprint for future efforts. We detail the process of generating 17 rounds of scenarios and projections at different stages of the COVID-19 pandemic, and disseminating results to the public health community and lay public. We also highlight how SMH was expanded to generate influenza projections during the 2022-23 season. We identify key impacts of SMH results on public health and draw lessons to improve future collaborative modeling efforts, research on scenario projections, and the interface between models and policy.
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Affiliation(s)
- Sara L Loo
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; International Vaccine Access Center, Johns Hopkins, Baltimore, MD, USA.
| | - Emily Howerton
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
| | - Lucie Contamin
- Public Health Dynamics Lab, University of Pittsburgh, Pittsburgh, PA, USA
| | - Claire P Smith
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Rebecca K Borchering
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
| | - Luke C Mullany
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA
| | - Samantha Bents
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Erica Carcelen
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; International Vaccine Access Center, Johns Hopkins, Baltimore, MD, USA
| | - Sung-Mok Jung
- UNC Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tiffany Bogich
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
| | - Willem G van Panhuis
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jessica Kerr
- Public Health Dynamics Lab, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jessi Espino
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA
| | - Katie Yan
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
| | - Harry Hochheiser
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA
| | - Michael C Runge
- Eastern Ecological Science Center at the Patuxent Research Refuge, US Geological Survey, Laurel, MD, USA
| | - Katriona Shea
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; UNC Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Shaun Truelove
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; International Vaccine Access Center, Johns Hopkins, Baltimore, MD, USA
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Klinkenberg D, Backer J, de Keizer N, Wallinga J. Projecting COVID-19 intensive care admissions for policy advice, the Netherlands, February 2020 to January 2021. Euro Surveill 2024; 29:2300336. [PMID: 38456214 PMCID: PMC10986673 DOI: 10.2807/1560-7917.es.2024.29.10.2300336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 12/07/2023] [Indexed: 03/09/2024] Open
Abstract
BackgroundModel projections of coronavirus disease 2019 (COVID-19) incidence help policymakers about decisions to implement or lift control measures. During the pandemic, policymakers in the Netherlands were informed on a weekly basis with short-term projections of COVID-19 intensive care unit (ICU) admissions.AimWe aimed at developing a model on ICU admissions and updating a procedure for informing policymakers.MethodThe projections were produced using an age-structured transmission model. A consistent, incremental update procedure integrating all new surveillance and hospital data was conducted weekly. First, up-to-date estimates for most parameter values were obtained through re-analysis of all data sources. Then, estimates were made for changes in the age-specific contact rates in response to policy changes. Finally, a piecewise constant transmission rate was estimated by fitting the model to reported daily ICU admissions, with a changepoint analysis guided by Akaike's Information Criterion.ResultsThe model and update procedure allowed us to make weekly projections. Most 3-week prediction intervals were accurate in covering the later observed numbers of ICU admissions. When projections were too high in March and August 2020 or too low in November 2020, the estimated effectiveness of the policy changes was adequately adapted in the changepoint analysis based on the natural accumulation of incoming data.ConclusionThe model incorporates basic epidemiological principles and most model parameters were estimated per data source. Therefore, it had potential to be adapted to a more complex epidemiological situation with the rise of new variants and the start of vaccination.
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Affiliation(s)
- Don Klinkenberg
- National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | - Jantien Backer
- National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | - Nicolette de Keizer
- Department of Medical Informatics, Amsterdam UMC, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- National Intensive Care Evaluation (NICE) Foundation, Amsterdam, The Netherlands
| | - Jacco Wallinga
- Leiden University Medical Centre, Leiden, The Netherlands
- National Institute for Public Health and the Environment, Bilthoven, The Netherlands
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Bay C, St-Onge G, Davis JT, Chinazzi M, Howerton E, Lessler J, Runge MC, Shea K, Truelove S, Viboud C, Vespignani A. Ensemble 2: Scenarios ensembling for communication and performance analysis. Epidemics 2024; 46:100748. [PMID: 38394928 DOI: 10.1016/j.epidem.2024.100748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 12/19/2023] [Accepted: 02/06/2024] [Indexed: 02/25/2024] Open
Abstract
Throughout the COVID-19 pandemic, scenario modeling played a crucial role in shaping the decision-making process of public health policies. Unlike forecasts, scenario projections rely on specific assumptions about the future that consider different plausible states-of-the-world that may or may not be realized and that depend on policy interventions, unpredictable changes in the epidemic outlook, etc. As a consequence, long-term scenario projections require different evaluation criteria than the ones used for traditional short-term epidemic forecasts. Here, we propose a novel ensemble procedure for assessing pandemic scenario projections using the results of the Scenario Modeling Hub (SMH) for COVID-19 in the United States (US). By defining a "scenario ensemble" for each model and the ensemble of models, termed "Ensemble2", we provide a synthesis of potential epidemic outcomes, which we use to assess projections' performance, bypassing the identification of the most plausible scenario. We find that overall the Ensemble2 models are well-calibrated and provide better performance than the scenario ensemble of individual models. The ensemble procedure accounts for the full range of plausible outcomes and highlights the importance of scenario design and effective communication. The scenario ensembling approach can be extended to any scenario design strategy, with potential refinements including weighting scenarios and allowing the ensembling process to evolve over time.
