An Impact-Oriented Approach to Epidemiological Modeling
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Clinical Excellence Research Center, Stanford University, Stanford, CA, USA; 2Covid Act Now, Walnut, CA, USA; 3Center for Policy, Outcomes and Prevention, Stanford University, Stanford, CA, USA.
J Gen Intern Med DOI: 10.1007/s11606-020-06230-1 © Society of General Internal Medicine 2020
COVID-19 pandemic has propelled epidemiological T hemodeling into the public and political consciousness, beyond the strict purview of scientific and public health experts. Models have emerged as crucial tools for decisionmakers, with calls for government-mandated non-pharmaceutical interventions (NPIs) such as stay-at-home orders to be based on data-driven thresholds such as case numbers and transmission rates.1 And it goes both ways: data drives use of NPIs, which then affect models in an iterative process. Meanwhile, the outputs of COVID-19 models have become a subject of public fixation and mainstay of media headlines. There is a growing body of evidence supporting the efficacy of NPIs such as shelter-in-place and mask-wearing, which are affected by the extent of the public’s buy-in and compliance. Studies have shown that NPIs averted a 67× increase in cases in China by February 29, 2020, and even lax compliance can reduce transmission by as much as 25%.2 Other studies suggest that suppression will minimally require social distancing by the entire population.3 Under such circumstances, public awareness and consensus become paramount, particularly in the USA, where societal and cultural norms may limit imposed lockdowns akin to those that occurred in Wuhan and other parts of China. Thus, there emerges an unprecedented need to build a shared understanding of the disease, not just among experts and policymakers but also for the public. 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. 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.
Received June 24, 2020 Accepted September 9, 2020
Traditionally, epidemiological models have been valued for their ability to inform decision-makers who possess prior knowledge of disease management.4 In the wake of the H1N1 pandemic in 2009, the World Health Organization (WHO) convened a mathematical modeling network of public health experts and academics.5 The Centers for Disease Control and Prevention (CDC) recently added policy development as a sixth item in its list of the major tasks of epidemiology in public health, but there remains no mention of the impact on the general public.6 Impact-oriented modeling values more than accuracy, which remains non-negotiable. Beyond simply the outputs of such a model, consideration must be given to the presentation of these outputs, including design, visualization, and supporting content, all of which affect the utility, user experience, downstream policy, and, ultimately, impact. To this end, we outline a set of 8
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