Spatiotemporal Surveillance of COVID-19 Based on Epidemiological Features: Evidence from Northeast Iran

Mohammad Tabasi, Ali Asghar Alesheikh, Elnaz Babaie and Javad Hatamiafkoueieh ()
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Mohammad Tabasi: Department of GIS, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran
Ali Asghar Alesheikh: Department of GIS, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran
Elnaz Babaie: Department of GIS, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran
Javad Hatamiafkoueieh: Department of Mechanics and Control Processes, Academy of Engineering, Peoples’ Friendship University of Russia (RUDN University), Miklukho-Maklaya Str. 6, Moscow 117198, Russia

Sustainability, 2022, vol. 14, issue 19, 1-15

Abstract: Spatiotemporal analysis of COVID-19 cases based on epidemiological characteristics leads to more refined findings about health inequalities and better allocation of medical resources in a spatially and timely fashion. While existing literature has explored the spatiotemporal clusters of COVID-19 worldwide, little attention has been paid to investigate the space-time clusters based on epidemiological features. This study aims to identify COVID-19 clusters by epidemiological factors in Golestan province, one of the highly affected areas in Iran. This cross-sectional study used GIS techniques, including local spatial autocorrelations, directional distribution statistics, and retrospective space-time Poisson scan statistics. The results demonstrated that Golestan has been facing an upward trend of epidemic waves, so the case fatality rate (CFR) of the province was roughly 2.5 times the CFR in Iran. Areas with a more proportion of young adults were more likely to generate space-time clusters. Most high-risk clusters have emerged since early June 2020. The infection first appeared in the west and southwest of the province and gradually spread to the center, east, and northeast regions. The results also indicated that the detected clusters based on epidemiological features varied across the province. This study provides an opportunity for health decision-makers to prioritize disease-prone areas and more vulnerable populations when allocating medical resources.

Keywords: health inequality; COVID-19; epidemiological features; spatiotemporal dynamics; GIS (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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