The spatial dissemination of COVID-19 and associated socio-economic consequences

Abstract

The ongoing coronavirus disease 2019 (COVID-19) pandemic has wreaked havoc worldwide with millions of lives claimed, human travel restricted and economic development halted. Leveraging city-level mobility and case data, our analysis shows that the spatial dissemination of COVID-19 can be well explained by a local diffusion process in the mobility network rather than a global diffusion process, indicating the effectiveness of the implemented disease prevention and control measures. Based on the constructed case prediction model, it is estimated that there could be distinct social consequences if the COVID-19 outbreak happened in different areas. During the epidemic control period, human mobility experienced substantial reductions and the mobility network underwent remarkable local and global structural changes toward containing the spread of COVID-19. Our work has important implications for the mitigation of disease and the evaluation of the socio-economic consequences of COVID-19 on society.

Modelling the roles of visitor flows and returning migrants in the spatial diffusion of COVID-19 from Wuhan city in China

“… Gao et al (2020) employed six circular cartograms to visualise the expansion and spread of confirmed COVID-19 cases among 31 provinces in China. Using statistical analysis, Chen et al (2020) found high correlation between population flow from Wuhan and the number of COVID-19 cases in January 2020, similar to Kraemer et al (2020) and Zhang et al (2022) . Mu et al (2021) found that disease incidence in a city was mainly driven by inter-city mobility flow from Wuhan.…”

“…Like SARs (Severe acute respiratory syndrome) in 2003 ( Hufnagel et al, 2004 ), population flow is considered the main channel of COVID-19 transmission between cities and countries. Previous studies found close correlation between population flows from Wuhan and the COVID-19 cases in other areas in China ( Kraemer et al, 2020 ; Mu et al, 2021 ; Shen, 2021a ; Zhang et al, 2022 ). Epidemiological modelling has also been used to demonstrate the role of population flow ( Lai et al, 2020 ; Wei et al, 2021 ; Wu et al, 2020 ).…”

“…Many studies have investigated the relationship between human mobility and COVID-19 diffusion using statistical analysis ( Kraemer et al, 2020 ; Mu et al, 2021 ; Shen, 2021a ; Zhang et al, 2022 ) and epidemiological modelling ( Lai et al, 2020 ; Wei et al, 2021 ; Wu et al, 2020 ).…”

SARS-CoV-2 seroprevalence and living conditions in Bamako (Mali): a cross-sectional multistage household survey after the first epidemic wave, 2020

“… 25 Additional evidence from geographical and socioeconomic components, 26 27 highlights the question of inequalities and individual vulnerability at each stage of the epidemic’s spread: from dissemination including various factors such as household size, 28 transmission of infection within the community to the associated societal consequences. 29 …”

SARS-CoV-2 seroprevalence and living conditions in Bamako (Mali): a cross-sectional multistage household survey after the first epidemic wave, 2020

“…Finally, the trend of increasing positivity of the social indicator summarized in household profiles leads us to consider that understanding epidemic dynamics in populated cities involves taking into account the spatial structure of the population [24]. Additional evidence from geographic and socio-economic components [25, 26]), highlight the question of inequalities and individual vulnerability at each stage of the epidemic’s spread: from dissemination including various factors such as household size [27], transmission of infection within the community to the associated societal consequences [28].…”