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Affiliation(s)
- Clara Bay
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Network Science Institute, Boston, MA, USA
| | - Guillaume St-Onge
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Network Science Institute, Boston, MA, USA
| | - Jessica T Davis
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Network Science Institute, Boston, MA, USA
| | - Matteo Chinazzi
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Network Science Institute, Boston, MA, USA; The Roux Institute, Northeastern University, Portland, ME, USA
| | - Emily Howerton
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
| | - Justin Lessler
- Department of Epidemiology, University of North Carolina Gillings School of Public Health, Chapel Hill, NC, USA; Carolina Population Center, University of North Carolina, Chapel Hill, NC, USA; Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Michael C Runge
- U.S. Geological Survey, Eastern Ecological Science Center, Laurel, MD, USA
| | - Katriona Shea
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
| | - Shaun Truelove
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA; Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Cecile Viboud
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Network Science Institute, Boston, MA, USA; The Roux Institute, Northeastern University, Portland, ME, USA.
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Kuwahara B, Bauch CT. Predicting Covid-19 pandemic waves with biologically and behaviorally informed universal differential equations. Heliyon 2024; 10:e25363. [PMID: 38370214 PMCID: PMC10869765 DOI: 10.1016/j.heliyon.2024.e25363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 12/29/2023] [Accepted: 01/25/2024] [Indexed: 02/20/2024] Open
Abstract
During the COVID-19 pandemic, it became clear that pandemic waves and population responses were locked in a mutual feedback loop in a classic example of a coupled behavior-disease system. We demonstrate for the first time that universal differential equation (UDE) models are able to extract this interplay from data. We develop a UDE model for COVID-19 and test its ability to make predictions of second pandemic waves. We find that UDEs are capable of learning coupled behavior-disease dynamics and predicting second waves in a variety of populations, provided they are supplied with learning biases describing simple assumptions about disease transmission and population response. Though not yet suitable for deployment as a policy-guiding tool, our results demonstrate potential benefits, drawbacks, and useful techniques when applying universal differential equations to coupled systems.
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Affiliation(s)
- Bruce Kuwahara
- Department of Applied Mathematics, University of Waterloo, 200 University Ave West, Waterloo, Ontario, Canada
| | - Chris T. Bauch
- Department of Applied Mathematics, University of Waterloo, 200 University Ave West, Waterloo, Ontario, Canada
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Robert A, Chapman LAC, Grah R, Niehus R, Sandmann F, Prasse B, Funk S, Kucharski AJ. Predicting subnational incidence of COVID-19 cases and deaths in EU countries. BMC Infect Dis 2024; 24:204. [PMID: 38355414 PMCID: PMC11361242 DOI: 10.1186/s12879-024-08986-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 01/04/2024] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND Recurring COVID-19 waves highlight the need for tools able to quantify transmission risk, and identify geographical areas at risk of outbreaks. Local outbreak risk depends on complex immunity patterns resulting from previous infections, vaccination, waning and immune escape, alongside other factors (population density, social contact patterns). Immunity patterns are spatially and demographically heterogeneous, and are challenging to capture in country-level forecast models. METHODS We used a spatiotemporal regression model to forecast subnational case and death counts and applied it to three EU countries as test cases: France, Czechia, and Italy. Cases in local regions arise from importations or local transmission. Our model produces age-stratified forecasts given age-stratified data, and links reported case counts to routinely collected covariates (e.g. test number, vaccine coverage). We assessed the predictive performance of our model up to four weeks ahead using proper scoring rules and compared it to the European COVID-19 Forecast Hub ensemble model. Using simulations, we evaluated the impact of variations in transmission on the forecasts. We developed an open-source RShiny App to visualise the forecasts and scenarios. RESULTS At a national level, the median relative difference between our median weekly case forecasts and the data up to four weeks ahead was 25% (IQR: 12-50%) over the prediction period. The accuracy decreased as the forecast horizon increased (on average 24% increase in the median ranked probability score per added week), while the accuracy of death forecasts was more stable. Beyond two weeks, the model generated a narrow range of likely transmission dynamics. The median national case forecasts showed similar accuracy to forecasts from the European COVID-19 Forecast Hub ensemble model, but the prediction interval was narrower in our model. Generating forecasts under alternative transmission scenarios was therefore key to capturing the range of possible short-term transmission dynamics. DISCUSSION Our model captures changes in local COVID-19 outbreak dynamics, and enables quantification of short-term transmission risk at a subnational level. The outputs of the model improve our ability to identify areas where outbreaks are most likely, and are available to a wide range of public health professionals through the Shiny App we developed.
