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Infectious Disease Modeling: Recommendations for Public Health Decision-Makers

Published online by Cambridge University Press:  02 June 2022

Scott W. Olesen Open the ORCID record for Scott W. Olesen [Opens in a new window]  and
Eric Trabert
Scott W. Olesen
Affiliation:
Center for Public Health Preparedness and Resilience, Institute for Public Research, CNA, Arlington, Virginia, USA
Eric Trabert*
Affiliation:
Center for Public Health Preparedness and Resilience, Institute for Public Research, CNA, Arlington, Virginia, USA
*
Corresponding author: Eric Trabert, Email: [email protected].
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Abstract

Infectious disease modeling plays an important role in the response to infectious disease outbreaks, perhaps most notably during the coronavirus disease 2019 (COVID-19) pandemic. In our experience working with state and local governments during COVID-19 and previous public health crises, we have observed that, while the scientific literature focuses on models’ accuracy and underlying assumptions, an important limitation on the effective application of modeling to public health decision-making is the ability of decision-makers and modelers to work together productively. We therefore propose a set of guiding principles, informed by our experience, for working relationships between decision-makers and modelers. We hypothesize that these guidelines will improve the utility of infectious disease modeling for public health decision-making, irrespective of the particular outbreak in question and of the precise modeling approaches being used.

Keywords

epidemiological modelsdecision-makingCOVID-19
Type
Report from the Field
Information
Disaster Medicine and Public Health Preparedness , Volume 16 , Issue 6 , December 2022 , pp. 2686 - 2688
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of Society for Disaster Medicine and Public Health, Inc.

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References

Ray, EL, Wattanachit, N, Niemi, J, et al. Ensemble forecasts of coronavirus disease 2019 (COVID-19) in the U.S. MedRxiv. 2020. doi: 10.1101/2020.08.19.20177493 Google Scholar
Becker, AD, Grantz, KH, Hegde, ST, et al. Development and dissemination of infectious disease dynamic transmission models during the COVID-19 pandemic: what can we learn from other pathogens and how can we move forward? Lancet Digit Health. 2021;3(1):e41-e50. doi: 10.1016/s2589-7500(20)30268-5 CrossRefGoogle ScholarPubMed
Jewell, NP, Lewnard, JA, Jewell, BL. Caution warranted: using the institute for health metrics and evaluation model for predicting the course of the COVID-19 Pandemic. Ann Intern Med. 2020;173(3):226-227. doi: 10.7326/M20-1565 Google ScholarPubMed
Jewell, NP, Lewnard, JA, Jewell, BL. Predictive mathematical models of the COVID-19 Pandemic. JAMA. 2020;323(19):1893-1894. doi: 10.1001/jama.2020.6585 CrossRefGoogle ScholarPubMed
US Government Accountability Office. Infectious disease modeling: opportunities to improve coordination and ensure reproducibility. 2020. Accessed May 10, 2022. https://www.gao.gov/products/gao-20-372 Google Scholar
Rivers, C, Chretien, JP, Riley, S, et al. Using “outbreak science” to strengthen the use of models during epidemics. Nat Commun. 2019;10(1):3102. doi: 10.1038/s41467-019-11067-2 CrossRefGoogle Scholar
World Health Organization. Guidance for country-level TB modelling. 2018. Accessed February 13, 2022. https://apps.who.int/iris/handle/10665/274279 Google Scholar
Muscatello, DJ, Chughtai, AA, Heywood, A, et al. Translation of real-time infectious disease modeling into routine public health practice. Emerg Infect Dis. 2017;23(5):e161720. doi: 10.3201/eid2305.161720 CrossRefGoogle ScholarPubMed
Doms, C, Kramer, SC, Shaman, J. Assessing the use of influenza forecasts and epidemiological modeling in public health decision making in the United States. Sci Rep. 2018;8(1):12406. doi: 10.1038/s41598-018-30378-w CrossRefGoogle ScholarPubMed
Prieto, DM, Das, TK, Savachkin, AA, et al. A systematic review to identify areas of enhancements of pandemic simulation models for operational use at provincial and local levels. BMC Public Health. 2012;12(1):251. doi: 10.1186/1471-2458-12-251 CrossRefGoogle ScholarPubMed
Rosenfeld, LA, Fox, CE, Kerr, D, et al. Use of computer modeling for emergency preparedness functions by local and state health officials: a needs assessment. J Public Health Manag Pract. 2009;15(2):96-104. doi: 10.1097/01.PHH.0000346004.21157.ef Google ScholarPubMed
US Centers for Disease Control and Prevention. CDC stands up new disease forecasting center. Published online August 18, 2021. Accessed May 10, 2022. https://www.cdc.gov/media/releases/2021/p0818-disease-forecasting-center.html Google Scholar