ArticleBMC infectious diseases2021
Spatio-temporal distribution characteristics of COVID-19 in China: a city-level modeling study.
Article in BMC infectious diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 3 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 3 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
- Small-scale spatiotemporal epidemiology of notifiable infectious diseases in China: a systematic review.BMC infectious diseases · 2022Pooled it
- Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review.International journal of environmental research and public health · 2022Pooled it
- Prevalence and influencing factors of occupational burnout among healthcare workers in the Chinese mainland during the late 2022 Omicron COVID-19 outbreak: a multicenter cross-sectional study.BMC public health · 2025Article
- Quantifying the magnitude of the general contextual effect in a multilevel study of SARS-CoV-2 infection in Ontario, Canada: application of the median rate ratio in population health research.Population health metrics · 2024Observational
- Evolution of COVID-19 dynamics in Guangdong Province, China: an endemic-epidemic modeling study.Archives of public health = Archives belges de sante publique · 2024Article
- Article
- Exploration of spatiotemporal heterogeneity and socio-demographic determinants on COVID-19 incidence rates in Sarawak, Malaysia.BMC public health · 2023Article
- Key epidemiological indicators and spatial autocorrelation patterns across five waves of COVID-19 in Catalonia.Scientific reports · 2023Article
- Predicting onset risk of COVID-19 symptom to support healthy travel route planning in the new normal of long-term coexistence with SARS-CoV-2.Environment and planning. B, urban analytics and city science · 2023Article
- Spatial Autocorrelation of COVID-19 in Slovakia.Tropical medicine and infectious disease · 2023Article
- Monitoring European data with prospective space-time scan statistics: predicting and evaluating emerging clusters of COVID-19 in European countries.BMC public health · 2022Article
- Evaluating COVID-19 control measures in mass gathering events with vaccine inequalities.Scientific reports · 2022Article
- Visualizing the features of inflection point shown on a temporal bar graph using the data of COVID-19 pandemic.Medicine · 2022Article
- SARS-CoV-2 antibody prevalence in a pediatric cohort of unvaccinated children in Mérida, Yucatán, México.PLOS global public health · 2022Article
- Article
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12 authors.
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Abstract
backgroundThe coronavirus disease 2019 (COVID-19) has become a pandemic. Few studies have been conducted to investigate the spatio-temporal distribution of COVID-19 on nationwide city-level in China.
objectiveTo analyze and visualize the spatiotemporal distribution characteristics and clustering pattern of COVID-19 cases from 362 cities of 31 provinces, municipalities and autonomous regions in mainland China.
methodsA spatiotemporal statistical analysis of COVID-19 cases was carried out by collecting the confirmed COVID-19 cases in mainland China from January 10, 2020 to October 5, 2020. Methods including statistical charts, hotspot analysis, spatial autocorrelation, and Poisson space-time scan statistic were conducted.
resultsThe high incidence stage of China's COVID-19 epidemic was from January 17 to February 9, 2020 with daily increase rate greater than 7.5%. The hot spot analysis suggested that the cities including Wuhan, Huangshi, Ezhou, Xiaogan, Jingzhou, Huanggang, Xianning, and Xiantao, were the hot spots with statistical significance. Spatial autocorrelation analysis indicated a moderately correlated pattern of spatial clustering of COVID-19 cases across China in the early phase, with Moran's I statistic reaching maximum value on January 31, at 0.235 (Z = 12.344, P = 0.001), but the spatial correlation gradually decreased later and showed a discrete trend to a random distribution. Considering both space and time, 19 statistically significant clusters were identified. 63.16% of the clusters occurred from January to February. Larger clusters were located in central and southern China. The most likely cluster (RR = 845.01, P < 0.01) included 6 cities in Hubei province with Wuhan as the centre. Overall, the clusters with larger coverage were in the early stage of the epidemic, while it changed to only gather in a specific city in the later period. The pattern and scope of clusters changed and reduced over time in China.
conclusionsSpatio-temporal cluster detection plays a vital role in the exploration of epidemic evolution and early warning of disease outbreaks and recurrences. This study can provide scientific reference for the allocation of medical resources and monitoring potential rebound of the COVID-19 epidemic in China.
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