Spread of COVID-19 in China: analysis from a city-based epidemic and mobility model

Abstract

Understanding the processes and mechanisms of the spatial spread of epidemics is essential for making reasonable judgments on the development trends of epidemics and for adopting effective containment measures. Using multi-agent technology and big data on population migration, this paper constructed a city-based epidemic and mobility model (CEMM) to stimulate the spatiotemporal of COVID-19. Compared with traditional models, this model is characterized by an urban network perspective and emphasizes the important role of intercity population mobility and high-speed transportation networks. The results show that the model could simulate the inter-city spread of COVID-19 at the early stage in China with high precision. Through scenario simulation, the paper quantitatively evaluated the effect of control measures "city lockdown" and "decreasing population mobility" on containing the spatial spread of the COVID-19 epidemic. According to the simulation, the total number of infectious cases in China would have climbed to 138,824 on February 2020, or 4.46 times the real number, if neither of the measures had been implemented. Overall, the containment effect of the lockdown of cities in Hubei was greater than that of decreasing intercity population mobility, and the effect of city lockdowns was more sensitive to timing relative to decreasing population mobility.

Spatiotemporal Dynamic of COVID-19 Diffusion in China: A Dynamic Spatial Autoregressive Model Analysis

“…In order to explore the causes of COVID-19 transmission, we used an OLS regression model. The findings from this analysis showed that population migration in and out of Wuhan was a primary factor in transmission, which is consistent with much of the literature [18,25,35]. The close relationship between population movements and the prevalence of epidemics has long been established [36].…”

“…Aside from migration, only the GDP had a significant impact on COVID-19's spread; that is, in the case of fixed migration in Wuhan, regions with higher GDPs were likely to face more serious epidemic transmission. Currently, only a few studies have focused on the role of economic factors in epidemic transmission [30][31][32][33][34][35][36][37][38][39][40][41][42]. We considered that a higher GDP was suggestive of greater human activity, closer traffic flows, and socioeconomic connections with other cities, as well as higher population movement (both between and within cities).…”

Assessing regional risk of COVID-19 infection from Wuhan via high-speed rail

“…Linking transportation networks with epidemic control has become a research focus that has received more and more attention from scholars and practitioners in recent years. For the COVID-19 pandemic most studies, so far, have investigated the risk of COVID-19 infection from Wuhan to other domestic regions ( Zhao et al, 2020 ; Zhang et al, 2020b ; Wei et al, 2021 ) or from one country to other countries via civil aviation ( Christidis and Christodoulou, 2020 ). Some researchers have found that there is a strong correlation between the cumulative number of confirmed cases of COVID-19 and travel by train in China, while travel by car and flight is not significantly correlated with the cumulative number ( Zhao et al, 2020 ).…”

Geovisualizing cancelled air and high-speed train services during the outbreak of COVID-19 in China

“… Zhang et al (2020) suggested that air and high-speed train (HST) services have played an important role in the spread of COVID-19 across Chinese cities. In China, locking down cities and travel restrictions were major control measures used to stop population migration and slow down the spread of the COVID-19 pandemic ( Wei et al, 2021 ). Therefore, Chinese airlines and China Railway cancelled many flights and HSTs due to greatly reduced intercity traffic demand and travel restrictions announced by the government.…”