Use of Modeling to Inform Decision Making in North Carolina During the COVID-19 Pandemic: A Qualitative Study

Overview

Journal MDM Policy Pract

Publisher Sage Publications

Specialty General Medicine

Date 2022 Aug 4

PMID 35923388

Authors

Karl Johnson

Caitlin B Biddell

Kristen Hassmiller Lich

Julie Swann

Paul Delamater

Maria Mayorga

Julie Ivy

Raymond L Smith

Mehul D Patel

Affiliations

Soon will be listed here.

Abstract

Highlights: Organizations from a diversity of sectors across North Carolina (including public health, education, business, government, religion, and public safety) have used decision-support modeling to inform decision making during COVID-19.Decision makers wish for models to project the spread of disease, especially at the local level (e.g., individual cities and counties), and to help estimate the outcomes of policies.Some organizational decision makers are hesitant to use modeling to inform their decisions, stemming from doubts that models could reflect nuances of human behavior, concerns about the accuracy and precision of data used in models, and the limited amount of modeling available at the local level.

Citing Articles

Do COVID-19 Infectious Disease Models Incorporate the Social Determinants of Health? A Systematic Review.

John-Baptiste A, Moulin M, Li Z, Hamilton D, Crichlow G, Eisenkraft Klein D Public Health Rev. 2024; 45:1607057.

PMID: 39450316 PMC: 11499127. DOI: 10.3389/phrs.2024.1607057.

Incorporating Social Determinants of Health in Infectious Disease Models: A Systematic Review of Guidelines.

Ali S, Li Z, Moqueet N, Moghadas S, Galvani A, Cooper L Med Decis Making. 2024; 44(7):742-755.

PMID: 39305116 PMC: 11491037. DOI: 10.1177/0272989X241280611.

References

Badr H, Gardner L . Limitations of using mobile phone data to model COVID-19 transmission in the USA. Lancet Infect Dis. 2020; 21(5):e113. PMC: 7837090. DOI: 10.1016/S1473-3099(20)30861-6. View

Biddell C, Johnson K, Patel M, Smith R, Hecht H, Swann J . Cross-sector decision landscape in response to COVID-19: A qualitative network mapping analysis of North Carolina decision-makers. Front Public Health. 2022; 10:906602. PMC: 9424900. DOI: 10.3389/fpubh.2022.906602. View

Jewell N, Lewnard J, Jewell B . Predictive Mathematical Models of the COVID-19 Pandemic: Underlying Principles and Value of Projections. JAMA. 2020; 323(19):1893-1894. DOI: 10.1001/jama.2020.6585. View

Wise J . Covid-19: Risk of second wave is very real, say researchers. BMJ. 2020; 369:m2294. DOI: 10.1136/bmj.m2294. View

Callaway E, Ledford H, Viglione G, Watson T, Witze A . COVID and 2020: An extraordinary year for science. Nature. 2020; 588(7839):550-552. DOI: 10.1038/d41586-020-03437-4. View

Siegenfeld A, Taleb N, Bar-Yam Y . Opinion: What models can and cannot tell us about COVID-19. Proc Natl Acad Sci U S A. 2020; 117(28):16092-16095. PMC: 7368306. DOI: 10.1073/pnas.2011542117. View

Adam D . Special report: The simulations driving the world's response to COVID-19. Nature. 2020; 580(7803):316-318. DOI: 10.1038/d41586-020-01003-6. View

Comfort L, Kapucu N, Ko K, Menoni S, Siciliano M . Crisis Decision-Making on a Global Scale: Transition from Cognition to Collective Action under Threat of COVID-19. Public Adm Rev. 2020; 80(4):616-622. PMC: 7300963. DOI: 10.1111/puar.13252. View

Gandhi R, Lynch J, Del Rio C . Mild or Moderate Covid-19. N Engl J Med. 2020; 383(18):1757-1766. DOI: 10.1056/NEJMcp2009249. View

10.

Vespignani A, Tian H, Dye C, Lloyd-Smith J, Eggo R, Shrestha M . Modelling COVID-19. Nat Rev Phys. 2021; 2(6):279-281. PMC: 7201389. DOI: 10.1038/s42254-020-0178-4. View

11.

Kinsella C, Santos P, Postigo-Hidalgo I, Folgueiras-Gonzalez A, Passchier T, Szillat K . Preparedness needs research: How fundamental science and international collaboration accelerated the response to COVID-19. PLoS Pathog. 2020; 16(10):e1008902. PMC: 7546461. DOI: 10.1371/journal.ppat.1008902. View

12.

Tong A, Sainsbury P, Craig J . Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care. 2007; 19(6):349-57. DOI: 10.1093/intqhc/mzm042. View

13.

Seetharaman P . Business models shifts: Impact of Covid-19. Int J Inf Manage. 2020; 54:102173. PMC: 7323683. DOI: 10.1016/j.ijinfomgt.2020.102173. View

14.

. Modeling COVID-19 scenarios for the United States. Nat Med. 2020; 27(1):94-105. PMC: 7806509. DOI: 10.1038/s41591-020-1132-9. View

15.

Roberts M, Russell L, Paltiel A, Chambers M, McEwan P, Krahn M . Conceptualizing a model: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-2. Med Decis Making. 2012; 32(5):678-89. DOI: 10.1177/0272989X12454941. View

16.

James L, Salomon J, Buckee C, Menzies N . The Use and Misuse of Mathematical Modeling for Infectious Disease Policymaking: Lessons for the COVID-19 Pandemic. Med Decis Making. 2021; 41(4):379-385. PMC: 7862917. DOI: 10.1177/0272989X21990391. View

17.

Hsieh H, Shannon S . Three approaches to qualitative content analysis. Qual Health Res. 2005; 15(9):1277-88. DOI: 10.1177/1049732305276687. View

18.

Nosyk B, Weiner J, Krebs E, Zang X, Enns B, Behrends C . Dissemination Science to Advance the Use of Simulation Modeling: Our Obligation Moving Forward. Med Decis Making. 2020; 40(6):718-721. PMC: 7484337. DOI: 10.1177/0272989X20945308. View