Modeling Behavioral Responses to COVID-19

Abstract

Many models have been developed to forecast the spread of the COVID-19 virus. We present one that is enhanced to allow individuals to alter their behavior in response to the virus. We show how adding this feature to the model both changes the resulting forecast and informs our understanding of the appropriate policy response. We find that when left to their own devices, individuals do curb their social activity in the face of risk, but not as much as a government planner would. The planner fully internalizes the effect of all individuals’ actions on others in society, while individuals do not. Further, our simulations suggest that government intervention may be particularly important in the middle and later stages of a pandemic.

Autonomous and policy-induced behavior change during the COVID-19 pandemic: Towards understanding and modeling the interplay of behavioral adaptation

“…The support of policy decisions can be considered the primary use for behavioral-epidemiological models [ 144 ], which require adequate assumptions for counterfactual scenarios. As Craig, Phelan and Siedlarek [ 139 ] point out, any narrative such as “strategy X would have saved more lives” is built on (implicit) behavioral assumptions. If that assumption is that no autonomous adaptation occurs, the counterfactual to compare interventions against may assume substantial exponential growth of infections [e.g., 145 , 146 ] and thus overestimate the role of interventions based on observed data [ 8 , 9 ].…”

“…Likewise, well established differences within or between populations, for instance with respect to risk perception [ 34 , 150 ], may be considered and represented. When the mechanisms driving behavioral adaptation or the change in contact rate are understood better, different response strategies may be evaluated more accurately with respect to their effectiveness at disease prevention and their costs [ 139 ].…”

“…It is promising that an increasing number of models includes the effects of both autonomous and policy-induced behavioral adaptation [11,16,[138][139][140]. Here, the key question is how the two effects are set in relation to one another.…”

Autonomous and policy-induced behavior change during the COVID-19 pandemic: Towards understanding and modeling the interplay of behavioral adaptation

“…As we expound further below, however, it is of key interest whether a change in behavior is the result of policy, of autonomous adaptation or both. Thus, it is promising that an increasing number of models includes the effects of both autonomous and policy-induced behavioral adaptation [11,16,[135][136][137]. Here, the key question is how the two effects are set in relation to one another.…”

“…The support of policy decisions can be considered the primary use for behavioralepidemiological models [141], which require adequate assumptions for counterfactual scenarios. As [136] point out, any narrative such as "strategy X would have saved more lives" is built on (implicit) behavioral assumptions. If that assumption is that no autonomous adaptation occurs, the counterfactual to compare interventions against may assume substantial exponential growth of infections [e.g., 142,143] and thus overestimate the role of interventions based on observed data [8,9].…”