ArticleBMC public health2021
Mapping and Spatial Pattern Analysis of COVID-19 in Central Iran Using the Local Indicators of Spatial Association (LISA).
Article in BMC public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 32 citations in OpenAlex.
- Determinants of COVID-19 prevalence in Central Java, Indonesia: An ecological study of socio-demographic, environmental, and healthcare factors.Dialogues in health · 2026Article
- Spatio-temporal evolution characteristics, challenges and future perspectives of intelligent health eldercare industry development in China: a spatial analysis and policy text mining from 1982 to 2024.BMC health services research · 2026Article
- Global or local modeling for XGBoost in geospatial studies upon simulated data and German COVID-19 infection forecasting.Scientific reports · 2025Article
- Effect of urban structure, population density and proximity to contagion on COVID-19 infections during the SARS-CoV-2 Alpha and Omicron waves in Málaga, Spain, March 2020 to December 2021.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2025Article
- Detecting the Seasonal and Spatial Patterns of COVID-19 Hospitalization and Deaths in Iran: Insights from a Spatiotemporal and Hotspot Analysis.International journal of preventive medicine · 2025Article
- Geospatial Modeling of Deep Neural Visual Features for Predicting Obesity Prevalence in Missouri: Quantitative Study.JMIR AI · 2024Article
- Influences of Differentiated Residence and Workplace Location on the Identification of Spatiotemporal Patterns of Dengue Epidemics: A Case Study in Guangzhou, China.International journal of environmental research and public health · 2022Article
- Spatiotemporal Analysis and Risk Assessment Model Research of Diabetes among People over 45 Years Old in China.International journal of environmental research and public health · 2022Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUsing geographical analysis to identify geographical factors related to the prevalence of COVID-19 infection can affect public health policies aiming at controlling the virus. This study aimed to determine the spatial analysis of COVID-19 in Qom Province, using the local indicators of spatial association (LISA).
methodsIn a primary descriptive-analytical study, all individuals infected with COVID-19 in Qom Province from February 19
resultsThe prevalence of COVID-19 in Qom Province was estimated to be 356.75 per 100,000 populations. The pattern of spatial distribution of the prevalence of COVID-19 in Qom was clustered. District 3 (Imam Khomeini St.) and District 6 (Imamzadeh Ebrahim St.) were set in the High-High category of LISA: a high-value area surrounded by high-value areas as the two foci of COVID-19 in Qom Province. District 1 (Bajak) of urban districts was set in the Low-High category: a low-value area surrounded by high values. This district is located in a low-value area surrounded by high values.
conclusionsAccording to the results, district 3 (Imam Khomeini St.) and district 6 (Imamzadeh Ebrahim St.) areas are key areas for preventing and controlling interventional measures. In addition, considering the location of District 1 (Bajak) as an urban district in the Low-High category surrounded by high values, it seems that distance and spatial proximity play a major role in the spread of the disease.
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