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Where Do We Go From Here? A Framework for Using Susceptible-Infectious-Recovered Models for Policy Making in Emerging Infectious Diseases.
Zawadzki, Roy S; Gong, Cynthia L; Cho, Sang K; Schnitzer, Jan E; Zawadzki, Nadine K; Hay, Joel W; Drabo, Emmanuel F.
  • Zawadzki RS; Department of Statistics, Donald Bren School of Information and Computer Sciences, University of California, Irvine, CA, USA.
  • Gong CL; Fetal and Neonatal Institute, Division of Neonatology, Department of Pediatrics, Children's Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. Electronic address: gongc@usc.edu.
  • Cho SK; College of Pharmacy, University of Houston, Houston, TX, USA.
  • Schnitzer JE; Proteogenomics Research Institute for Systems Medicine (PRISM), San Diego, CA, USA.
  • Zawadzki NK; Schaeffer Center for Health Policy & Economics, School of Pharmacy, University of Southern California, Los Angeles, CA, USA.
  • Hay JW; Schaeffer Center for Health Policy & Economics, School of Pharmacy, University of Southern California, Los Angeles, CA, USA.
  • Drabo EF; Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
Value Health ; 24(7): 917-924, 2021 07.
Article in English | MEDLINE | ID: covidwho-1233520
ABSTRACT

OBJECTIVES:

Throughout the coronavirus disease 2019 pandemic, susceptible-infectious-recovered (SIR) modeling has been the preeminent modeling method to inform policy making worldwide. Nevertheless, the usefulness of such models has been subject to controversy. An evolution in the epidemiological modeling field is urgently needed, beginning with an agreed-upon set of modeling standards for policy recommendations. The objective of this article is to propose a set of modeling standards to support policy decision making.

METHODS:

We identify and describe 5 broad standards transparency, heterogeneity, calibration and validation, cost-benefit analysis, and model obsolescence and recalibration. We give methodological recommendations and provide examples in the literature that employ these standards well. We also develop and demonstrate a modeling practices checklist using existing coronavirus disease 2019 literature that can be employed by readers, authors, and reviewers to evaluate and compare policy modeling literature along our formulated standards.

RESULTS:

We graded 16 articles using our checklist. On average, the articles met 6.81 of our 19 categories (36.7%). No articles contained any cost-benefit analyses and few were adequately transparent.

CONCLUSIONS:

There is significant room for improvement in modeling pandemic policy. Issues often arise from a lack of transparency, poor modeling assumptions, lack of a system-wide perspective in modeling, and lack of flexibility in the academic system to rapidly iterate modeling as new information becomes available. In anticipation of future challenges, we encourage the modeling community at large to contribute toward the refinement and consensus of a shared set of standards for infectious disease policy modeling.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Epidemiologic Methods / Communicable Diseases, Emerging Type of study: Experimental Studies / Prognostic study Limits: Humans Language: English Journal: Value Health Journal subject: Pharmacology Year: 2021 Document Type: Article Affiliation country: J.jval.2021.03.005

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Epidemiologic Methods / Communicable Diseases, Emerging Type of study: Experimental Studies / Prognostic study Limits: Humans Language: English Journal: Value Health Journal subject: Pharmacology Year: 2021 Document Type: Article Affiliation country: J.jval.2021.03.005