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Affiliation(s)
- Alexis Robert
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.
| | - Lloyd A C Chapman
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.
| | - Rok Grah
- European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden
| | - Rene Niehus
- European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden
| | - Frank Sandmann
- European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden
- Current address: Robert Koch Institute, Berlin, Germany
| | - Bastian Prasse
- European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden
| | - Sebastian Funk
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Adam J Kucharski
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
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Haenchen S, McCabe B, Mack WJ, Doctor JN, Linder JA, Persell SD, Tibbels J, Meeker D. Use of Telehealth Information for Early Detection: Insights From the COVID-19 Pandemic. Am J Public Health 2024; 114:218-225. [PMID: 38335480 PMCID: PMC10862224 DOI: 10.2105/ajph.2023.307499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2024]
Abstract
Objectives. To examine whether the addition of telehealth data to existing surveillance infrastructure can improve forecasts of cases and mortality. Methods. In this observational study, we compared accuracy of 14-day forecasts using real-time data available to the National Syndromic Surveillance Program (standard forecasts) to forecasts that also included telehealth information (telehealth forecasts). The study was performed in a national telehealth service provider in 2020 serving 50 US states and the District of Columbia. Results. Among 10.5 million telemedicine encounters, 169 672 probable COVID-19 cases were diagnosed by 5050 clinicians, with a rate between 0.79 and 47.8 probable cases per 100 000 encounters per day (mean = 8.37; SD = 10.75). Publicly reported case counts ranged from 0.5 to 237 916 (mean: 53 913; SD = 47 466) and 0 to 2328 deaths (mean = 1035; SD = 550) per day. Telehealth-based forecasts improved 14-day case forecasting accuracy by 1.8 percentage points to 30.9% (P = .06) and mortality forecasting by 6.4 percentage points to 26.9% (P < .048). Conclusions. Modest improvements in forecasting can be gained from adding telehealth data to syndromic surveillance infrastructure. (Am J Public Health. 2024;114(2):218-225. https://doi.org/10.2105/AJPH.2023.307499).
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Affiliation(s)
- Steven Haenchen
- Steven Haenchen, Bridget McCabe, and Jason Tibbels are with Teladoc Health, Purchase, NY. Wendy J. Mack is with Keck School of Medicine, University of Southern California, Los Angeles. Jason N. Doctor is with Schaeffer Center for Health Policy and Economics, University of Southern California. Jeffrey A. Linder and Stephen D. Persell are with Division of General Internal Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL. Daniella Meeker is with Yale School of Medicine, Yale University, New Haven, CT
| | - Bridget McCabe
- Steven Haenchen, Bridget McCabe, and Jason Tibbels are with Teladoc Health, Purchase, NY. Wendy J. Mack is with Keck School of Medicine, University of Southern California, Los Angeles. Jason N. Doctor is with Schaeffer Center for Health Policy and Economics, University of Southern California. Jeffrey A. Linder and Stephen D. Persell are with Division of General Internal Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL. Daniella Meeker is with Yale School of Medicine, Yale University, New Haven, CT
| | - Wendy J Mack
- Steven Haenchen, Bridget McCabe, and Jason Tibbels are with Teladoc Health, Purchase, NY. Wendy J. Mack is with Keck School of Medicine, University of Southern California, Los Angeles. Jason N. Doctor is with Schaeffer Center for Health Policy and Economics, University of Southern California. Jeffrey A. Linder and Stephen D. Persell are with Division of General Internal Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL. Daniella Meeker is with Yale School of Medicine, Yale University, New Haven, CT
| | - Jason N Doctor
- Steven Haenchen, Bridget McCabe, and Jason Tibbels are with Teladoc Health, Purchase, NY. Wendy J. Mack is with Keck School of Medicine, University of Southern California, Los Angeles. Jason N. Doctor is with Schaeffer Center for Health Policy and Economics, University of Southern California. Jeffrey A. Linder and Stephen D. Persell are with Division of General Internal Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL. Daniella Meeker is with Yale School of Medicine, Yale University, New Haven, CT
| | - Jeffrey A Linder
- Steven Haenchen, Bridget McCabe, and Jason Tibbels are with Teladoc Health, Purchase, NY. Wendy J. Mack is with Keck School of Medicine, University of Southern California, Los Angeles. Jason N. Doctor is with Schaeffer Center for Health Policy and Economics, University of Southern California. Jeffrey A. Linder and Stephen D. Persell are with Division of General Internal Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL. Daniella Meeker is with Yale School of Medicine, Yale University, New Haven, CT
| | - Stephen D Persell
- Steven Haenchen, Bridget McCabe, and Jason Tibbels are with Teladoc Health, Purchase, NY. Wendy J. Mack is with Keck School of Medicine, University of Southern California, Los Angeles. Jason N. Doctor is with Schaeffer Center for Health Policy and Economics, University of Southern California. Jeffrey A. Linder and Stephen D. Persell are with Division of General Internal Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL. Daniella Meeker is with Yale School of Medicine, Yale University, New Haven, CT
| | - Jason Tibbels
