Spatial epidemic dynamics of the COVID-19 outbreak in China

Kang, Dayun; Choi, Hyuk Joong; Kim, Jong-Hun; Choi, Jungsoon

doi:10.1016/j.ijid.2020.03.076

Cited by 354 publications

(344 citation statements)

References 7 publications

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“…However, mapping of disease incidence to identify spatial clustering and patterns remains an important pathway to understanding disease epidemiology and is required for effective planning, prevention or containment action [11][12][13]. There are a few studies that attempt to map the pandemic in China [14] and in Iran [15]. However, the temporal dynamic of the COVID-19 pandemic has not been taken into account in order to assess space-time dynamics although a descriptive representation of the spatio-temporal pattern of the CFR was published for Brazil [16].…”

Section: Introductionmentioning

confidence: 99%

The spatio-temporal epidemic dynamics of COVID-19 outbreak in Africa

et al. 2020

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Corona virus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first detected in the city of Wuhan, China in December 2019. Although, the disease appeared in Africa later than other regions, it has now spread to virtually all countries on the continent. We provide early spatio-temporal dynamics of COVID-19 within the first 62 days of the disease's appearance on the African continent. We used a two-parameter hurdle Poisson model to simultaneously analyse the zero counts and the frequency of occurrence. We investigate the effects of important healthcare capacities including hospital beds and number of medical doctors in different countries. The results show that cases of the pandemic vary geographically across Africa with notably high incidence in neighbouring countries particularly in West and North Africa. The burden of the disease (per 100 000) mostly impacted Djibouti, Tunisia, Morocco and Algeria. Temporally, during the first 4 weeks, the burden was highest in Senegal, Egypt and Mauritania, but by mid-April it shifted to Somalia, Chad, Guinea, Tanzania, Gabon, Sudan and Zimbabwe. Currently, Namibia, Angola, South Sudan, Burundi and Uganda have the least burden. These findings could be useful in guiding epidemiological interventions and the allocation of scarce resources based on heterogeneity of the disease patterns.

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Section: Introductionmentioning

confidence: 99%

The spatio-temporal epidemic dynamics of COVID-19 outbreak in Africa

et al. 2020

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“…Following the disease's dynamics and the exponential growth of the number of cases, several studies have been reported [2][3][4][5]. Particularly, those that perform temporal and spatial analyses of COVID-19 have demonstrated the impact of morbidity, mortality and global geographical dissemination of the disease in the world.…”

Section: Introductionmentioning

confidence: 99%

“…Particularly, those that perform temporal and spatial analyses of COVID-19 have demonstrated the impact of morbidity, mortality and global geographical dissemination of the disease in the world. The use of aggregate spatial data allows to map the patterns of the rapid progression of the disease and to support decision-making in the allocation of resources for the prevention and control of COVID-19 in priority areas [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Risk clusters of COVID-19 transmission in northeastern Brazil: prospective space–time modelling

et al. 2020

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This study aimed to analyse the trend and spatial–temporal clusters of risk of transmission of COVID-19 in northeastern Brazil. We conducted an ecological study using spatial and temporal trend analysis. All confirmed cases of COVID-19 in the Northeast region of Brazil were included, from 7 March to 22 May 2020. We used the segmented log-linear regression model to assess time trends, and the local empirical Bayesian estimator, the global and local Moran indexes for spatial analysis. The prospective space–time scan statistic was performed using the Poisson probability distribution model. There were 113 951 confirmed cases of COVID-19. The average incidence rate was 199.73 cases/100 000 inhabitants. We observed an increasing trend in the incidence rate in all states. Spatial autocorrelation was reported in metropolitan areas, and 178 municipalities were considered a priority, especially in the states of Ceará and Maranhão. We identified 11 spatiotemporal clusters of COVID-19 cases; the primary cluster included 70 municipalities from Ceará state. COVID-19 epidemic is increasing rapidly throughout the Northeast region of Brazil, with dispersion towards countryside. It was identified high risk clusters for COVID-19, especially in the coastal side.

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“…An OD flow graph is a visual analysis method that connects the starting place and destination with straight lines or curves, reflecting the OD flow through line thickness [25][26][27][28][29][30]. In this paper, the location information of the source and destination of the migration provided by the Baidu migration data is used to conduct clustering processing according to the time information of the migration data.…”

Section: Model Of Epidemic Transmission Forcementioning

confidence: 99%

Transmission and Control Pressure Analysis of the COVID-19 Epidemic Situation Using Multisource Spatio-temporal Big Data

Wang

Tan

Yao

et al. 2020

Preprint

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Taking the Guangdong-Hong Kong-Macao Greater Bay Area as the research area, this paper used OD cluster analysis based on Baidu migration data from January 11 to January 25 (before the sealing-off of Wuhan) and concluded that there is a significant correlation between the migration level from Wuhan to the GBA and the epidemic severity index. This paper also analyzed the migration levels and diffusivity of the outer and inner cities of the GBA. Lastly, four evaluation indexes were selected to research the possibility of work resumption and the rating of epidemic prevention and control through kernel density estimation. According to the study, the amount of migration depends on the geographical proximity, relationship and economic development of the source region, and the severity of the epidemic depends mainly on the migration volume and the severity of the epidemic in the source region. The epidemic risk is related not only to the severity of the epidemic in the source region but also to the degree of urban traffic development and the degree of urban openness. After the resumption of work, the pressure of epidemic prevention and control has been concentrated mainly in Shenzhen and Canton; the further away a region is from the core cities, the lower the pressure in that region. The mass migration of the population makes it difficult to control the epidemic effectively. The study of the relationship between migration volume, epidemic severity and epidemic risk is helpful to further analyze transmission types and predict the trends of the epidemic.

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Spatial epidemic dynamics of the COVID-19 outbreak in China

Cited by 354 publications

References 7 publications

The spatio-temporal epidemic dynamics of COVID-19 outbreak in Africa

The spatio-temporal epidemic dynamics of COVID-19 outbreak in Africa

Risk clusters of COVID-19 transmission in northeastern Brazil: prospective space–time modelling

Transmission and Control Pressure Analysis of the COVID-19 Epidemic Situation Using Multisource Spatio-temporal Big Data

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