Spontaneous social distancing in response to a simulated epidemic: a virtual experiment

Abstract

BackgroundStudies of social distancing during epidemics have found that the strength of the response can have a decisive impact on the outcome. In previous work we developed a model of social distancing driven by individuals’ risk attitude, a parameter which determines the extent to which social contacts are reduced in response to a given infection level. We showed by simulation that a strong response, driven by a highly cautious risk attitude, can quickly suppress an epidemic. However, a moderately cautious risk attitude gives weak control and, by prolonging the epidemic without reducing its impact, may yield a worse outcome than doing nothing. In real societies, social distancing may arise spontaneously from individual choices rather than being imposed centrally. There is little data available about this as opportunistic data collection during epidemics is difficult. Our study uses a simulated epidemic in a computer game setting to measure the social distancing response.MethodsTwo hundred thirty participants played a computer game simulating an epidemic on a spatial network. The player controls one individual in a population of 2500 (with others controlled by computer) and decides how many others to contact each day. To mimic real-world trade-offs, the player is motivated to make contact by being rewarded with points, while simultaneously being deterred by the threat of infection. Participants completed a questionnaire regarding psychological measures of health protection motivation. Finally, simulations were used to compare the experimentally-observed response to epidemics with no response.ResultsParticipants reduced contacts in response to infection in a manner consistent with our model of social distancing. The experimentally observed response was too weak to halt epidemics quickly, resulting in a somewhat reduced attack rate and a substantially reduced peak attack rate, but longer duration and fewer social contacts, compared to no response. Little correlation was observed between participants’ risk attitudes and the psychological measures.ConclusionsOur cognitive model of social distancing matches responses to a simulated epidemic. If these responses indicate real world behaviour, spontaneous social distancing can be expected to reduce peak attack rates. However, additional measures are needed if it is important to stop an epidemic quickly.

The impact of social distancing and epicenter lockdown on the COVID-19 epidemic in mainland China: A data-driven SEIQR model study

“…Our results showed that early intervention of social distancing could reduce the epidemic size The results of virtual experiments vary. Some research suggested social distancing is effective, under the condition of early activation and long-lasting implement of combined measures [8], strict implement [6,9], and spontaneously adopted [10]; some concluded the effectiveness was mild [11]; some argued that moderate social distancing can worsen the disease outcome, [6,9] But real-world studies are rare. Here we quantified the impact of social distancing on the epidemic size in the real world, providing evidence that social distancing is effective to interrupt the transmission of the respiratory pathogen, especially with early implementation and to a substantial extent.…”

Protection motivation theory and social distancing behaviour in response to a simulated infectious disease epidemic

“…Simulation results showed that the social distancing response was weak, tending to prolong the epidemic instead of stopping it (Kleczkowski et al, 2015). Analysis showed that social distancing behaviour was not significantly correlated with any of the PMT variables.…”

“…Initially, 150 randomly chosen individuals (6%) are infected, and the others are susceptible. Thereafter, the computer-controlled agents mirror the social distancing behaviour adopted by the participant (Kleczkowski et al, 2015). The game comes to an end when the participant becomes infected or when the epidemic ends, which happens when all infected individuals in the simulated population recover, or when a maximum number of periods is reached.…”

Detecting Flu Outbreaks Based on Spatiotemporal Information From Urban Systems – Designing a Novel Study

“…The progress of an outbreak affects the movement patterns of inhabitants. Some measures of population self-control can be expected to be applied (Kleczkowski, Maharaj, Rasmussen, Williams, & Cairns, 2015), making detailed pre-outbreak demographic data potentially outdated as outbreaks progress.…”