- Steven Haenchen, Bridget McCabe, and Jason Tibbels are with Teladoc Health, Purchase, NY. Wendy J. Mack is with Keck School of Medicine, University of Southern California, Los Angeles. Jason N. Doctor is with Schaeffer Center for Health Policy and Economics, University of Southern California. Jeffrey A. Linder and Stephen D. Persell are with Division of General Internal Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL. Daniella Meeker is with Yale School of Medicine, Yale University, New Haven, CT
| | - Daniella Meeker
- Steven Haenchen, Bridget McCabe, and Jason Tibbels are with Teladoc Health, Purchase, NY. Wendy J. Mack is with Keck School of Medicine, University of Southern California, Los Angeles. Jason N. Doctor is with Schaeffer Center for Health Policy and Economics, University of Southern California. Jeffrey A. Linder and Stephen D. Persell are with Division of General Internal Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL. Daniella Meeker is with Yale School of Medicine, Yale University, New Haven, CT
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Chowell G, Bleichrodt A, Dahal S, Tariq A, Roosa K, Hyman JM, Luo R. GrowthPredict: A toolbox and tutorial-based primer for fitting and forecasting growth trajectories using phenomenological growth models. Sci Rep 2024; 14:1630. [PMID: 38238407 PMCID: PMC10796326 DOI: 10.1038/s41598-024-51852-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 01/10/2024] [Indexed: 01/22/2024] Open
Abstract
Simple dynamic modeling tools can help generate real-time short-term forecasts with quantified uncertainty of the trajectory of diverse growth processes unfolding in nature and society, including disease outbreaks. An easy-to-use and flexible toolbox for this purpose is lacking. This tutorial-based primer introduces and illustrates GrowthPredict, a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using phenomenological dynamic growth models based on ordinary differential equations. This toolbox is accessible to a broad audience, including students training in mathematical biology, applied statistics, and infectious disease modeling, as well as researchers and policymakers who need to conduct short-term forecasts in real-time. The models included in the toolbox capture exponential and sub-exponential growth patterns that typically follow a rising pattern followed by a decline phase, a common feature of contagion processes. Models include the 1-parameter exponential growth model and the 2-parameter generalized-growth model, which have proven useful in characterizing and forecasting the ascending phase of epidemic outbreaks. It also includes the 2-parameter Gompertz model, the 3-parameter generalized logistic-growth model, and the 3-parameter Richards model, which have demonstrated competitive performance in forecasting single peak outbreaks. We provide detailed guidance on forecasting time-series trajectories and available software ( https://github.com/gchowell/forecasting_growthmodels ), including the full uncertainty distribution derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance across different models, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score. This tutorial and toolbox can be broadly applied to characterizing and forecasting time-series data using simple phenomenological growth models. As a contagion process takes off, the tools presented in this tutorial can help create forecasts to guide policy regarding implementing control strategies and assess the impact of interventions. The toolbox functionality is demonstrated through various examples, including a tutorial video, and the examples use publicly available data on the monkeypox (mpox) epidemic in the USA.
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Affiliation(s)
- Gerardo Chowell
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA.
| | - Amanda Bleichrodt
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Sushma Dahal
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Amna Tariq
- School of Medicine, Stanford University, Stanford, CA, USA
| | - Kimberlyn Roosa
- National Institute for Mathematical and Biological Synthesis (NIMBioS), University of Tennessee, Knoxville, TN, USA
| | - James M Hyman
- Department of Mathematics, Center for Computational Science, Tulane University, New Orleans, LA, USA
| | - Ruiyan Luo
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
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Chen Y, Gel YR, Marathe MV, Poor HV. A simplicial epidemic model for COVID-19 spread analysis. Proc Natl Acad Sci U S A 2024; 121:e2313171120. [PMID: 38147553 PMCID: PMC10769830 DOI: 10.1073/pnas.2313171120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 11/11/2023] [Indexed: 12/28/2023] Open
Abstract
Networks allow us to describe a wide range of interaction phenomena that occur in complex systems arising in such diverse fields of knowledge as neuroscience, engineering, ecology, finance, and social sciences. Until very recently, the primary focus of network models and tools has been on describing the pairwise relationships between system entities. However, increasingly more studies indicate that polyadic or higher-order group relationships among multiple network entities may be the key toward better understanding of the intrinsic mechanisms behind the functionality of complex systems. Such group interactions can be, in turn, described in a holistic manner by simplicial complexes of graphs. Inspired by these recently emerging results on the utility of the simplicial geometry of complex networks for contagion propagation and armed with a large-scale synthetic social contact network (also known as a digital twin) of the population in the U.S. state of Virginia, in this paper, we aim to glean insights into the role of higher-order social interactions and the associated varying social group determinants on COVID-19 propagation and mitigation measures.
