Asymptomatic individuals can increase the final epidemic size under adaptive human behavior

Abstract

Infections produced by non-symptomatic (pre-symptomatic and asymptomatic) individuals have been identified as major drivers of COVID-19 transmission. Non-symptomatic individuals, unaware of the infection risk they pose to others, may perceive themselves—and be perceived by others—as not presenting a risk of infection. Yet, many epidemiological models currently in use do not include a behavioral component, and do not address the potential consequences of risk misperception. To study the impact of behavioral adaptations to the perceived infection risk, we use a mathematical model that incorporates the behavioral decisions of individuals, based on a projection of the system’s future state over a finite planning horizon. We found that individuals’ risk misperception in the presence of non-symptomatic individuals may increase or reduce the final epidemic size. Moreover, under behavioral response the impact of non-symptomatic infections is modulated by symptomatic individuals’ behavior. Finally, we found that there is an optimal planning horizon that minimizes the final epidemic size.

Heterogeneous adaptive behavioral responses may increase epidemic burden

“…Finally, we incorporate the role of uncertain information on the decision-making process. We let the perceived health status represent a source of information uncertainty, where non-symptomatic individuals (exposed and asymptomatic), unaware of the infection risk they pose to others, may perceive themselves—and be perceived by others—as not presenting a risk of infection 25 .…”

“…We model the susceptible individuals’ daily optimal contact choice problem as a dynamic programming problem, the solution to which generates the privately optimal contact rate 22 – 25 . Note that, regardless of the behavioral group, individuals follows a SEIAR disease progression across health states.…”

“…Diverse modeling frameworks have been used to characterize the intertwined dynamics generated by the disease dynamics and the set of behavioral responses available to mitigate disease spread 3 – 5 , 22 – 25 . However, often, the mathematical models do not consider the adaptive nature of the co-evolving epidemic and adaptive behavioral dynamics—in other words, most current behavioral models in epidemiology look only at the current and past system’s state, and use that information to infer individual behavioral responses.…”

“…During an epidemic, the feedback loop between people’s activities to avoid risk of infection and the desire to maintain social interactions and economic productivity creates a complex adaptive system, in which behavioral responses both drive and are driven by the disease transmission process. Recently, the modeling framework proposed by Fenichel et al 22 – 25 envisions individual behavioral adaptations as a Markov decision process by using an epidemiological-economic model. Markov decision processes have found applications and provided foundations in AI for modeling sequential decision-making in diverse contexts 26 – 28 .…”

“…We extend the behavioral modeling framework by Fenichel et al 22 – 25 by: ( i ) coupling a set of Markov decision processes (MDPs) to incorporate differential adaptive behavioral responses; ( ii ) using a more complex epidemic model of disease transmission; and ( iii ) including the role of uncertain information about individual health states, affecting the decision-making processes. Individuals are divided into two classes based on their risk-acceptance behavior.…”