ABSTRACT
COVID-19 has arisen great control challenges to Governments and decision-makers. In 2020, the COVID-19 pandemic has spread around the world, causing nearly 123 million of confirmed cases (March 22, 2021). With the fact that cities are densely populated and public transport is a place that gathers a great number of populations, questions of the impact of urban mobility on COVID-19 propagation and the impact of protection measures on COVID-19 propagation are to be addressed. This research paper presents our novel transport based approach for modeling and simulating COVID-19 disease centered on the SUMO traffic simulator. Conventional approaches will be presented firstly, we discuss their pros and cons and we give a comparison. Based on their comparison, we noticed that mathematical, spatiooral, cellular automata and agent-based models cannot represent many transport aspects related to transport restrictions (e.g., barriers and reduction of vehicles capacities). We detail then the proposed approach in which we describe the required data, which are Open Street Map data, traffic data, individuals' data, pandemic and restrictions data. We are currently using this approach for developing a COVID-19 simulator based on the SUMO traffic simulator. Obtained intermediate results confirmed that the proposed approach addresses well the above-mentioned questions. © 2021 IEEE.