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The ongoing Coronavirus Disease 2019 (COVID-19) pandemic threatens the healthof humans and causes great economic losses. Predictive modelling andforecasting the epidemic trends are essential for developing countermeasures tomitigate this pandemic. We develop a network model, where each node representsan individual and the edges represent contacts between individuals where theinfection can spread. The individuals are classified based on the number ofcontacts they have each day (their node degrees) and their infection status.The transmission network model was respectively fitted to the reported data forthe COVID-19 epidemic in Wuhan (China), Toronto (Canada), and the ItalianRepublic using a Markov Chain Monte Carlo (MCMC) optimization algorithm. Ourmodel fits all three regions well with narrow confidence intervals and could beadapted to simulate other megacities or regions. The model projections on therole of containment strategies can help inform public health authorities toplan control measures.

A Data-Driven Network Model for the Emerging COVID-19 Epidemics in Wuhan, Toronto and Italy