An epidemiological model with voluntary quarantine strategies governed by evolutionary game dynamics

Abstract

During pandemic events, strategies such as social distancing can be fundamental to reduce simultaneous infections and mitigate the disease spreading, which is very relevant to the risk of a healthcare system collapse. Although these strategies can be recommended, or even imposed, their actual implementation may depend on the population perception of the risks associated with a potential infection. The current COVID-19 crisis, for instance, is showing that some individuals are much more prone than others to remain isolated. To better understand these dynamics, we propose an epidemiological SIR model that uses evolutionary game theory for combining in a single process social strategies, individual risk perception, and viral spreading. In particular, we consider a disease spreading through a population, whose agents can choose between self-isolation and a lifestyle careless of any epidemic risk. The strategy adoption is individual and depends on the perceived disease risk compared to the quarantine cost. The game payoff governs the strategy adoption, while the epidemic process governs the agent’s health state. At the same time, the infection rate depends on the agent’s strategy while the perceived disease risk depends on the fraction of infected agents. Our results show recurrent infection waves, which are usually seen in previous historic epidemic scenarios with voluntary quarantine. In particular, such waves re-occur as the population reduces disease awareness. Notably, the risk perception is found to be fundamental for controlling the magnitude of the infection peak, while the final infection size is mainly dictated by the infection rates. Low awareness leads to a single and strong infection peak, while a greater disease risk leads to shorter, although more frequent, peaks. The proposed model spontaneously captures relevant aspects of a pandemic event, highlighting the fundamental role of social strategies.

Optimal governance and implementation of vaccination programmes to contain the COVID-19 pandemic

“…In the COVID-19 context, strategies will evolve as more information becomes available about the disease itself and about the vaccines. Different strategies will contest for primacy, and evolutionary game theory [50][51][52] can explicitly model this.…”

Modeling the Consequences of Social Distancing Over Epidemics Spreading in Complex Social Networks: From Link Removal Analysis to SARS-CoV-2 Prevention

“…SIR-type models have been recently used for SARS-CoV-2 [7,31]; we therefore decided to combine LR analyses and the SIR model. Nonetheless, other epidemiological models, such as the SEIRS [24], have been proposed to describe SARS-CoV-2 spreading.…”

“…The prominent role of networks in epidemiology has been recognized in last 2 decades [1][2][3][4][5][6][7]. In fact, a spreading disease can be modeled as a network where nodes represent the individuals (i.e., the hosts) and links (edges) represent the social contacts between the individuals.…”

Using Evolutionary Game Dynamics to Reproduce Phase Portrait Diversity in a Pandemic Scenario

“…Behavioral science is a way to study the pandemic response [23]. Along this line, various works appeared quite recently devoted to shed light in available COVID-19 data [24][25][26][27]. Amaral et al [24] extended Poletti's model to split the susceptible infectious stock into normal and altered.…”

“…Along this line, various works appeared quite recently devoted to shed light in available COVID-19 data [24][25][26][27]. Amaral et al [24] extended Poletti's model to split the susceptible infectious stock into normal and altered. In [25], a SEQIHR model (susceptible, exposed, quarantined, infected, hospitalized, recovered) with behavioral dynamics is studied.…”