Understanding the spatial diffusion dynamics of the COVID-19 pandemic in the city system in China

Abstract

Investigating the spatial epidemic dynamics of COVID-19 is crucial in understanding the routine of spatial diffusion and in surveillance, prediction, identification and prevention of another potential outbreak. However, previous studies attempting to evaluate these spatial diffusion dynamics are limited. Using city as the research unit and spatial association analysis as the primary strategy, this study explored the changing primary risk factors impacting the spatial spread of COVID-19 across Chinese cities under various diffusion assumptions and throughout the epidemic stage. Moreover, this study investigated the characteristics and geographical distributions of high-risk areas in different epidemic stages. The results empirically indicated rapid intercity diffusion at the early stage and primarily intracity diffusion thereafter. Before countermeasures took effect, proximity, GDP per capita, medical resources, outflows from Wuhan and intercity mobility significantly affected early diffusion. With speedily effective countermeasures, outflows from the epicenter, proximity, and intracity outflows played an important role. At the early stage, high-risk areas were mainly cities adjacent to the epicenter, with higher GDP per capita, or a combination of higher GDP per capita and better medical resources, with more outflow from the epicenter, or more intercity mobility. After countermeasures were effected, cities adjacent to the epicenter, or with more outflow from the epicenter or more intracity mobility became high-risk areas. This study provides an insightful understanding of the spatial diffusion of COVID-19 across cities. The findings are informative for effectively handling the potential recurrence of COVID-19 in various settings.

Keywords: COVID-19; China; City; Diffusion; Spatial association.

Fig. 1

City-level total confirmed cases and morbidity in mainland China, March 15, 2020. (a) Total confirmed cases. (b) Crude morbidity. (c) EB smoothed morbidity. (d) Scatter plot of crude morbidity vs. EB smoothed morbidity.

Fig. 2

The diffusion of COVID-19 from January 19 to March 15 by week. (a) Accumulated cases. (b) Newly confirmed cases. (c)Box chart of morbidity (logarithm transformed). (d) Box chart of newly confirmed cases (logarithm transformed).

Fig. 3

The distribution of mobility data. (a) Inter-city move-in index by city by province by week. (b) Intra-city mobility by city by province by week. (c) Outmigration from Wuhan (January 1 to 18). (d) Outmigration from Wuhan (Jan 1 to 23). (Note: Movement data two weeks earlier (week 0-Jan 12 to 18, week -1-Jan 5 to 11) and later (week 9-March 16 to 22, week 10-March 23 to 29) were added in Fig. 3(a) and (b) for ease of comparison. BJ-Beijing, TJ-Tianjin, HeB-Hebei, SX-Shanxi, IM-Inner Mongolia, LN-Liaoning, JL-Jilin, HLJ-Heilongjiang, SH-Shanghai, JS-Jiangsu, ZJ-Zhejiang, AH-Anhui, FJ-Fujian, JX-Jiangxi, SD-Shandong, HeN-Henan, HB-Hubei, HN-Hunan, GD-Guangdong, GX-Guangxi, HaN-Hainan, CQ-Chongqing, SC-Sichuan, GZ-Guizhou, YN-Yunnan, TB-Tibet, SaX-Shaanxi, GS-Gansu, QH-Qinghai, NX-Ningxia, XJ-Xinjiang.).

Fig. 4

Temporal change of Moran's I statistics for morbidity and newly confirmed cases under various assumptions. (a) Daily morbidity rate. (b) Daily newly confirmed cases. (Single factor matrix. Black line with squares records Moran's I value, red line with circles records the corresponding p-value, and dark blue dashed line is the 0.05 p-value line). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 5

Temporal change of Moran's I statistics for morbidity and newly confirmed cases under various assumptions. (a) Daily morbidity rate. (b) Daily newly confirmed cases. (Compound factor matrix. Black line with squares records Moran's I value, red line with circles records the corresponding p-value, and dark blue dashed line is the 0.05 p-value line). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 6

Clusters of morbidity under various assumptions. (a) Queen_based contiguity. (b) GDP/capita. (c) Licensed (assistant) doctors. (d) Beds per 1000 people. (e) Outflows from Wuhan. (f) Accumulated inter-city mobility. (g) Accumulated intra-city mobility. Note: Cities at the right-hand side of the arrow have indicators with median or higher values, cities at the left-hand side of the arrow have indicators with lower than median values.

Fig. 7

Clusters of newly confirmed cases under various assumptions. (a) Queen_based contiguity. (b) GDP/capita. (c) Hospitals. (d) Licensed (assistant) doctors. (e) Beds. (f) Outflows from Wuhan. (g) Inter-city mobility. (h) Intra-city mobility. Note: Cities at the right-hand side of the arrow have indicators with median or higher values, cities at the left-hand side of the arrow have indicators with lower than median values.