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Affiliation(s)
- Yuzhou Chen
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA19122
| | - Yulia R. Gel
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, TX75080
- Division of Mathematical Sciences, NSF, Alexandria, VA22314
| | - Madhav V. Marathe
- Department of Computer Science, University of Virginia
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA22904
| | - H. Vincent Poor
- Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ08544
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Cueva K, Bulkow L, Milton E, Van Wyck R, Hargrave S, Meyer J. Application of COVIDTracer to Inform Public Health Workforce Planning in Alaska During the COVID-19 Pandemic. Public Health Rep 2024; 139:11-17. [PMID: 37846519 PMCID: PMC10905761 DOI: 10.1177/00333549231199479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2023] Open
Abstract
The COVID-19 pandemic has caused social and economic disruption worldwide and spurred numerous mitigation strategies, including state investments in training a large contact tracing and case investigation workforce. A team at the University of Alaska Anchorage evaluated implementation of the COVID-19 contact tracing and case investigation program of the State of Alaska Department of Health and Social Services, Division of Public Health, Section of Public Health Nursing. As part of that evaluation, the team used COVIDTracer, a spreadsheet modeling tool. COVIDTracer generated projections of COVID-19 case counts that informed estimates of workforce needs and case prioritization strategies. Case count projections approximated the reported epidemiologic curve with a median 7% difference in the first month. The accuracy of case count predictions declined after 1 month with a median difference of 80% in the second month. COVIDTracer inputs included previous case counts, the average length of time for telephone calls to cases and outreach to identified contacts, and the average number of contacts per case. As each variable increased, so too did estimated workforce needs. Decreasing the average time from exposure to outreach from 10 to 5 days reduced case counts estimated by COVIDTracer by approximately 93% during a 5-month period. COVIDTracer estimates informed Alaska's workforce planning and decisions about prioritizing case investigation during the pandemic. Lessons learned included the importance of being able to rapidly scale up and scale down workforce to adjust to a dynamic crisis and the limitations of prediction modeling (eg, that COVIDTracer was accurate for only about 1 month into the future). These findings may be useful for future pandemic preparedness planning and other public health emergency response activities.
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Affiliation(s)
- Katie Cueva
- Center for Behavioral Health Research and Services, Institute of Social and Economic Research, University of Alaska Anchorage, Anchorage, AK, USA
| | - Lisa Bulkow
- Division of Population Health Sciences, University of Alaska Anchorage, Anchorage, AK, USA
| | - Elaina Milton
- Division of Population Health Sciences, University of Alaska Anchorage, Anchorage, AK, USA
| | - Rebecca Van Wyck
- Division of Population Health Sciences, University of Alaska Anchorage, Anchorage, AK, USA
| | - Sarah Hargrave
- Section of Public Health Nursing, Division of Public Health, State of Alaska Department of Health and Social Services, Anchorage, AK, USA
| | - Jennifer Meyer
- Division of Population Health Sciences, University of Alaska Anchorage, Anchorage, AK, USA
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40
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Levi R, Zerhouni EG, Altuvia S. Predicting the spread of SARS-CoV-2 variants: An artificial intelligence enabled early detection. PNAS NEXUS 2024; 3:pgad424. [PMID: 38170049 PMCID: PMC10759796 DOI: 10.1093/pnasnexus/pgad424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 11/27/2023] [Indexed: 01/05/2024]
Abstract
During more than 3 years since its emergence, SARS-CoV-2 has shown great ability to mutate rapidly into diverse variants, some of which turned out to be very infectious and have spread throughout the world causing waves of infections. At this point, many countries have already experienced up to six waves of infections. Extensive academic work has focused on the development of models to predict the pandemic trajectory based on epidemiological data, but none has focused on predicting variant-specific spread. Moreover, important scientific literature analyzes the genetic evolution of SARS-CoV-2 variants and how it might functionally affect their infectivity. However, genetic attributes have not yet been incorporated into existing epidemiological modeling that aims to capture infection trajectory. Thus, this study leverages variant-specific genetic characteristics together with epidemiological information to systematically predict the future spread trajectory of newly detected variants. The study describes the analysis of 9.0 million SARS-CoV-2 genetic sequences in 30 countries and identifies temporal characteristic patterns of SARS-CoV-2 variants that caused significant infection waves. Using this descriptive analysis, a machine-learning-enabled risk assessment model has been developed to predict, as early as 1 week after their first detection, which variants are likely to constitute the new wave of infections in the following 3 months. The model's out-of-sample area under the curve (AUC) is 86.3% for predictions after 1 week and 90.8% for predictions after 2 weeks. The methodology described in this paper could contribute more broadly to the development of improved predictive models for variants of other infectious viruses.
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Affiliation(s)
- Retsef Levi
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - El Ghali Zerhouni
- Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Shoshy Altuvia
- Department of Microbiology and Molecular Genetics, The Hebrew University-Hadassah Medical School, Jerusalem, 9112102, Israel
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41
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Chen L. Application progress of ensemble forecast technology in influenza forecast based on infectious disease model. Front Public Health 2023; 11:1335499. [PMID: 38162616 PMCID: PMC10755026 DOI: 10.3389/fpubh.2023.1335499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 12/04/2023] [Indexed: 01/03/2024] Open
Abstract
To comprehensively understand the application progress of ensemble forecast technology in influenza forecast based on infectious disease model, so as to provide scientific references for further research. In this study, two keywords of "influenza" and "ensemble forecast" are selected to search and select the relevant literatures, which are then outlined and summarized. It is found that: In recent years, some studies about ensemble forecast technology for influenza have been reported in the literature, and some well-performed influenza ensemble forecast systems have already been operationally implemented and provide references for scientific prevention and control. In general, ensemble forecast can well represent various uncertainties in forecasting influenza cases based on infectious disease models, and can achieve more accurate forecasts and more valuable information than single deterministic forecast. However, there are still some shortcomings in the current studies, it is suggested that scientists engaged in influenza forecast based on infectious disease models strengthen cooperation with scholars in the field of numerical weather forecast, which is expected to further improve the skills and application level of ensemble forecast for influenza.
