Abstract
The ongoing Coronavirus Disease 2019 (COVID-19) pandemic threatens the health of humans and causes great economic losses. Predictive modeling and forecasting the epidemic trends are essential for developing countermeasures to mitigate this pandemic. We develop a network model, where each node represents an individual and the edges represent contacts between individuals where the infection can spread. The individuals are classified based on the number of contacts they have each day (their node degrees) and their infection status. The transmission network model was respectively fitted to the reported data for the COVID-19 epidemic in Wuhan (China), Toronto (Canada), and the Italian Republic using a Markov Chain Monte Carlo (MCMC) optimization algorithm. Our model fits all three regions well with narrow confidence intervals and could be adapted to simulate other megacities or regions. The model projections on the role of containment strategies can help inform public health authorities to plan control measures.
Highlights
The development of international trade and tourism has accelerated the spatial spread of infectious diseases
The unprecedented Coronavirus Disease 2019 (COVID-19) outbreak began at the end of 2019
Various scenarios of implementing mitigation strategies showed that the peak epidemic size and final epidemic size in Italy are greatly reduced by personal protection, social distancing, behavior change of symptomatically infected individuals, and quarantine
Summary
The development of international trade and tourism has accelerated the spatial spread of infectious diseases. Chinazzi et al used a global meta-population disease transmission model to project the impact of travel limitations on the national and international spread of the epidemic They showed that the travel restriction of Wuhan, China had a more marked effect on the international scale than that on Mainland China [6]. The epidemic curves are all fitted very well using the small-world network structure models, indicating that the typical small-world property is able to capture the contact patterns during COVID-19 epidemics The differences in these fitted parameters and starting times reflect the differences in the underlying transmission mechanisms and potential spread in the regions. Our findings can guide public health authorities to implement effective mitigation strategies and be prepared for potential future outbreaks
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