Risk Analysis of Intergroup Conflicts under the COVID-19 Pandemic: A Case Study of Washington, DC

Abstract

The COVID-19 pandemic not only seriously threatens human life and health, but also becomes a new risk source to human society, leading to a series of severe problems such as intergroup conflicts (protest marches, racial conflicts) and crimes (theft crimes, robbery crimes). In order to investigate the domino effect resulted from the pandemic, a model coupled with event chain and dynamic Bayesian network is established. Moreover, a typical case reported in Washington, DC was studied to build the main nodes of the Bayesian network. Furthermore, scenario analysis is conducted to examine how probability of specific nodes changes along with dynamic influence from other nodes. The results show that the increase of confirmed cases may considerably make the employment situation worsen and further aggravate the intergroup conflicts and crimes. Base on the quantitative analysis, in the scenario where there are a large number of cumulative confirmed cases, the probability of social unemployment risk at a high level increases by 0.25; in the scenario that the confirmed cases have a high growth rate, the probability of protest marches increases by 0.2; in the scenario social unemployment risk is at a low risk, the probability of robbery crime risk in a low level increases by 0.1. So it can be concluded that robbery crimes risk are the most sensitive to social employment, social protest march risk is most sensitive to the growth rate of confirmed cases, social unemployment risk is most sensitive to the cumulative number of confirmed cases.