An Impact-Oriented Approach to COVID-19 Epidemiological Modeling
Epidemiological modeling has emerged as a crucial tool to help decision-makers combat COVID-19, with calls for non-pharmaceutical interventions such as stay-at-home orders and the wearing of masks. But those models have become ubiquitous and part of the public lexicon — so Nirav Shah and Jason Wang write that they should follow an impact-oriented approach.
The COVID-19 pandemic has “propelled epidemiological modeling into the public and political consciousness, beyond the strict purview of scientific and public health experts,” note Stanford Health Policy’s Jason Wang and Nirav R. Shah of Stanford’s Clinical Excellence Research Center.
Many of these new models have emerged as crucial tools for decision-makers, they write in a joint viewpoint published in the Journal of General Internal Medicine. But they’ve also become the subject of public fixation and a mainstay of media headlines.
“Those who develop epidemiological models are no longer only creating specialty tools, but consumer products as well, and thus face a new, non-traditional, set of considerations,” they write. “We propose that this requires an impact-oriented approach, i.e., what is the cumulative impact of their models upon the public? We call this impact-oriented modeling.”
Wang and Shah, along with co-author Debbie Lai of COVID Act Now, lay out a set of eight key considerations for impact modeling, which they concede will not be easily met in totality but as many as possible should be incorporated into pandemic modeling.