Improving pandemic mitigation policies across communities through coupled dynamics of risk perception and infection

Abstract

Decisions to adhere to health-protective behaviors (e.g. mask-wearing, social distancing, etc.) that impact the spread of COVID-19 are not made in isolation by each individual. They are instead the result of the social construction of perceived risks and resulting community norms. In populations in which disease is unlikely to spread throughout all communities simultaneously, community-driven perception of risk can drastically alter collective outcomes. A community could respond to a few infections by becoming fearful and adopting anticipatory behaviors that protect them from disease spread. Similarly, there could be false reassurance, in which low disease incidence over time leads to community consensus that protective actions are unnecessary (even if they are the reason cases remains rare). We therefore model COVID-19 spread with three synergistic dynamics governing individual behavioral choices: (1) Social construction of concern, (2) Awareness of disease incidence, and (3) Reassurance by lack of disease. We use a multiplex network approach that captures social communication and epidemiological spread. We find that effective protective policies enacted too early may backfire by allowing a community to become reassured and therefore unwilling to adopt or maintain further protective behaviors. Based on these insights, we propose that public health policies for which success relies on collective action should be designed to exploit the behaviourally receptive phase; the period between the generation of sufficient concern as to foster adoption of novel protective behaviors and the relaxation of adherence driven by reassurance fostered by avoidance of negative outcomes over time.

Where to learn to flatten the curve: a modelling study

“…All modelling was conducted in R3.6.1 [19] and the code used is provided on GitHub (https://github.com/matthewsilk/CoupledDynamics2_layeruse). The general modelling framework was the same as that used by Silk et al [20] and is described in that paper and in the Supplementary Material.…”

“…We used the same 9 multiplex social networks as detailed in Silk et al [20] . These were coupled, multiplex networks that connected all individuals within a communication layer that influenced the spread of concern about the disease and an infection layer that influenced the transmission of the pathogen itself.…”

“…We used the same concern model as Silk et al [20] . Concern about the disease was modelled as a complex contagion [21] through the communication layer.…”

“…For each simulation run we simulated a time period of 300 days. The simulation algorithm was the same used in Silk et al [20] and is detailed in the Supplementary Material.…”

Homophily in risk and behavior complicate understanding the COVID-19 epidemic curve

“…Beyond mixing changes due to the holidays or weather-related behavior, there are also likely ongoing gradual changes in local adoption rates of mask wearing or social distancing, especially as local case incidence created greater local awareness of potential disease severity than may have been believed before direct observable outcomes due to differences in national reporting and media consumption (14)(15)(16)(17). These regional differences in behavior lead to differences in community vulnerability, causing a potential feedback loop between behavior and local outbreak severity (18). Certainly, there have been some areas of the country that have experienced such high prevalence that the number of individuals with natural immunity after recovery should now begin to slow transmission (19).…”