Version 1
: Received: 24 February 2020 / Approved: 25 February 2020 / Online: 25 February 2020 (07:38:24 CET)
How to cite:
Liu, Z.; Magal, P.; Seydi, O.; Webb, G. Predicting the Cumulative Number of Cases for the COVID-19 Epidemic in China From Early Data. Preprints2020, 2020020365 (doi: 10.20944/preprints202002.0365.v1).
Liu, Z.; Magal, P.; Seydi, O.; Webb, G. Predicting the Cumulative Number of Cases for the COVID-19 Epidemic in China From Early Data. Preprints 2020, 2020020365 (doi: 10.20944/preprints202002.0365.v1).
Cite as:
Liu, Z.; Magal, P.; Seydi, O.; Webb, G. Predicting the Cumulative Number of Cases for the COVID-19 Epidemic in China From Early Data. Preprints2020, 2020020365 (doi: 10.20944/preprints202002.0365.v1).
Liu, Z.; Magal, P.; Seydi, O.; Webb, G. Predicting the Cumulative Number of Cases for the COVID-19 Epidemic in China From Early Data. Preprints 2020, 2020020365 (doi: 10.20944/preprints202002.0365.v1).
Abstract
We model the COVID-19 coronavirus epidemic in China. We use early reported case data to predict the cumulative number of reported cases to a final size. The key features of our model are the timing of implementation of major public policies restricting social movement, the identification and isolation of unreported cases, and the impact of asymptomatic infectious cases.
Keywords
corona virus; reported and unreported cases; isolation; quarantine; public closings; epidemic mathematical model
Subject
LIFE SCIENCES, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
