A risk assessment system of COVID-19 based on Bayesian inference

Abstract

The novel coronavirus disease (COVID-19) has now spread to most countries in the world. Preventing and controlling the risk of the coronavirus disease has rapidly become a major concern. A risk assessment system of novel coronavirus disease is proposed based on Bayesian inference in this paper. The system includes multiple handheld terminals and a cloud processing centre. The handheld terminal measures, records, and uploads the individual’s physical information (e.g., body temperature, cough) and GPS information of the terminal. We establish a Bayesian diagnosis network to deduce the risk probability related to the individual’s detection information. The cloud obtains the individual’s detection information and positions in last 14 days, and estimates the epidemic risk probability using Bayesian inference. This probability can be helpful for relevant institutions to judge the individual’s risk levels and corresponding measures. This risk assessment system, which assesses the COVID-19 risk of subjects dynamically, can not only assist and guide the normalization of epidemic prevention and control in relevant institutions, but also assist in epidemiological case tracing.