Spatiotemporal analysis, hotspot mapping, and clustering of confirmed cases of COVID-19 in the initial phase of the pandemic in Qom province, Iran

Abstract

Objective: To identify the incidence rate, relative risk, hotspot regions and incidence trend of COVID-19 in Qom province, northwest part of Iran in the first stage of the pandemic.Methods: The study included 1 125 officially reported PCR-confirmed cases of COVID-19 from 20 February 2020 to 20 April 2020 in 90 regions in Qom city, Iran. The Bayesian hierarchical spatial model was used to model the relative risk of COVID-19 in Qom city, and the segmented regression model was used to estimate the trend of COVID-19 incidence rate. The Poisson distribution was applied for the observed number of COVID-19, and independent Gamma prior was used for inference on log-relative risk parameters of the model.Results: The total incidence rate of COVID-19 was estimated at 89.5 per 100 000 persons in Qom city (95% CI: 84.3, 95.1). According to the results of the Bayesian hierarchical spatial model and posterior probabilities, 43.33% of the regions in Qom city have relative risk greater than 1; however, only 11.11% of them were significantly greater than 1. Based on Geographic Information Systems (GIS) spatial analysis, 10 spatial clusters were detected as active and emerging hotspot areas in the south and central parts of the city. The downward trend was estimated 10 days after the reporting of the first case (February 7, 2020); however, the incidence rate was decreased by an average of 4.24% per day (95%CI:-10.7, -3.5).Conclusions: Spatial clusters with high incidence rates of COVID-19 in Qom city were in the south and central regions due to the high population density. The GIS could depict the spatial hotspot clusters of COVID-19 for timely surveillance and decision-making as a way to contain the disease.