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Affiliation(s)
- Lianglyu Chen
- Chongqing Institute of Meteorological Sciences, Chongqing, China
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42
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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 : THE PREPRINT SERVER FOR HEALTH SCIENCES 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] [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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Srivastava A. The variations of SIkJalpha model for COVID-19 forecasting and scenario projections. Epidemics 2023; 45:100729. [PMID: 37981463 DOI: 10.1016/j.epidem.2023.100729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 11/07/2023] [Accepted: 11/08/2023] [Indexed: 11/21/2023] Open
Abstract
We proposed the SIkJalpha model at the beginning of the COVID-19 pandemic (early 2020). Since then, as the pandemic evolved, more complexities were added to capture crucial factors and variables that can assist with projecting desired future scenarios. Throughout the pandemic, multi-model collaborative efforts have been organized to predict short-term outcomes (cases, deaths, and hospitalizations) of COVID-19 and long-term scenario projections. We have been participating in five such efforts. This paper presents the evolution of the SIkJalpha model and its many versions that have been used to submit to these collaborative efforts since the beginning of the pandemic. Specifically, we show that the SIkJalpha model is an approximation of a class of epidemiological models. We demonstrate how the model can be used to incorporate various complexities, including under-reporting, multiple variants, waning of immunity, and contact rates, and to generate probabilistic outputs.
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44
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Reich NG, Wang Y, Burns M, Ergas R, Cramer EY, Ray EL. Assessing the utility of COVID-19 case reports as a leading indicator for hospitalization forecasting in the United States. Epidemics 2023; 45:100728. [PMID: 37976681 PMCID: PMC10871058 DOI: 10.1016/j.epidem.2023.100728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 09/29/2023] [Accepted: 11/06/2023] [Indexed: 11/19/2023] Open
Abstract
Identifying data streams that can consistently improve the accuracy of epidemiological forecasting models is challenging. Using models designed to predict daily state-level hospital admissions due to COVID-19 in California and Massachusetts, we investigated whether incorporating COVID-19 case data systematically improved forecast accuracy. Additionally, we considered whether using case data aggregated by date of test or by date of report from a surveillance system made a difference to the forecast accuracy. Evaluating forecast accuracy in a test period, after first having selected the best-performing methods in a validation period, we found that overall the difference in accuracy between approaches was small, especially at forecast horizons of less than two weeks. However, forecasts from models using cases aggregated by test date showed lower accuracy at longer horizons and at key moments in the pandemic, such as the peak of the Omicron wave in January 2022. Overall, these results highlight the challenge of finding a modeling approach that can generate accurate forecasts of outbreak trends both during periods of relative stability and during periods that show rapid growth or decay of transmission rates. While COVID-19 case counts seem to be a natural choice to help predict COVID-19 hospitalizations, in practice any benefits we observed were small and inconsistent.
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Affiliation(s)
- Nicholas G Reich
- School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, United States of America.
| | - Yijin Wang
- School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, United States of America
| | - Meagan Burns
- Massachusetts Department of Public Health, Boston, MA, United States of America
| | - Rosa Ergas
- Massachusetts Department of Public Health, Boston, MA, United States of America
| | - Estee Y Cramer
- School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, United States of America
| | - Evan L Ray
- School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, United States of America
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45
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Jit M, Ainslie K, Althaus C, Caetano C, Colizza V, Paolotti D, Beutels P, Willem L, Edmunds J, Nunes B, Namorado S, Faes C, Low N, Wallinga J, Hens N. Reflections On Epidemiological Modeling To Inform Policy During The COVID-19 Pandemic In Western Europe, 2020-23. Health Aff (Millwood) 2023; 42:1630-1636. [PMID: 38048502 DOI: 10.1377/hlthaff.2023.00688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/06/2023]
Abstract
We reflect on epidemiological modeling conducted throughout the COVID-19 pandemic in Western Europe, specifically in Belgium, France, Italy, the Netherlands, Portugal, Switzerland, and the United Kingdom. Western Europe was initially one of the worst-hit regions during the COVID-19 pandemic. Western European countries deployed a range of policy responses to the pandemic, which were often informed by mathematical, computational, and statistical models. Models differed in terms of temporal scope, pandemic stage, interventions modeled, and analytical form. This diversity was modulated by differences in data availability and quality, government interventions, societal responses, and technical capacity. Many of these models were decisive to policy making at key junctures, such as during the introduction of vaccination and the emergence of the Alpha, Delta, and Omicron variants. However, models also faced intense criticism from the press, other scientists, and politicians around their accuracy and appropriateness for decision making. Hence, evaluating the success of models in terms of accuracy and influence is an essential task. Modeling needs to be supported by infrastructure for systems to collect and share data, model development, and collaboration between groups, as well as two-way engagement between modelers and both policy makers and the public.
