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检测伊朗新冠肺炎住院和死亡的季节性及空间模式:时空与热点分析的见解

Detecting the Seasonal and Spatial Patterns of COVID-19 Hospitalization and Deaths in Iran: Insights from a Spatiotemporal and Hotspot Analysis.

作者信息

Mounesan Leila, Farhadi Ebrahim, Eybpoosh Sana, Hosseini Ali, Parsaeian Mahboubeh, Gharibzadeh Safoora, Ahmadinezhad Mozhgan, Bahari Farideh, Gouya Mohammad Mehdi, Haghdoost Aliakbar, Mostafavi Ehsan

机构信息

Department of Epidemiology and Biostatistics, Research Centre for Emerging and Reemerging Infectious Diseases, Pasteur Institute of Iran, Tehran, Iran.

Department of Human Geography and Planning, University of Tehran, Tehran, Iran.

出版信息

Int J Prev Med. 2025 Apr 24;16:20. doi: 10.4103/ijpvm.ijpvm_146_24. eCollection 2025.


DOI:10.4103/ijpvm.ijpvm_146_24
PMID:40376078
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12080935/
Abstract

BACKGROUND: Understanding the seasonal and spatial patterns of COVID-19 hospitalization and deaths is crucial for effective hospital management, resource allocation, and public health interventions. The current study conducts a spatiotemporal hotspot analysis that explores the seasonal and geographical patterns of high-risk areas of COVID-19 hospitalizations and deaths in Iran. METHODS: Provincial-level data on laboratory-confirmed COVID-19 cases with acute respiratory symptoms in Iran (February 2019-March 30, 2022) were collected. Hotspot analyses mapped seasonal incidence risks, and Global Moran's spatial autocorrelation analysis identified COVID-19 clusters. RESULTS: Over the 2 years, 26 hotspots and 11 cold spots were identified ( < 0.05). Western and central provinces showed the highest hospitalization hotspots, while the west and north had the most death hotspots. South and southeast provinces exhibited low incidence and the highest number of cold spots. High-risk areas were prevalent in spring and autumn, mainly in the west, north, and central regions. CONCLUSIONS: This research unveils the clustering patterns of COVID-19 hospitalizations and fatalities in Iran during the most severe pandemic. Spatial clusters and dynamic hotspots varied across regions and time. Prioritizing high-risk areas during critical epidemic waves, devising seasonal care strategies, and implementing preventive measures can significantly improve health outcomes.

摘要

背景:了解新冠病毒感染住院和死亡的季节性及空间模式对于有效的医院管理、资源分配和公共卫生干预至关重要。本研究进行了一项时空热点分析,以探究伊朗新冠病毒感染住院和死亡高风险地区的季节性和地理模式。 方法:收集了伊朗2019年2月至2022年3月30日期间有急性呼吸道症状的实验室确诊新冠病毒感染病例的省级数据。热点分析绘制了季节性发病风险图,全局莫兰空间自相关分析确定了新冠病毒感染聚集区。 结果:在这两年间,共确定了26个热点和11个冷点(<0.05)。西部和中部省份的住院热点最高,而西部和北部的死亡热点最多。南部和东南部省份发病率较低,冷点数量最多。高风险地区在春季和秋季较为普遍,主要集中在西部、北部和中部地区。 结论:本研究揭示了伊朗在最严重疫情期间新冠病毒感染住院和死亡的聚集模式。空间聚集区和动态热点在不同地区和时间有所不同。在关键疫情波期间对高风险地区进行优先排序、制定季节性护理策略以及实施预防措施可显著改善健康结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/006a/12080935/7ff36dd11364/IJPVM-16-20-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/006a/12080935/d63749ebe1c2/IJPVM-16-20-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/006a/12080935/7d9c93cdbde6/IJPVM-16-20-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/006a/12080935/ddb32c73c49a/IJPVM-16-20-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/006a/12080935/7ff36dd11364/IJPVM-16-20-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/006a/12080935/d63749ebe1c2/IJPVM-16-20-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/006a/12080935/7d9c93cdbde6/IJPVM-16-20-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/006a/12080935/ddb32c73c49a/IJPVM-16-20-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/006a/12080935/7ff36dd11364/IJPVM-16-20-g004.jpg

相似文献

[1]
Detecting the Seasonal and Spatial Patterns of COVID-19 Hospitalization and Deaths in Iran: Insights from a Spatiotemporal and Hotspot Analysis.

Int J Prev Med. 2025-4-24

[2]
Integration of Moran's I, geographically weighted regression (GWR), and ordinary least square (OLS) models in spatiotemporal modeling of COVID-19 outbreak in Qom and Mazandaran Provinces, Iran.

Model Earth Syst Environ. 2023-2-15

[3]
Spatial analysis of COVID-19 incidence and mortality rates in northwest iran for future epidemic preparedness.

Sci Rep. 2025-3-3

[4]
High-risk spatiotemporal patterns of cutaneous leishmaniasis: a nationwide study in Iran from 2011 to 2020.

Infect Dis Poverty. 2023-5-15

[5]
Geospatial dynamics of COVID-19 clusters and hotspots in Bangladesh.

Transbound Emerg Dis. 2021-11

[6]
Spatiotemporal analysis of the morbidity of global Omicron from November 2021 to February 2022.

J Med Virol. 2022-11

[7]
A description of spatial-temporal patterns of the novel COVID-19 outbreak in the neighbourhoods' scale in Tehran, Iran.

Med J Islam Repub Iran. 2021-10-4

[8]
Spatial analysis of COVID-19 spread in Iran: Insights into geographical and structural transmission determinants at a province level.

PLoS Negl Trop Dis. 2020-11-18

[9]
Exploring the geospatial epidemiology of breast cancer in Iran: identifying significant risk factors and spatial patterns for evidence-based prevention strategies.

BMC Cancer. 2023-12-11

[10]
Spatio-temporal patterns of dengue in Bangladesh during 2019 to 2023: Implications for targeted control strategies.

PLoS Negl Trop Dis. 2024-9-20

本文引用的文献

[1]
Covid-19: WHO declares end of global health emergency.

BMJ. 2023-5-9

[2]
Efficacy and Safety of a Protein-Based SARS-CoV-2 Vaccine: A Randomized Clinical Trial.

JAMA Netw Open. 2023-5-1

[3]
Genomic surveillance of SARS-CoV-2 strains circulating in Iran during six waves of the pandemic.

Influenza Other Respir Viruses. 2023-4

[4]
Assessing COVID-19 pandemic policies and behaviours and their economic and educational trade-offs across US states from Jan 1, 2020, to July 31, 2022: an observational analysis.

Lancet. 2023-4-22

[5]
Effectiveness assessment of non-pharmaceutical interventions: lessons learned from the COVID-19 pandemic.

Lancet Public Health. 2023-4

[6]
The COVID-19 pandemic's true death toll in Iran after two years: an interrupted time series analysis of weekly all-cause mortality data.

BMC Public Health. 2023-3-7

[7]
The Dynamic Effective Reproductive Number of COVID-19 during the Epidemic in Iran.

Iran J Public Health. 2022-4

[8]
A systematic review of COVID-19 transport policies and mitigation strategies around the globe.

Transp Res Interdiscip Perspect. 2022-9

[9]
A description of spatial-temporal patterns of the novel COVID-19 outbreak in the neighbourhoods' scale in Tehran, Iran.

Med J Islam Repub Iran. 2021-10-4

[10]
Mapping and Spatial Pattern Analysis of COVID-19 in Central Iran Using the Local Indicators of Spatial Association (LISA).

BMC Public Health. 2021-12-8

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