Modeling indoor-level non-pharmaceutical interventions during the COVID-19 pandemic: A pedestrian dynamics-based microscopic simulation approach

Abstract

Mathematical modeling of epidemic spreading has been widely adopted to estimate the threats of epidemic diseases (i.e., the COVID-19 pandemic) as well as to evaluate epidemic control interventions. The indoor place is considered to be a significant epidemic spreading risk origin, but existing widely-used epidemic spreading models are usually limited for indoor places since the dynamic physical distance changes between people are ignored, and the empirical features of the essential and non-essential travel are not differentiated. In this paper, we introduce a pedestrian-based epidemic spreading model that is capable of modeling indoor transmission risks of diseases during people's social activities. Taking advantage of the before-and-after mobility data from the University of Maryland COVID-19 Impact Analysis Platform, it's found that people tend to spend more time in grocery stores once their travel frequencies are restricted to a low level. In other words, an increase in dwell time could balance the decrease in travel frequencies and satisfy people's demands. Based on the pedestrian-based model and the empirical evidence, combined non-pharmaceutical interventions from different operational levels are evaluated. Numerical simulations show that restrictions on people's travel frequency and open hours of indoor places may not be universally effective in reducing average infection risks for each pedestrian who visit the place. Entry limitations can be a widely effective alternative, whereas the decision-maker needs to balance the decrease in risky contacts and the increase in queue length outside the place that may impede people from fulfilling their travel needs. The results show that a good coordination among the decision-makers can contribute to the improvement of the performance of combined non-pharmaceutical interventions, and it also benefits the short-term and long-term interventions in the future.

Keywords: Epidemic spreading; Pedestrian dynamics; Travel demand.

Fig. 1

Observed mobility changes using UMD COVID-19 IAP.

Fig. 2

Daily mean dwell time versus number of visits.

Fig. 3

Arrival time distribution.

Fig. 4

Sketch map of pedestrian-based epidemic spreading process.

Fig. 5

Results of the baseline scenarios.

Fig. 6

Results of the combined non-pharmaceutical interventions.

Fig. A.1

Types of obstacles in a square space.

Fig. B.1

The total infection time for different desired speeds.