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Madadizadeh F, Sefidkar R. Ranking and Clustering Iranian Provinces Based on COVID-19 Spread: K-Means Cluster Analysis. J Environ Health Sustain Dev 2021; 6 (1) :1184-1195
URL: http://jehsd.ssu.ac.ir/article-1-280-en.html

Introduction: The Coronavirus has crossed geographical borders. This study was performed to rank and cluster Iranian provinces based on coronavirus disease (COVID-19) recorded cases from February 19 to March 22, 2020.
Materials and Methods: This cross-sectional study was conducted in 31 provinces of Iran using the daily number of confirmed cases. Cumulative Frequency (CF) and Adjusted CF (ACF) of new cases for each province were calculated. Characteristics of provinces like population density, area, distance from the original epicenter (Qom province), altitude from sea level, and Human Development Index (HDI) were used to investigate their correlation with ACF values. Spearman correlation coefficient and K-Means Cluster Analysis (KMCA) were used for data analysis. Statistical analyses were conducted in RStudio. The significant level was set at 0.05.
Results: There were 21,638 infected cases with COVID-19 in Iran during the study period. Significant correlations between ACF values and province HDI (r = 0.46) and distance from the original epicenter (r = -0.66) was observed. KMCA, based on both CF and ACF values, classified provinces into 10 clusters. In terms of ACF, the highest level of spreading belonged to cluster 1 (Semnan and Qom provinces), and the lowest one belonged to cluster 10 (Kerman, Sistan and Baluchestan, Chaharmahal and Bakhtiari and Busher provinces).
Conclusion: This study showed that ACF gives a real picture of each province's spreading status. KMCA results based on ACF identify the provinces that have critical conditions and need attention. Therefore, using this accurate model to identify hot spots to perform quarantine is recommended.