Back to Search Start Over

Simulating potential outbreaks of Delta and Omicron variants based on contact-tracing data: A modelling study in Fujian Province, China

Authors :
Yichao Guo
Wenjing Ye
Zeyu Zhao
Xiaohao Guo
Wentao Song
Yanhua Su
Benhua Zhao
Jianming Ou
Yanqin Deng
Tianmu Chen
Source :
Infectious Disease Modelling, Vol 8, Iss 1, Pp 270-281 (2023)
Publication Year :
2023
Publisher :
KeAi Communications Co., Ltd., 2023.

Abstract

Although studies have compared the relative severity of Omicron and Delta variants by assessing the relative risks, there are still gaps in the knowledge of the potential COVID-19 burden these variations may cause. And the contact patterns in Fujian Province, China, have not been described. We identified 8969 transmission pairs in Fujian, China, by analyzing a contact-tracing database that recorded a SARS-CoV-2 outbreak in September 2021. We estimated the waning vaccine effectiveness against Delta variant infection, contact patterns, and epidemiology distributions, then simulated potential outbreaks of Delta and Omicron variants using a multi-group mathematical model. For instance, in the contact setting without stringent lockdowns, we estimated that in a potential Omicron wave, only 4.7% of infections would occur in Fujian Province among individuals aged >60 years. In comparison, 58.75% of the death toll would occur in unvaccinated individuals aged >60 years. Compared with no strict lockdowns, combining school or factory closure alone reduced cumulative deaths of Delta and Omicron by 28.5% and 6.1%, respectively. In conclusion, this study validates the need for continuous mass immunization, especially among elderly aged over 60 years old. And it confirms that the effect of lockdowns alone in reducing infections or deaths is minimal. However, these measurements will still contribute to lowering peak daily incidence and delaying the epidemic, easing the healthcare system's burden.

Details

Language :
English
ISSN :
24680427
Volume :
8
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Infectious Disease Modelling
Publication Type :
Academic Journal
Accession number :
edsdoj.12046e9f26046dc90241e5887090ce4
Document Type :
article
Full Text :
https://doi.org/10.1016/j.idm.2023.02.002