ArticleInternational journal of environmental research and public health2020
Spatio-Temporal Patterns of the 2019-nCoV Epidemic at the County Level in Hubei Province, China.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Spatio-Temporal COVID-19 Modeling: A Global Systematic Review of Data Integration, Equity, and Lessons for Pandemic Preparedness.International journal of environmental research and public health · 2026Pooled it
- Applications of GIS and geospatial analyses in COVID-19 research: A systematic review.F1000Research · 2020Pooled it
- Spatial Analysis of Tuberculosis, COVID-19, and Tuberculosis/COVID-19 Coinfection in Recife, PE, Brazil.International journal of environmental research and public health · 2025Article
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- Spatiotemporal dynamics of epidemiology diseases: mobility based risk and short-term prediction modeling of COVID-19.Frontiers in public health · 2024Article
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- Geo-clusters and socio-demographic profiles at village-level associated with COVID-19 incidence in the metropolitan city of Jakarta: An ecological study.Transboundary and emerging diseases · 2022Article
- Impact of urbanisation and environmental factors on spatial distribution of COVID-19 cases during the early phase of epidemic in Singapore.Scientific reports · 2022Article
- Multiple change point estimation of trends in Covid-19 infections and deaths in India as compared with WHO regions.Spatial statistics · 2022Article
- Do spatiotemporal units matter for exploring the microgeographies of epidemics?Applied geography (Sevenoaks, England) · 2022Article
- Clinical informatics solutions in COVID-19 pandemic: Scoping literature review.Informatics in medicine unlocked · 2022Article
- Spatiotemporal Study of COVID-19 in Fars Province, Iran, October-November 2020: Establishment of Early Warning System.The Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale · 2022Article
- A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 cases.Stochastic environmental research and risk assessment : research journal · 2022Article
- Mapping and Spatial Pattern Analysis of COVID-19 in Central Iran Using the Local Indicators of Spatial Association (LISA).BMC public health · 2021Article
- Bayesian spatiotemporal forecasting and mapping of COVID-19 risk with application to West Java Province, Indonesia.Journal of regional science · 2021Article
- COVID-19 Deaths in the United States: Shifts in Hot Spots over the Three Phases of the Pandemic and the Spatiotemporally Varying Impact of Pandemic Vulnerability.International journal of environmental research and public health · 2021Article
- The Use of GIS Technology to Optimize COVID-19 Vaccine Distribution: A Case Study of the City of Warsaw, Poland.International journal of environmental research and public health · 2021Article
- Geospatial Analysis of COVID-19: A Scoping Review.International journal of environmental research and public health · 2021Article
- Spatial temporal distribution of COVID-19 risk during the early phase of the pandemic in Malawi.PeerJ · 2021Article
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4 authors.
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Abstract
Understanding the spatio-temporal characteristics or patterns of the 2019 novel coronavirus (2019-nCoV) epidemic is critical in effectively preventing and controlling this epidemic. However, no research analyzed the spatial dependency and temporal dynamics of 2019-nCoV. Consequently, this research aims to detect the spatio-temporal patterns of the 2019-nCoV epidemic using spatio-temporal analysis methods at the county level in Hubei province. The Mann-Kendall and Pettitt methods were used to identify the temporal trends and abrupt changes in the time series of daily new confirmed cases, respectively. The local Moran's I index was applied to uncover the spatial patterns of the incidence rate, including spatial clusters and outliers. On the basis of the data from January 26 to February 11, 2020, we found that there were 11 areas with different types of temporal patterns of daily new confirmed cases. The pattern characterized by an increasing trend and abrupt change is mainly attributed to the improvement in the ability to diagnose the disease. Spatial clusters with high incidence rates during the period were concentrated in Wuhan Metropolitan Area due to the high intensity of spatial interaction of the population. Therefore, enhancing the ability to diagnose the disease and controlling the movement of the population can be confirmed as effective measures to prevent and control the regional outbreak of the epidemic.
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