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Affiliation(s)
- Mark Jit
- Mark Jit , London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Kylie Ainslie
- Kylie Ainslie, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
| | | | - Constantino Caetano
- Constantino Caetano, National Institute of Health Doutor Ricardo Jorge, Lisbon, Portugal
| | | | | | | | | | - John Edmunds
- John Edmunds, London School of Hygiene and Tropical Medicine
| | - Baltazar Nunes
- Baltazar Nunes, National Institute of Health Doutor Ricardo Jorge
| | - Sónia Namorado
- Sónia Namorado, National Institute of Health Doutor Ricardo Jorge
| | | | | | - Jacco Wallinga
- Jacco Wallinga, National Institute for Public Health and the Environment (RIVM)
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46
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Grieve R, Yang Y, Abbott S, Babu GR, Bhattacharyya M, Dean N, Evans S, Jewell N, Langan SM, Lee W, Molenberghs G, Smeeth L, Williamson E, Mukherjee B. The importance of investing in data, models, experiments, team science, and public trust to help policymakers prepare for the next pandemic. PLOS GLOBAL PUBLIC HEALTH 2023; 3:e0002601. [PMID: 38032861 PMCID: PMC10688710 DOI: 10.1371/journal.pgph.0002601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/02/2023]
Abstract
The COVID-19 pandemic has brought about valuable insights regarding models, data, and experiments. In this narrative review, we summarised the existing literature on these three themes, exploring the challenges of providing forecasts, the requirement for real-time linkage of health-related datasets, and the role of 'experimentation' in evaluating interventions. This literature review encourages us to broaden our perspective for the future, acknowledging the significance of investing in models, data, and experimentation, but also to invest in areas that are conceptually more abstract: the value of 'team science', the need for public trust in science, and in establishing processes for using science in policy. Policy-makers rely on model forecasts early in a pandemic when there is little data, and it is vital to communicate the assumptions, limitations, and uncertainties (theme 1). Linked routine data can provide critical information, for example, in establishing risk factors for adverse outcomes but are often not available quickly enough to make a real-time impact. The interoperability of data resources internationally is required to facilitate sharing across jurisdictions (theme 2). Randomised controlled trials (RCTs) provided timely evidence on the efficacy and safety of vaccinations and pharmaceuticals but were largely conducted in higher income countries, restricting generalisability to low- and middle-income countries (LMIC). Trials for non-pharmaceutical interventions (NPIs) were almost non-existent which was a missed opportunity (theme 3). Building on these themes from the narrative review, we underscore the importance of three other areas that need investment for effective evidence-driven policy-making. The COVID-19 response relied on strong multidisciplinary research infrastructures, but funders and academic institutions need to do more to incentivise team science (4). To enhance public trust in the use of scientific evidence for policy, researchers and policy-makers must work together to clearly communicate uncertainties in current evidence and any need to change policy as evidence evolves (5). Timely policy decisions require an established two-way process between scientists and policy makers to make the best use of evidence (6). For effective preparedness against future pandemics, it is essential to establish models, data, and experiments as fundamental pillars, complemented by efforts in planning and investment towards team science, public trust, and evidence-based policy-making across international communities. The paper concludes with a 'call to actions' for both policy-makers and researchers.
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Affiliation(s)
- Richard Grieve
- Centre for Data and Statistical Science for Health (DASH), London School of Hygiene and Tropical Medicine, London, United Kingdom
- Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Youqi Yang
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Sam Abbott
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Giridhara R. Babu
- Indian Institute of Public Health, Public Health Foundation of India, Bengaluru, India
| | | | - Natalie Dean
- Department of Biostatistics & Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Stephen Evans
- Centre for Data and Statistical Science for Health (DASH), London School of Hygiene and Tropical Medicine, London, United Kingdom
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Nicholas Jewell
- Centre for Data and Statistical Science for Health (DASH), London School of Hygiene and Tropical Medicine, London, United Kingdom
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Sinéad M. Langan
- Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Woojoo Lee
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Geert Molenberghs
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Universiteit Hasselt & KU Leuven, Hasselt, Belgium
| | - Liam Smeeth
- Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Elizabeth Williamson
- Centre for Data and Statistical Science for Health (DASH), London School of Hygiene and Tropical Medicine, London, United Kingdom
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Bhramar Mukherjee
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America
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47
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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: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [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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48
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Wattanachit N, Ray EL, McAndrew TC, Reich NG. Comparison of combination methods to create calibrated ensemble forecasts for seasonal influenza in the U.S. Stat Med 2023; 42:4696-4712. [PMID: 37648218 PMCID: PMC10710272 DOI: 10.1002/sim.9884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 07/10/2023] [Accepted: 08/10/2023] [Indexed: 09/01/2023]
Abstract
The characteristics of influenza seasons vary substantially from year to year, posing challenges for public health preparation and response. Influenza forecasting is used to inform seasonal outbreak response, which can in turn potentially reduce the impact of an epidemic. The United States Centers for Disease Control and Prevention, in collaboration with external researchers, has run an annual prospective influenza forecasting exercise, known as the FluSight challenge. Uniting theoretical results from the forecasting literature with domain-specific forecasts from influenza outbreaks, we applied parametric forecast combination methods that simultaneously optimize model weights and calibrate the ensemble via a beta transformation and made adjustments to the methods to reduce their complexity. We used the beta-transformed linear pool, the finite beta mixture model, and their equal weight adaptations to produce ensemble forecasts retrospectively for the 2016/2017, 2017/2018, and 2018/2019 influenza seasons in the U.S. We compared their performance to methods that were used in the FluSight challenge to produce the FluSight Network ensemble, namely the equally weighted linear pool and the linear pool. Ensemble forecasts produced from methods with a beta transformation were shown to outperform those from the equally weighted linear pool and the linear pool for all week-ahead targets across in the test seasons based on average log scores. We observed improvements in overall accuracy despite the beta-transformed linear pool or beta mixture methods' modest under-prediction across all targets and seasons. Combination techniques that explicitly adjust for known calibration issues in linear pooling should be considered to improve probabilistic scores in outbreak settings.
