Characterization of superspreaders movement in a bidirectional corridor using a social force model

Abstract

During infectious disease outbreaks, some infected individuals may spread the disease widely and amplify risks in the community. People whose daily activities bring them in close proximity to many others can unknowingly become superspreaders. The use of contact tracking based on social networks, GPS, or mobile tracking data can help to identify superspreaders and break the chain of transmission. We propose a model that aims at providing insight into risk factors of superspreading events. Here, we use a social force model to estimate the superspreading potential of individuals walking in a bidirectional corridor. First, we applied the model to identify parameters that favor exposure to an infectious person in scattered crowds. We find that low walking speed and high body mass both increase the expected number of close exposures. Panic events exacerbate the risks while social distancing reduces both the number and duration of close encounters. Further, in dense crowds, pedestrians interact more and cannot easily maintain the social distance between them. The number of exposures increases with the density of person in the corridor. The study of movements reveals that individuals walking toward the center of the corridor tend to rotate and zigzag more than those walking along the edges, and thus have higher risks of superspreading. The corridor model can be applied to designing risk reduction measures for specific high volume venues, including transit stations, stadiums, and schools.

Keywords: COVID-19; close-contact infections; contact patterns; panic; pedestrian dynamics; social distancing; superspreaders movement; superspreading events.

Figure 1

Schematic representation of the model parameters defining the interactions between two pedestrians described as blue disks. Each individual has a center x_i and a radius r_i. The distance separating the two individuals i and j is described by d_ij, while we denote the distance separating an individual from a wall w by d_iw. The desired velocity of each individual is v_{d, i} and ϕ_ij is the angle between the direction of the desired velocity of i and the distance between the individual i and j.

Figure 2

A representation of the domain where numerical simulations are taking place. It corresponds to a bidirectional corridor with two entrances/exits. Red disks represent individuals moving from the left to the right, while blue ones correspond to pedestrians walking from the right to the left. The size of each disk is proportional to the mass of the corresponding individual.

Figure 3

(A) Impact of compliance to physical distancing on the distribution of contacts among the pedestrians. The results correspond to the average of 30 numerical simulations for each value. (B) The total and average numbers of contacts for different values of A_soc. (C) Contact distribution for A_soc = 80N, fitted to a von Mises distribution. (D) Contact distribution for high compliance to social-distancing (A_soc = 340N) fitted with a Pearson distribution.

Figure 4

(A) Impact of changes in the desired velocity induced by panic situations on the distribution of contacts. (B) The total and the average contact numbers in the population for different values of the mean desired velocity (v_d).

Figure 5

Comparison of the duration of contacts of a pedestrian with a high number of contacts and a pedestrian with a low number of contacts. (A) The average duration of contacts as a function of the compliance level to physical distancing. (B) The average duration of contacts as a function of the mean desired velocity.

Figure 6

(A) A comparison between the average number of contacts for different crowd densities. We also investigate potential superspreaders position in the corridor where the y-coordinate varies from 0 to 10 m and the x-coordinate varies from 0 to 50 m. (B) The distribution of the average y-coordinate of potential superspreaders in crowded settings. (C) The distribution of the average y-coordinate of pedestrians with a lower number of contacts in a dense crowd.

Figure 7

Comparison of trajectories in both dense and scattered crowds. (A) Examples of the trajectory of individuals with a high number of contacts. (B) Examples of the trajectory of individuals with a low number of contacts.