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.

Quantifying the impact of COVID-19 on e-bike safety in China via multi-output and clustering-based regression models

“…Based on longterm mobile device location data in the US, the University of Maryland research team showed that the pandemic itself has dramatically reduced people's number of trips and trip mileages, especially in states in a serious infection situation; while the pure impact of lockdown policies signed by governments was much smaller [19][20][21]. Using the same dataset, Xiao et al found that people's trip duration has notably decreased after the national call [22]. Similar evidence on the decline of travel frequency and duration was found in other regions, such as Asia and Europe [23,24].…”

“…To this end, we have found significant negative impacts of COVID-19 on the numbers of ebike fatalities and injuries at both the country-level and the clustering-based province-level in China. The fact could be caused by the decline of travel demand due to lockdown policies and the people's panic about the pandemic [19][20][21][22][23][24]. With the progress of vaccination campaigns, people's travel demand could recover together with e-bike safety issues in the post-pandemic world.…”

Model-based assessment of the risks of viral transmission in non-confined crowds

“…However, the translation of these results into epidemiological predictions relevant for policy-making ( Vuorinen et al, 2020 , Poydenot et al, 2021 ) is uneasy, and trailing. Poles apart from these microscopic studies, risk assessments at the scale of a facility or venue by means of agent-based pedestrian simulations ( Xiao et al, 2021 , Harweg et al, 2021 , Romero et al, 2020 ) or large-scale experiments ( Moritz et al, 2021 ) resort to particularly crude assumptions with regard to viral transmission. Often, an individual is considered exposed to the disease when he or she comes in a given radius (e.g., 2 m) around an infected person, regardless of their orientations, overlooking that their head orientations control the direction in which respiratory droplets are expelled.…”

Flatten the curve: Empirical evidence on how non-pharmaceutical interventions substituted pharmaceutical treatments during COVID-19 pandemic

“…NPTs are actions, apart from getting vaccinated and taking medicine, that people and communities can take to help slow the spread of illnesses like pandemic influenza [ 5 ]. NPTs have been proven to be considerable in delaying and containing the spread of the virus [ 6 – 12 ]. Many state governments issued stay-at-home orders, shut down businesses, and limited gatherings to restrict human mobilities.…”