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Affiliation(s)
- Nutcha Wattanachit
- School of Public Health and Health Sciences, University of Massachusetts Amherst, Massachusetts, USA
| | - Evan L. Ray
- School of Public Health and Health Sciences, University of Massachusetts Amherst, Massachusetts, USA
| | | | - Nicholas G. Reich
- School of Public Health and Health Sciences, University of Massachusetts Amherst, Massachusetts, USA
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49
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de Souza GN, Mendes AGB, Costa JDS, Oliveira MDS, Lima PVC, de Moraes VN, Silva DCC, da Rocha JEC, Botelho MDN, Araujo FA, Fernandes RDS, Souza DL, Braga MDB. Deep learning framework for epidemiological forecasting: A study on COVID-19 cases and deaths in the Amazon state of Pará, Brazil. PLoS One 2023; 18:e0291138. [PMID: 37976312 PMCID: PMC10656034 DOI: 10.1371/journal.pone.0291138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 08/22/2023] [Indexed: 11/19/2023] Open
Abstract
Modeling time series has been a particularly challenging aspect due to the need for constant adjustments in a rapidly changing environment, data uncertainty, dependencies between variables, volatile fluctuations, and the need to identify ideal hyperparameters. The present study presents a Framework capable of making projections from time series related to cases and deaths by COVID-19 in the Amazonian state of Pará, in Brazil. For the first time, deep learning models such as TCN, TRANSFORMER, TFT, N-BEATS, and N-HiTS were assessed for this purpose. The ARIMA statistical model was also used in post-processing for residual adjustment and short-term smoothing of the generated forecasts. The Framework generates probabilistic forecasts, with multivariate support, considering the following variables: daily cases per day of the first symptom, cases published daily, the occurrence of deaths, deaths published daily, and percentage of daily vaccination. The generated predictions are statistically evaluated by determining the best model for 7-day moving average projections using evaluating metrics such as MSE, RMSE, MAPE, sMAPE, r2, Coefficient of Variation, and residual analysis. As a result, the generated projections showed an average error of 5.4% for Cases Publication, 8.0% for Cases Symptoms, 11.12% for Deaths Publication, and 4.6% for Deaths Occurrence, with the N-HiTS and N-BEATS models obtaining better results. In general terms, the use of deep learning models to predict cases and deaths from COVID-19 has proven to be a valuable practice for analyzing the spread of the virus, which allows health managers to better understand and respond to this kind of pandemic outbreak.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Daniel Leal Souza
- Computer Science Institute, Centro Universitário do Estado do Pará, Belém, Pará, Brazil
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50
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Koichubekov B, Takuadina A, Korshukov I, Sorokina M, Turmukhambetova A. The Epidemiological and Economic Impact of COVID-19 in Kazakhstan: An Agent-Based Modeling. Healthcare (Basel) 2023; 11:2968. [PMID: 37998460 PMCID: PMC10671669 DOI: 10.3390/healthcare11222968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 11/09/2023] [Accepted: 11/09/2023] [Indexed: 11/25/2023] Open
Abstract
BACKGROUND Our study aimed to assess how effective the preventative measures taken by the state authorities during the pandemic were in terms of public health protection and the rational use of material and human resources. MATERIALS AND METHODS We utilized a stochastic agent-based model for COVID-19's spread combined with the WHO-recommended COVID-ESFT version 2.0 tool for material and labor cost estimation. RESULTS Our long-term forecasts (up to 50 days) showed satisfactory results with a steady trend in the total cases. However, the short-term forecasts (up to 10 days) were more accurate during periods of relative stability interrupted by sudden outbreaks. The simulations indicated that the infection's spread was highest within families, with most COVID-19 cases occurring in the 26-59 age group. Government interventions resulted in 3.2 times fewer cases in Karaganda than predicted under a "no intervention" scenario, yielding an estimated economic benefit of 40%. CONCLUSION The combined tool we propose can accurately forecast the progression of the infection, enabling health organizations to allocate specialists and material resources in a timely manner.
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Affiliation(s)
- Berik Koichubekov
- Department of Informatics and Biostatistics, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan; (A.T.); (I.K.); (M.S.)
| | - Aliya Takuadina
- Department of Informatics and Biostatistics, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan; (A.T.); (I.K.); (M.S.)
| | - Ilya Korshukov
- Department of Informatics and Biostatistics, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan; (A.T.); (I.K.); (M.S.)
| | - Marina Sorokina
- Department of Informatics and Biostatistics, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan; (A.T.); (I.K.); (M.S.)
| | - Anar Turmukhambetova
- Institute of Life Sciences, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan;
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