Abstract
The global pandemic of COVID-19 revealed the dynamic heterogeneity in how individuals respond to infection risks, government orders, and community-specific social norms. Here we demonstrate how both individual observation and social learning are likely to shape behavioral, and therefore epidemiological, dynamics over time. Efforts to delay and reduce infections can compromise their own success, especially when disease risk and social learning interact within sub-populations, as when people observe others who are (a) infected and/or (b) socially distancing to protect themselves from infection. Simulating socially-learning agents who observe effects of a contagious virus, our modelling results are consistent with with 2020 data on mask-wearing in the U.S. and also concur with general observations of cohort induced differences in reactions to public health recommendations. We show how shifting reliance on types of learning affect the course of an outbreak, and could therefore factor into policy-based interventions incorporating age-based cohort differences in response behavior.
Highlights
Over the course of 2020, the numbers of COVID-19 cases rose, fell, and re-surged in many Western nations
We take the approach of discrete behavioral choice with social influence [33,34,35,36], where we model decisions as based on a separable combination of two components: observational and social learning
We focused on public health measures that could be quickly adopted, in the initial case where sweeping lockdowns are not politically feasible
Summary
Over the course of 2020, the numbers of COVID-19 cases rose, fell, and re-surged in many Western nations. Data: N: number of of agents, tstep: number of time steps, Pð~bÞ: vector with the probability of infection for different behaviors, b d: the distance between two agents under which the disease can be transmitted, i0: the number of initial infections, p: the probability of observational learning, r: radius within which individual can learn socially κ: transparency (steepness of the sigmoid), ν: inflexion point of the sigmoid, κr: steepness of the reversion sigmoid, νr: inflexion point of the reversion sigmoid, Result: A table with the SIR distribution per timestep
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
