Estimation of reproduction numbers of COVID-19 in typical countries and epidemic trends under different prevention and control scenarios

Abstract

The coronavirus disease 2019 (COVID-19) has become a life-threatening pandemic. The epidemic trends in different countries vary considerably due to different policy-making and resources mobilization. We calculated basic reproduction number (R₀) and the time-varying estimate of the effective reproductive number (R_t) of COVID-19 by using the maximum likelihood method and the sequential Bayesian method, respectively. European and North American countries possessed higher R₀ and unsteady R_t fluctuations, whereas some heavily affected Asian countries showed relatively low R₀ and declining R_t now. The numbers of patients in Africa and Latin America are still low, but the potential risk of huge outbreaks cannot be ignored. Three scenarios were then simulated, generating distinct outcomes by using SEIR (susceptible, exposed, infectious, and removed) model. First, evidence-based prompt responses yield lower transmission rate followed by decreasing R_t. Second, implementation of effective control policies at a relatively late stage, in spite of huge casualties at early phase, can still achieve containment and mitigation. Third, wisely taking advantage of the time-window for developing countries in Africa and Latin America to adopt adequate measures can save more people’s life. Our mathematical modeling provides evidence for international communities to develop sound design of containment and mitigation policies for COVID-19.

Keywords

reproduction number / SEIR model / COVID-19 / estimate

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Acknowledgements

This work is funded by Medicine and Engineering Interdisciplinary Research Fund of Shanghai Jiao Tong University (No. YG2020YQ06), the National Key Research and Development Project (Nos. 2018YFC1705100, 2018YFC1705103, and 2018YFC2000700) and the National Natural Science Foundation of China (Nos. 71673187 and 81630086), the Key Research Program (No. ZDRW-ZS-2017-1) of the Chinese Academy of Sciences, Innovative research team of high-level local universities in Shanghai. We acknowledge all healthcare workers involved in the diagnosis, treatment, and prevention of COVID-19 all around world. We thank WHO and other institutes for coordinating data collection for patients with COVID-19.

Compliance with ethics guideline

Chen Xu, Yinqiao Dong, Xiaoyue Yu, Huwen Wang, Lhakpa Tsamlag, Shuxian Zhang, Ruijie Chang, Zezhou Wang, Yuelin Yu, Rusi Long, Ying Wang, Gang Xu, Tian Shen, Suping Wang, Xinxin Zhang, Hui Wang, and Yong Cai declare no competing interests. The data sets analyzed for this study can be found in the reports of the World Health Organization (WHO) (https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/). This manuscript does not involve a research protocol requiring approval by the relevant institutional review board or ethics committee.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11684-020-0787-4 and is accessible for authorized users.

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2020 Higher Education Press