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

Kleczkowski A, Maharaj S, Rasmussen S, Williams L, Cairns N - BMC Public Health (2015)

Bottom Line: Studies of social distancing during epidemics have found that the strength of the response can have a decisive impact on the outcome.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.If these responses indicate real world behaviour, spontaneous social distancing can be expected to reduce peak attack rates.

View Article: PubMed Central - PubMed

Affiliation: Computing Science and Mathematics, School of Natural Sciences, University of Stirling, Stirling, UK. ak@cs.stir.ac.uk.

ABSTRACT

Background: Studies 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.

Methods: Two 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.

Results: Participants 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.

Conclusions: Our 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.

No MeSH data available.


Related in: MedlinePlus

Local sensitivity of outcomes to probability of recovery. The results of simulations with a population behaving according to Model A, with epidemic and spatial parameters as in the experiments, except that q is varied in a narrow range [015,0.25] around the experimental value (q = 0.20, indicated by a vertical line in the figures). Each black point represents the mean of 100 replicates, with error bars representing ± one standard deviation. a shows the attack rate. b shows the peak attack rate. c shows the duration. d shows the overall level of social contact during 1000 time steps. It is assumed that all individuals resume full social contact once the epidemic is over
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Fig9: Local sensitivity of outcomes to probability of recovery. The results of simulations with a population behaving according to Model A, with epidemic and spatial parameters as in the experiments, except that q is varied in a narrow range [015,0.25] around the experimental value (q = 0.20, indicated by a vertical line in the figures). Each black point represents the mean of 100 replicates, with error bars representing ± one standard deviation. a shows the attack rate. b shows the peak attack rate. c shows the duration. d shows the overall level of social contact during 1000 time steps. It is assumed that all individuals resume full social contact once the epidemic is over

Mentions: Local sensitivity analysis was performed to explore the effect of slightly varying the values of p and q used in the simulations. The results are shown in Figs. 8 and 9. Small increases in p lead to increases in the attack rate, peak attack rate, and number of contacts, and a reduction in the duration. Small increases in q lead to reductions in the attack rate, peak attack rate and duration, and an increase in the number of contacts.Fig. 8


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

Kleczkowski A, Maharaj S, Rasmussen S, Williams L, Cairns N - BMC Public Health (2015)

Local sensitivity of outcomes to probability of recovery. The results of simulations with a population behaving according to Model A, with epidemic and spatial parameters as in the experiments, except that q is varied in a narrow range [015,0.25] around the experimental value (q = 0.20, indicated by a vertical line in the figures). Each black point represents the mean of 100 replicates, with error bars representing ± one standard deviation. a shows the attack rate. b shows the peak attack rate. c shows the duration. d shows the overall level of social contact during 1000 time steps. It is assumed that all individuals resume full social contact once the epidemic is over
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4587782&req=5

Fig9: Local sensitivity of outcomes to probability of recovery. The results of simulations with a population behaving according to Model A, with epidemic and spatial parameters as in the experiments, except that q is varied in a narrow range [015,0.25] around the experimental value (q = 0.20, indicated by a vertical line in the figures). Each black point represents the mean of 100 replicates, with error bars representing ± one standard deviation. a shows the attack rate. b shows the peak attack rate. c shows the duration. d shows the overall level of social contact during 1000 time steps. It is assumed that all individuals resume full social contact once the epidemic is over
Mentions: Local sensitivity analysis was performed to explore the effect of slightly varying the values of p and q used in the simulations. The results are shown in Figs. 8 and 9. Small increases in p lead to increases in the attack rate, peak attack rate, and number of contacts, and a reduction in the duration. Small increases in q lead to reductions in the attack rate, peak attack rate and duration, and an increase in the number of contacts.Fig. 8

Bottom Line: Studies of social distancing during epidemics have found that the strength of the response can have a decisive impact on the outcome.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.If these responses indicate real world behaviour, spontaneous social distancing can be expected to reduce peak attack rates.

View Article: PubMed Central - PubMed

Affiliation: Computing Science and Mathematics, School of Natural Sciences, University of Stirling, Stirling, UK. ak@cs.stir.ac.uk.

ABSTRACT

Background: Studies 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.

Methods: Two 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.

Results: Participants 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.

Conclusions: Our 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.

No MeSH data available.


Related in: MedlinePlus