Rxivist logo

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

By Karl Johnson, Caitlin Biddell, Kristen Hassmiller Lich, Julie Swann, Paul Delamater, Maria Mayorga, Julie Ivy, Raymond Smith, Mehul Patel

Posted 15 Apr 2021
medRxiv DOI: 10.1101/2021.04.13.21255401

Background: The COVID-19 Pandemic has popularized computer-based decision-support models as a tool for decision-makers to manage their organizations. It is unclear how decision-makers have considered these models to inform COVID-19-related decisions. Methods: We interviewed decision-makers from North Carolina across diverse organizational backgrounds to assess major decision-making processes during COVID-19, including the use of modeling as an input to inform decision-making. Results: Interviewees were aware of models during COVID-19, with some depending upon multiple models. Models were used to compare trends in disease spread across localities, allocate scarce resources, and track disease spread within small geographic areas. Decision-makers desired models to project disease spread within subpopulations and estimate where local outbreaks could occur as well as estimate the outcomes of social distancing policies, including consequences beyond typical health-related outcomes. Challenges to the use of modeling included doubts that models could reflect nuances of human behavior, concerns about the quality of data used in models, and the limited amount of modeling at the local level. Conclusions: Throughout COVID-19, decision-makers perceived modeling as valuable for understanding disease spread within their communities and to inform organization decisions, yet there were variations in organization ability and willingness to use models for these purposes.

Download data

  • Downloaded 227 times
  • Download rankings, all-time:
    • Site-wide: 125,537
    • In health policy: 317
  • Year to date:
    • Site-wide: 37,359
  • Since beginning of last month:
    • Site-wide: 52,178

Altmetric data


Downloads over time

Distribution of downloads per paper, site-wide


PanLingua

News