Prediction of COVID-19 infection spread through agent-based simulation

ABSTRACTIn this work, we develop an agent-based model to predict the infection spread with accuracy. Under COVID-19 pandemic, we faced difficult decision-making of disease control including social distancing and shutdown, which significantly restricted our daily living. Without strong evidences to its effect, however, it was very hard to implement necessary policies in a timely manner. To this end, it is imperative to design a computationally efficient simulation model that can predict the infection spread with accuracy, taking into account the changes of important control policies. We develop an agent-based model that can incorporate individual behaviors and interactions, while capturing large-scale features of the infection spread. We verify the accuracy of our model by comparing the prediction results with the traces of COVID-19 confirmed cases in several countries.