Propagation dynamics and control policies of COVID-19 pandemic at early stages: Implications on future resurgence response

Author:

Dong Ni12,Guan Xiangyang3ORCID,Zhang Jin1,Zhou Hanchu4ORCID,Zhang Jie1,Liu Xiaobo1,Sun Yichen1ORCID,Xu Pengpeng5ORCID,Li Qin6,Hao Xingjie7

Affiliation:

1. School of Transportation and Logistics, National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu, Sichuan 611756, China

2. Lyles School of Civil Engineering, Purdue University, West Lafayette, Indiana 47907, USA

3. Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98105, USA

4. School of Data Science, City University of Hong Kong, Hong Kong 999077, China

5. Department of Civil Engineering, The University of Hong Kong, Hong Kong 999077, China

6. Department of Nephrology, Chengdu Second People’s Hospital, Chengdu, Sichuan 610021, China

7. Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China

Abstract

The spreading of novel coronavirus (SARS-CoV-2) has gravely impacted the world in the last year and a half. Understanding the spatial and temporal patterns of how it spreads at the early stage and the effectiveness of a governments' immediate response helps our society prepare for future COVID-19 waves or the next pandemic and contain it before the spreading gets out of control. In this article, a susceptible-exposed-infectious-removed model is used to model the city-to-city spreading patterns of the disease at the early stage of its emergence in China (from December 2019 to February 2020). Publicly available reported case numbers in 312 Chinese cities and between-city mobility data are leveraged to estimate key epidemiological characteristics, such as the transmission rate and the number of infectious people for each city. It is discovered that during any given time period, there are always only a few cities that are responsible for spreading the disease to other cities. We term these few cities as transmission centers. The spatial and temporal changes in transmission centers demonstrate predictable patterns. Moreover, rigorously designed experiments show that in controlling the disease spread in a city, non-pharmaceutical interventions (NPIs) implemented at transmission centers are more effective than the NPI implemented in the city itself. These findings have implications on the control of an infectious disease at the early stage of its spreading: implementing NPIs at transmission centers at early stages is effective in controlling the spread of infectious diseases.

Funder

National Natural Science Foundation of China

National Institutes of Health

China Scholarship Council

National Key Research and Development Program of China

Publisher

AIP Publishing

Subject

Applied Mathematics,General Physics and Astronomy,Mathematical Physics,Statistical and Nonlinear Physics

Reference37 articles.

1. World Health Organization (2020). WHO Coronavirus Disease (COVID-19) Dashboard. https://covid19.who.int/. (Last accessed 09/21/2021).

2. World Health Organization (2020). Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). https://www.who.int/publications-detail-redirect/report-of-the-who-china-joint-mission-on-coronavirus-disease-2019-(covid-19). (Last accessed 09/21/2021).

3. World Health Organization (2020). Coronavirus disease (COVID-19). https://www.who.int/emergencies/diseases/novel-coronavirus-2019. (Last accessed 10/22/2021).

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