How Human Mobility Models Can Help to Deal with COVID-19

Abstract

One of the key factors for the spreading of human infections, such as the COVID-19, is human mobility. There is a huge background of human mobility models developed with the aim of evaluating the performance of mobile computer networks, such as cellular networks, opportunistic networks, etc. In this paper, we propose the use of these models for evaluating the temporal and spatial risk of transmission of the COVID-19 disease. First, we study both pure synthetic model and simulated models based on pedestrian simulators, generated for real urban scenarios such as a square and a subway station. In order to evaluate the risk, two different risks of exposure are defined. The results show that we can obtain not only the temporal risk but also a heat map with the exposure risk in the evaluated scenario. This is particularly interesting for public spaces, where health authorities could make effective risk management plans to reduce the risk of transmission.

Modelling the epidemic dynamics of COVID-19 with consideration of human mobility

“…[19] utilized telecommunication data in Switzerland to describe the human mobility patterns and construct regression models to predict the growth of COVID-19 cases. [12] proved that compared to pure synthetic model, simulated models based on pedestrian simulators make more contributions to the solution of COVID-19. However, the above research only considered inter-region mobility patterns, which can-not fully capture the effects of human mobility on disease transmission.…”

“…Due to the large population movement during the outbreak, the modelling of COVID-19 has become complicated. Some scholars tried to consider the impact of human mobility on the epidemic [5,12,15,19,23,27]. Specifically, [27] utilized the exported infected cases between cities and the human mobility data to forecast the extent of the domestic and global risks of epidemic.…”

A history of crowd simulation: the past, evolution, and new perspectives

“…Furthermore, finally, Shi et al [92] propose a work which goal is to verify the applicability of the Social Force Model (SFM), embedded in Viswalk software, to reproduce the effect of egress flow under normal and emergency conditions. As can be observed, many papers have been proposed in the context of evacuation and crowd mobility, as the example of Orallo and Martinez [36]. The authors study models for evaluating the temporal and spatial risk of transmission of the COVID-19 as a function of humans mobility.…”

Machine Learning Models of COVID-19 Cases in the United States: A Study of Initial Lockdown and Reopen Regimes

“…In this paper, we analyze the spread of COVID-19 cases in the United States from 13 March 2020 to 31 May 2020. We divide this period into two regimes to reflect lockdown (13 March-15 April) and reopen (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31). We observe this division to recognize two distinct regimes of American life.…”

“…We have three general expectations based on human mobility, social distancing, and control measures (school closures, public gathering bans) implemented in China [14][15][16], as well as travel restrictions, social distancing, and home quarantine implemented in Australia [17]. All of these studies tracked, modeled, or simulated changes in COVID-19 within or between different cities, states, and regions.…”