Multiple models for outbreak decision support in the face of uncertainty-Reference-Cited by-同舟云学术

Multiple models for outbreak decision support in the face of uncertainty

Published:2023-04-25 Issue:18 Volume:120 Page:
ISSN:0027-8424
Container-title:Proceedings of the National Academy of Sciences
language:en
Short-container-title:Proc. Natl. Acad. Sci. U.S.A.

Author:

Shea Katriona¹²^ORCID,Borchering Rebecca K.¹²^ORCID,Probert William J. M.³^ORCID,Howerton Emily¹²^ORCID,Bogich Tiffany L.¹²^ORCID,Li Shou-Li⁴^ORCID,van Panhuis Willem G.⁵,Viboud Cecile⁶,Aguás Ricardo³^ORCID,Belov Artur A.⁷,Bhargava Sanjana H.⁸^ORCID,Cavany Sean M.⁹^ORCID,Chang Joshua C.¹⁰¹¹^ORCID,Chen Cynthia¹²^ORCID,Chen Jinghui¹³,Chen Shi¹⁴¹⁵^ORCID,Chen YangQuan¹⁶^ORCID,Childs Lauren M.¹⁷^ORCID,Chow Carson C.¹⁸^ORCID,Crooker Isabel¹⁹,Del Valle Sara Y.¹⁹^ORCID,España Guido⁹^ORCID,Fairchild Geoffrey¹⁹^ORCID,Gerkin Richard C.²⁰^ORCID,Germann Timothy C.¹⁹^ORCID,Gu Quanquan¹³^ORCID,Guan Xiangyang¹²^ORCID,Guo Lihong²¹^ORCID,Hart Gregory R.²²,Hladish Thomas J.⁸²³^ORCID,Hupert Nathaniel²⁴^ORCID,Janies Daniel²⁵,Kerr Cliff C.²²^ORCID,Klein Daniel J.²²,Klein Eili Y.²⁶²⁷^ORCID,Lin Gary²⁶²⁷^ORCID,Manore Carrie¹⁹,Meyers Lauren Ancel²⁸^ORCID,Mittler John E.²⁹^ORCID,Mu Kunpeng³⁰,Núñez Rafael C.²²,Oidtman Rachel J.⁹^ORCID,Pasco Remy³¹^ORCID,Pastore y Piontti Ana³⁰,Paul Rajib¹⁴,Pearson Carl A. B.³²³³³⁴^ORCID,Perdomo Dianela R.⁸^ORCID,Perkins T. Alex⁹^ORCID,Pierce Kelly³⁵^ORCID,Pillai Alexander N.⁸^ORCID,Rael Rosalyn Cherie¹⁹^ORCID,Rosenfeld Katherine²²^ORCID,Ross Chrysm Watson¹⁹^ORCID,Spencer Julie A.¹⁹^ORCID,Stoltzfus Arlin B.³⁶,Toh Kok Ben³⁷,Vattikuti Shashaank¹⁸^ORCID,Vespignani Alessandro³⁰^ORCID,Wang Lingxiao¹³,White Lisa J.³^ORCID,Xu Pan¹³^ORCID,Yang Yupeng²⁷,Yogurtcu Osman N.⁷^ORCID,Zhang Weitong¹³,Zhao Yanting³⁸,Zou Difan¹³,Ferrari Matthew J.¹²,Pannell David³⁹^ORCID,Tildesley Michael J.⁴⁰,Seifarth Jack¹²^ORCID,Johnson Elyse¹²,Biggerstaff Matthew⁴¹^ORCID,Johansson Michael A.⁴¹^ORCID,Slayton Rachel B.⁴¹^ORCID,Levander John D.⁴²^ORCID,Stazer Jeff⁴²,Kerr Jessica⁴²,Runge Michael C.⁴³^ORCID

Affiliation:

1. Department of Biology, The Pennsylvania State University, University Park, PA 16802

2. Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA 16802

3. Nuffield Department of Medicine, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom

4. State Key Laboratory of Grassland Agro-ecosystems, Center for Grassland Microbiome, and College of Pastoral, Agriculture Science and Technology, Lanzhou University, Lanzhou, 73000, People’s Republic of China

5. Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15260

6. Fogarty International Center, National Institutes of Health, Bethesda, MD 20892

7. Office of Biostatistics and Pharmacovigilance, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD 20993

8. Department of Biology, University of Florida, Gainesville, FL 32611

9. Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556

10. Epidemiology and Biostatistics Section, Rehabilitation Medicine, Clinical Center, National Institutes of Health, Bethesda, MD 20892

11. Mederrata Research Inc, Columbus, OH 43212

12. Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195

13. Department of Computer Science, University of California, Los Angeles, Los Angeles, CA 90095

14. Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223

15. School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223

16. Mechatronics, Embedded Systems and Automation Laboratory, School of Engineering, University of California, Merced, CA 95343

17. Department of Mathematics, Virginia Tech, Blacksburg, VA 24061

18. Mathematical Biology Section, Laboratory of Biological Modeling, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892

19. Los Alamos National Laboratory, Los Alamos, NM 87545

20. School of Life Sciences, Arizona State University, Tempe, AZ 85287

21. School of Mathematics, Jilin University, Changchun, Jilin 130012, People’s Republic of China

22. Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, WA 98109

23. Emerging Pathogens Institute, University of Florida, Gainesville, FL 32610

24. Department of Population Health Sciences, Division of Epidemiology, Weill Cornell Medicine, Cornell University, New York, NY 10065

25. Computational Intelligence to Predict Health and Environmental Risks, University of North Carolina at Charlotte, Charlotte, NC 28223

26. Department of Emergency Medicine, Johns Hopkins University, Baltimore, MD 21209

27. One Health Trust, Washington, DC 20015

28. Department of Integrative Biology, The University of Texas at Austin, Austin, TX 78712

29. Department of Microbiology, School of Medicine, University of Washington, Seattle, WA 98195

30. Laboratory for the Modeling of Biological and Socio-technical Systems, Network Science Institute, Northeastern University, Boston, MA 02115

31. Operations Research and Industrial Engineering, The University of Texas at Austin, Austin, TX 78712

32. Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London WC1E 7HT, United Kingdom

33. Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London WC1E 7HT, United Kingdom

34. South African Department of Science and Innovation - National Research Foundation Centre of Excellence in Epidemiological Modelling and Analysis, Stellenbosch University, Stellenbosch, 7600 South Africa

35. Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX 78712

36. National Institute of Standards and Technology, Gaithersburg, MD 20899

37. School of Natural Resources and Environment, University of Florida, Gainesville, FL 32611

38. The 28th Research Institute of China Technology Group Corporation, Nanjing, Jiangsu 210023, People’s Republic of China

39. School of Agriculture and Environment, University of Western Australia, Perth, WA 6009, Australia

40. Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, CV4 7AL, United Kingdom

41. Centers for Disease Control and Prevention COVID-19 Response, Atlanta, GA 30329

42. Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, PA 15260

43. U.S. Geological Survey, Eastern Ecological Science Center, Laurel, MD 20708

Abstract

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.

Funder

National Science Foundation

Publisher

Proceedings of the National Academy of Sciences

Subject

Multidisciplinary

Link

https://pnas.org/doi/pdf/10.1073/pnas.2207537120

Reference39 articles.

1. Rational policymaking during a pandemic

2. Harnessing multiple models for outbreak management

3. Improving Pandemic Response: Employing Mathematical Modeling to Confront Coronavirus Disease 2019