Original Articles
10 February 2022
Vol. 17 No. s1 (2022): Special issue on COVID-19
https://doi.org/10.4081/gh.2022.1023

Spatiotemporal epidemiology of COVID-19 from an epidemic course perspective

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COVID-19, epidemiology, spatiotemporal analysis, spatial analysis, epidemic, epidemic course, health geography, China.
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Authors

Yuling Huang
Sichuan Centre for Disease Control and Prevention, Chengdu, China.
Shujuan Yang
https://orcid.org/0000-0002-6929-4823
West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China; International Institute of Spatial Lifecourse Epidemiology (ISLE), Wuhan University, Wuhan, China.
Yuxuan Zou
International Institute of Spatial Lifecourse Epidemiology (ISLE), Wuhan University, Wuhan, China.
Jianming Su
Health Commission of Sichuan Province, Chengdu, China.
Canglang Wu
Health Information Centre of Sichuan Province, Chengdu, China.
Bo Zhong
Sichuan Centre for Disease Control and Prevention, Chengdu, China.
Peng Jia
jiapengff@hotmail.com
https://orcid.org/0000-0003-0110-3637
International Institute of Spatial Lifecourse Epidemiology (ISLE), Wuhan University, Wuhan, China; School of Resource and Environmental Sciences, Wuhan University, Wuhan, China.

Although coronavirus disease 2019 (COVID-19) remains rampant in many countries, it has recently waned in Sichuan, China. This study examined spatiotemporal variations of the epidemiological characteristics of COVID-19 across its course. Three approaches, i.e. calendar-based, measure-driven and data-driven ones, were applied to all individual cases reported as of 30th November 2020, dividing the COVID-19 pandemic into five periods. A total of 808 people with confirmed diagnosis and 279 asymptomatic cases were reported, the majority of whom were aged 30-49 and <30 years, respectively. The highest risk was seen in Chengdu (capital city), with 411 confirmed and 195 asymptomatic cases. The main sources of infection changed from importation from Hubei Province to importation from other provinces, then local transmission and ultimately importation from foreign countries. The periods highlighted by the three methods presented different epidemic patterns and trends. The calendar-based periods were even with most cases aggregated in the first period, which did not reflect various transmission patterns of COVID-19 due to various sources of infection; the measure-driven and data-driven periods were not consistent with each other, revealing that the effects of implementing prevention measures were reflected on the epidemic trend with a time lag. For example, the decreasing trends of new cases occurred 7, 3 and 4 days later than the firstlevel emergency response, the district-level prevention measures and the second-level emergency response, respectively. This study has advanced our understanding of epidemic course and foreshown all stages of COVID-19 epidemic. Many countries can learn from our findings about what will occur next in their timelines and how to be better prepared.

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Spatiotemporal epidemiology of COVID-19 from an epidemic course perspective. (2022). Geospatial Health, 17(s1). https://doi.org/10.4081/gh.2022.1023
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