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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

Model network (a) compared to the game user interface (b). a shows a part of the square lattice spatial network with the player-controlled susceptible individual (black circle) in the centre. The player’s awareness neighbourhood (outer, dark blue circle, radius 4) contains infected neighbours (red circles) and non-infected neighbours (green circles). Nodes outside this circle contain individuals that are not visible to the player, but which may transmit infection into the player’s awareness neighbourhood. The inner, light blue circle shows the player’s chosen infection radius, ri,t. Here, ri,t = 1.5 and the player will contact 8 neighbours (bold-outlined circles). b shows the same scenario as displayed to the player via the game interface. The player controls the size of the infection radius by making the blue circle larger or smaller. Neighbours remain in fixed locations in the underlying lattice, but are displayed in the game interface as animated figures that move around rapidly. This prevents players from wrongly thinking that they can know the exact location of their infected neighbours and attempting to adjust the circle to exclude those locations
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Fig2: Model network (a) compared to the game user interface (b). a shows a part of the square lattice spatial network with the player-controlled susceptible individual (black circle) in the centre. The player’s awareness neighbourhood (outer, dark blue circle, radius 4) contains infected neighbours (red circles) and non-infected neighbours (green circles). Nodes outside this circle contain individuals that are not visible to the player, but which may transmit infection into the player’s awareness neighbourhood. The inner, light blue circle shows the player’s chosen infection radius, ri,t. Here, ri,t = 1.5 and the player will contact 8 neighbours (bold-outlined circles). b shows the same scenario as displayed to the player via the game interface. The player controls the size of the infection radius by making the blue circle larger or smaller. Neighbours remain in fixed locations in the underlying lattice, but are displayed in the game interface as animated figures that move around rapidly. This prevents players from wrongly thinking that they can know the exact location of their infected neighbours and attempting to adjust the circle to exclude those locations

Mentions: Disease transmission and recovery are modelled using the well-known Susceptible-Infected-Recovered (SIR) model [27], adapted to deal with discrete individuals and discrete time. The epidemic model is closely linked with the spatial model, see Figs. 1 and 2. Initially, a fraction I0 of the population are infected and the rest are susceptible. At each time step, each individual makes contact with others; the details of which individuals can contact each other are left to the explanation of the spatial model (below). Each contact between a susceptible and an infected individual may cause the susceptible to become infected, with probability p. At each time step, infected individuals may recover, with probability q, after which they remain immune.Fig. 1


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)

Model network (a) compared to the game user interface (b). a shows a part of the square lattice spatial network with the player-controlled susceptible individual (black circle) in the centre. The player’s awareness neighbourhood (outer, dark blue circle, radius 4) contains infected neighbours (red circles) and non-infected neighbours (green circles). Nodes outside this circle contain individuals that are not visible to the player, but which may transmit infection into the player’s awareness neighbourhood. The inner, light blue circle shows the player’s chosen infection radius, ri,t. Here, ri,t = 1.5 and the player will contact 8 neighbours (bold-outlined circles). b shows the same scenario as displayed to the player via the game interface. The player controls the size of the infection radius by making the blue circle larger or smaller. Neighbours remain in fixed locations in the underlying lattice, but are displayed in the game interface as animated figures that move around rapidly. This prevents players from wrongly thinking that they can know the exact location of their infected neighbours and attempting to adjust the circle to exclude those locations
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig2: Model network (a) compared to the game user interface (b). a shows a part of the square lattice spatial network with the player-controlled susceptible individual (black circle) in the centre. The player’s awareness neighbourhood (outer, dark blue circle, radius 4) contains infected neighbours (red circles) and non-infected neighbours (green circles). Nodes outside this circle contain individuals that are not visible to the player, but which may transmit infection into the player’s awareness neighbourhood. The inner, light blue circle shows the player’s chosen infection radius, ri,t. Here, ri,t = 1.5 and the player will contact 8 neighbours (bold-outlined circles). b shows the same scenario as displayed to the player via the game interface. The player controls the size of the infection radius by making the blue circle larger or smaller. Neighbours remain in fixed locations in the underlying lattice, but are displayed in the game interface as animated figures that move around rapidly. This prevents players from wrongly thinking that they can know the exact location of their infected neighbours and attempting to adjust the circle to exclude those locations
Mentions: Disease transmission and recovery are modelled using the well-known Susceptible-Infected-Recovered (SIR) model [27], adapted to deal with discrete individuals and discrete time. The epidemic model is closely linked with the spatial model, see Figs. 1 and 2. Initially, a fraction I0 of the population are infected and the rest are susceptible. At each time step, each individual makes contact with others; the details of which individuals can contact each other are left to the explanation of the spatial model (below). Each contact between a susceptible and an infected individual may cause the susceptible to become infected, with probability p. At each time step, infected individuals may recover, with probability q, after which they remain immune.Fig. 1

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