Spatial epidemiology of COVID-19 infection through the first outbreak in the city of Mashhad, Iran

Abstract

The COVID-19 epidemic is currently the most important public health challenge worldwide. The current study aimed to survey the spatial epidemiology of the COVID-19 outbreak in Mashhad, Iran, across the first outbreak. The data was including the hospitalized lab-confirmed COVID-19 cases from Feb 4 until Apr 13, 2020. For comparison between the groups, classical statistics analyses were used. A logistic regression model was built to detect the factors affecting mortality. After calculating the empirical Bayesian rate (EBR), the Local Moran’s I statistic was applied to quantify the spatial autocorrelation of disease. The total cumulative incidence and case fatality rates were respectively 4.6 per 10,000 (95% CI: 4.3–4.8) and 23.1% (95% CI: 23.2–25.4). Of 1535 cases, 62% were males and were more likely to die than females (adjusted Odds Ratio (aOR): 1.58, 95% CI: 1.23–2.04). The odds of death for patients over 60 years was more than three times (aOR: 3.66, 95% CI: 2.79–4.81). Although the distribution of COVID-19 patients was nearly random in Mashhad, the downtown area had the most significant high-high clusters throughout most of the biweekly periods. The most likely factors influencing the development of hotspots around the downtown include the congested population (due to the holy shrine), low socioeconomic and deprived neighborhoods, poor access to health facilities, indoor crowding, and further use of public transportation. Constantly raising public awareness, emphasizing social distancing, and increasing the whole community immunization, particularly in the high-priority areas detected by spatial analysis, can lead people to a brighter picture of their lives.