Model-based evaluation of the impact of noncompliance with public health measures on COVID-19 disease control

Abstract

The word ‘pandemic’ conjures dystopian images of bodies stacked in the streets and societies on the brink of collapse. Despite this frightening picture, denialism and noncompliance with public health measures are common in the historical record, for example during the 1918 Influenza pandemic or the 2015 Ebola epidemic. The unique characteristics of SARS-CoV-2—its high reproductive number (R₀), time-limited natural immunity and considerable potential for asymptomatic spread—exacerbate the public health repercussions of noncompliance with biomedical and nonpharmaceutical interventions designed to limit disease transmission. In this work, we used game theory to explore when noncompliance confers a perceived benefit to individuals, demonstrating that noncompliance is a Nash equilibrium under a broad set of conditions. We then used epidemiological modeling to explore the impact of noncompliance on short-term disease control, demonstrating that the presence of a noncompliant subpopulation prevents suppression of disease spread. Our modeling shows that the existence of a noncompliant population can also prevent any return to normalcy over the long run. For interventions that are highly effective at preventing disease spread, however, the consequences of noncompliance are borne disproportionately by noncompliant individuals. In sum, our work demonstrates the limits of free-market approaches to compliance with disease control measures during a pandemic. The act of noncompliance with disease intervention measures creates a negative externality, rendering COVID-19 disease control ineffective in the short term and making complete suppression impossible in the long term. Our work underscores the importance of developing effective strategies for prophylaxis through public health measures aimed at complete suppression and the need to focus on compliance at a population level.