A stochastic model for the influence of social distancing on loneliness
Jos\'e F. Fontanari

TL;DR
This paper introduces a stochastic agent-based model to study how social distancing measures during Covid-19 can lead to a phase transition from healthy social interactions to a burnout regime characterized by diverging loneliness, highlighting mental health risks.
Contribution
The paper develops a novel stochastic model linking social distancing to loneliness dynamics and identifies a phase transition separating healthy and burnout regimes.
Findings
Community enters burnout regime with diverging loneliness
Phase transition between healthy and burnout states
Mean-field theory accurately describes community dynamics
Abstract
The short-term economic consequences of the critical measures employed to curb the transmission of Covid-19 are all too familiar, but the consequences of isolation and loneliness resulting from those measures on the mental well-being of the population and their ensuing long-term economic effects are largely unknown. Here we offer a stochastic agent-based model to investigate social restriction measures in a community where the feelings of loneliness of the agents dwindle when they are socializing and grow when they are alone. In addition, the intensity of those feelings, which are measured by a real variable that we term degree of loneliness, determines whether the agent will seek social contact or not. We find that decrease of the number, quality or duration of social contacts lead the community to enter a regime of burnout in which the degree of loneliness diverges, although the…
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