A four-compartment model for the COVID-19 infection-implications on infection kinetics, control measures, and lockdown exit strategies

Tianbing Wang; Yanqiu Wu; Johnson Yiu-Nam Lau; Yingqi Yu; Liyu Liu; Jing Li; Kang Zhang; Weiwei Tong; Baoguo Jiang

doi:10.1093/pcmedi/pbaa018

A four-compartment model for the COVID-19 infection-implications on infection kinetics, control measures, and lockdown exit strategies

Precis Clin Med. 2020 Jun;3(2):104-112. doi: 10.1093/pcmedi/pbaa018. Epub 2020 May 28.

Authors

Tianbing Wang¹, Yanqiu Wu¹, Johnson Yiu-Nam Lau², Yingqi Yu³, Liyu Liu³, Jing Li⁴, Kang Zhang⁵, Weiwei Tong³, Baoguo Jiang¹

Affiliations

¹ Peking University People's Hospital, Beijing 100044, China.
² Department of Applied Biology and Chemical Technology, Hong Kong Polytechnic University, Hong Kong, China.
³ Gennlife (Beijing) Technology Co Ltd, Beijing, China.
⁴ School of Medicine, Hangzhou Normal University, Hangzhou 311121, China.
⁵ Center for Biomedicine and Innovations, Faculty of Medicine, Macau University of Science and Technology, Macau, China.

Abstract

Objective: To analyse the impact and repercussions of the surge in healthcare demand in response to the COVID-19 pandemic, assess the potential effectiveness of various infection/disease control measures, and make projections on the best approach to exit from the current lockdown.

Design: A four-compartment model was constructed for SARS-CoV-2 infection based on the Wuhan data and validated with data collected in Italy, the UK, and the US. The model captures the effectiveness of various disease suppression measures in three modifiable factors: (a) the per capita contact rate (β) that can be lowered by means of social distancing, (b) infection probability upon contacting infectious individuals that can be lowered by wearing facemasks, personal hygiene, etc., and (c) the population of infectious individuals in contact with the susceptible population, which can be lowered by quarantine. The model was used to make projections on the best approach to exit from the current lockdown.

Results: The model was applied to evaluate the epidemiological data and hospital burden in Italy, the UK, and the US. The control measures were identified as the key drivers for the observed epidemiological data through sensitivity analyses. Analysing the different lockdown exit strategies showed that a lockdown exit strategy with a combination of social separation/general facemask use may work, but this needs to be supported by intense monitoring which would allow re-introduction/tightening of the control measures if the number of new infected subjects increases again.

Conclusions and relevance: Governments should act early in a swift and decisive manner for containment policies. Any lockdown exit will need to be monitored closely, with regards to the potential of lockdown reimplementation. This mathematical model provides a framework for major pandemics in the future.

Keywords: COVID-19; control measures; exit strategy; population infection rates.