Modeling Infectious Behaviors
The Need to Account for Behavioral Adaptation in COVID-19 Models
Published in: Journal on Policy and Complex Systems, Volume 7, Number 1, pages 21–32 (Spring 2021). doi: 10.18278/jpcs.7.1.3
Posted on RAND.org on October 20, 2021
The current COVID-19 pandemic affects billions of people worldwide, and its unprecedented scale and duration are causing extraordinary disruptions to lives and livelihoods. Policymakers have taken comparably extraordinary measures to mitigate the spread of the virus (SARS-CoV-2) by implementing a range of nonpharmaceutical public health interventions (NPIs), from partial closings of business to complete lockdown and mask-wearing (Aledort et al., 2007). NPIs continue to be critically important as new vaccines roll out in an attempt to reach herd immunity as quickly as possible. Several models have been developed to help policymakers compare interventions such as NPIs and vaccination. Such models attempt—dependent on various uncertain assumptions—to forecast cases, deaths, and medical supply needs; predict the timing of peaks in cases; and estimate if and when to expect additional waves or surges. However, a key limitation of many existing models is that they do not directly integrate adaptive behavioral components to account for how risk perceptions, protective behaviors, and compliance with interventions change over time and ultimately influence transmission. Patterns of COVID-19 transmission shape the subsequent patterns of behavioral responses to the disease and, in turn, are shaped by such responses. Changes in risk perceptions during the pandemic affect behaviors, including adherence to NPIs and willingness to vaccinate. Also, the effects that NPI shave on a population activities have led to pandemic fatigue and a decline in compliance with recommended restrictions (Alagoz et al., 2020; Craneet al., 2021; Kantor and Kantor, 2020). Moreover, perceptions of the risks and benefits of vaccination change, increasing or decreasing the vaccine hesitancy that undermines attempts to reach herd immunity (Yaqub et al., 2014). Decision-makers are faced with the daunting task of interpreting model predictions while simultaneously estimating how the behavioral responses should alter predictions. Despite considerable uncertainties, it may be possible to improve these estimates by explicitly modeling behavioral responses to intervention, rather than merely varying parameters such as willingness to be vaccinated. Useful explicit modeling will require tapping new sources of data as we recommend in this article.