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A data-driven network model for the emerging COVID-19 epidemics in Wuhan, Toronto and Italy
journal contributionposted on 02.12.2020, 01:29 by L Xue, S Jing, Joel Miller, W Sun, H Li, JG Estrada-Franco, JM Hyman, H Zhu
© 2020 Elsevier Inc. 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.
LX is funded by Fundamental Research Funds for the Central Universities of China. JCM received startup funding from La Trobe University. WS is funded by the National Science Foundation for Young Scholars of Heilongjiang Province, China QC2018004, and Fundamental Research Funds for the Central Universities of China. JGEF is supported by multidisciplinary, Mexico grant SIP-IPN 20196759. HZ is supported by Canadian Institutes of Health Research (CIHR), Canadian COVID-19 Math Modelling Task Force, and York Research Chair program of York University, Canada.
Pagination10p. (p. 1-10)
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Science & TechnologyLife Sciences & BiomedicineBiologyMathematical & Computational BiologyLife Sciences & Biomedicine - Other TopicsCOVID-19Mitigation strategiesNetwork modelHeterogeneityControl measuresCOMPLEX NETWORKSDYNAMICSSIZEHumansPneumonia, ViralCoronavirus InfectionsContact TracingConfidence IntervalsMonte Carlo MethodMarkov ChainsQuarantineAlgorithmsModels, BiologicalComputer SimulationOntarioChinaItalyBasic Reproduction NumberMathematical ConceptsEpidemicsPandemicsBetacoronavirusBioinformatics