Crowd management COVID-19

Abstract

Crowds are a source of transmission in the COVID-19 spread. Contention and mitigation measures have focused on reducing people's mass gathering. Such efforts have led to a drop in the economy. The application of a vaccine at a world level represents a grand challenge for humanity, and it is not likely to accomplish even within months. In the meantime, we still need tools to allow the people integration into their regular routines reducing the risk of infection. In this context, this paper presents a solution for crowd management. The aim is to monitor and manage crowd levels in interior places or point-of-interests (POI), particularly shopping centers or stores. The solution is based on a POI recommendation system that suggests the nearest safe options upon request of a particular POI to visit by the user. In this sense, it recommends places near the user location with the least estimated crowd. The recommendation algorithm uses a top-K approach and behavioral game theory to predict the user's choice and estimate the crowd level for the requested POI. To evaluate the efficiency of this technological intervention in terms of the potential number of contacts of possible COVID-19 infections and the recommendation quality, we have developed an agent-based model (ABM). The adoption level of new technologies can be related to the end-user experience and trust in such technologies. As the end-user follows a recommendation that leads to uncrowded places, both the end-user experience and trust increased. We study and model this process using the OCEAN model of personality. The results from the studied scenarios showed that the proposed solution is widely adopted by the agents, as the trust factor increased from 0.5 (initial set value) to 0.76. In terms of crowd level, these are effectively managed and reduced on average by 40%. The mobility contacts were reduced by 40%, decreasing the risk of COVID-19 infection. An APP has been designed to support the described crowd management and contact tracing functionality. This APP is available on GitHub.

Keywords: Agent-based model; COVID-19; Mitigation strategy; POI recommendation.

Fig. 1

System’s architecture.

Fig. 2

Hypothesis causal model.

Fig. 3

People process overview.

Fig. 4

Influence diagram.

Fig. 5

Algorithm.

Fig. 6

Crowd by store - Scenario 1.

Fig. 7

Crowd by store - Scenario 2.

Fig. 8

Recommendation assessment - Scenario 2.

Fig. 9

Decisions evolution - Scenario 2.

Fig. 10

Crowd by store - Scenario 3.

Fig. 11

Recommendation assessment - Scenario 3.

Fig. 12

Decisions evolution - Scenario 3.

Fig. 13

Mobility contacts.

Fig. 14

Gama interface.

Fig. 15

Establishment screenshots. On the left, the main screen for establishment users. On the right, check-in screen.

Fig. 16

Statistics for establishment.

Fig. 17

User screenshots. On the left, notify diagnostic. On the right, main screen for the user.