Coupling Epidemiological Models with Social Dynamics

Abstract

In this work we study a Susceptible-Infected-Susceptible model coupled with a continuous opinion dynamics model. We assume that each individual can take measures to reduce the probability of contagion, and the level of effort each agent applies can change due to social interactions. We propose simple rules to model the propagation of behaviors that modify the level of effort, and analyze their impact on the dynamics of the disease. We derive a two dimensional set of ordinary differential equations describing the dynamic of the proportion of the number of infected individuals and the mean value of the effort parameter, and analyze the equilibria of the system. The stability of the endemic phase and disease free equilibria depends only on the mean value of the levels of efforts, and not on the initial distribution of levels of effort.

On the optimal control of kinetic epidemic models with uncertain social features

“…In particular, it was shown that, for negative values of the parameter 𝛿, the resulting equilibrium contact distribution is given by a distribution with polynomial tails (17). On the other hand, slim tailed distributions can be obtained for positive values of 𝛿, see (15) and (16).…”

“…Special attention was recently paid by the scientific community to the role and the estimate of the distribution of contacts between individuals as also a relevant cause of the potential pathogen transmission 11‐13 . Nevertheless, we have often limited information on the real social features of a population, whose characteristics are structurally uncertain and may frequently change due to exogenous processes that are also influenced by psychological factors, determining different responses in terms of individuals' protective behavior, see for example, References 14 and 15.…”

Heterogeneous risk tolerance, in-groups, and epidemic waves

“…A variety of factors and perspectives are likely involved in feedback loops between disease dynamics and prophylaxis. Many of these have been modeled, including, for instance, fear of infection mediated by messages from social circles or mass media [10][11][12][13], social influence [5, 14,15], socioeconomic utility maximization [16][17][18], and evolutionary game theory [19][20][21][22][23]. Modeling such factors helps in understanding the determinants of the prophylactic responses of human populations to disease risk and is crucial for pre-or post-assessment of the effectiveness of causal interventions, including non-pharmaceutical interventions such as mask wearing, social distancing, and hand washing.…”

Kinetic Models for Epidemic Dynamics in the Presence of Opinion Polarization

“…Buonomo et al (2022) and the references therein. In this direction, we mention the recent results in Della Marca et al (2022b), Giambiagi Ferrari et al (2021), Kontorovsky et al (2022) and Zhou et al (2019) where agent-based dynamics are upscaled at the level of observable epidemiological quantities.…”