Spatiotemporal changes of COVID-19 outbreak in Shanghai.
10.3760/cma.j.cn112338-20220608-00511
- VernacularTitle:上海市新型冠状病毒肺炎流行与疫情时空变化分析
- Author:
Jun Yan FAN
1
;
Jia Ying SHEN
2
;
Ming HU
1
;
Yue ZHAO
1
;
Jian Sheng LIN
3
;
Guang Wen CAO
1
Author Information
1. Department of Epidemiology, Faculty of Naval Medicine, Naval Medical University, Shanghai 200433, China.
2. Tongji University School of Medicine, Shanghai 200331,China.
3. School of Medicine,Jinan University, Guangzhou 510632, China.
- Publication Type:Journal Article
- MeSH:
Humans;
COVID-19/epidemiology*;
China/epidemiology*;
Disease Outbreaks;
Epidemics;
Spatial Analysis
- From:
Chinese Journal of Epidemiology
2022;43(11):1699-1704
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To clarify the epidemiological characteristics and spatiotemporal clustering dynamics of COVID-19 in Shanghai in 2022. Methods: The COVID-19 data presented on the official websites of Municipal Health Commissions of Shanghai during March 1, 2022 and May 31, 2022 were collected for a spatial autocorrelation analysis by GeoDa software. A logistic growth model was used to fit the epidemic situation and make a comparison with the actual infection situation. Results: Pudong district had the highest number of symptomatic and asymptomatic infectants, accounting for 29.30% and 35.58% of the total infectants. Differences in cumulative attack rates and infection rates among 16 districts (P<0.001) were significant. The rates were significantly higher in Huangpu district than in other districts. The attack rate of COVID-19 from March 1, 2022 to May 31, 2022 had a global spatial positive correlation (P<0.05). Spatial distribution of COVID-19 attack rate was different at different periods. The global autocorrelation coefficient from March 16 to March 29, April 6 to April 12 and May 18 to May 24 had no statistical significance (P>0.05). Our local autocorrelation analysis showed that 22 high-high clustering areas were detected in eight periods.The high-risk hot-spot areas have experienced a "less-more-less" change process. The growth model fitting results were consistent with the actual infection situation. Conclusion: There was a clear spatiotemporal correlation in the distribution of COVID-19 in Shanghai. The comprehensive prevention and control measures of COVID-19 epidemic in Shanghai have effectively prohibited the growth of the epidemic, not only curbing the spatially spread of high-risk epidemic areas, but also reducing the risk of transmission to other cities.
