La Trobe

File(s) stored somewhere else

Please note: Linked content is NOT stored on La Trobe and we can't guarantee its availability, quality, security or accept any liability.

A data-driven network model for the emerging COVID-19 epidemics in Wuhan, Toronto and Italy

journal contribution
posted 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.

Funding

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.

History

Publication Date

01/01/2020

Journal

Mathematical Biosciences

Volume

326

Article Number

108391

Pagination

10p. (p. 1-10)

Publisher

Elsevier

ISSN

0025-5564

Rights Statement

Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. TheCOVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.