Human mobility based individual-level epidemic simulation platform

Abstract

Coronavirus has spread worldwide and about 3.5 million people have been confirmed infected and over 300 thousands people died. Scientists have reached the consensus that human mobility is the principal factor in spreading the virus and mobility should be restricted to control the epidemic. Against this background, we propose a novel coronavirus (COVID-19) fine-grained transmission model based on real-world human mobility data and develop a platform that maps the movement of people before determining transit flows and infection probabilities. Algorithms incorporate a series of incubation period and infection vector analysis. The next step is to work backward to find patients that have not yet been diagnosed by following the chain of transmission. The platform also aims to determine at-risk members of the population based on the travels of infected patients and provide early warning to those members of society. The multi-functional platform improves the opportunities for community leaders and decision-makers to implement different policies at the municipal and local levels, for a safe and healthy society. For example, local decision-makers can set optimal prevention and control policies based on the transmission within their local community.