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.

Background: Since December 2019, SARS-CoV-2 infection has converted to a severe threat to global health. It is now considered as the fifth worldwide pandemic problem. This study aims to explore spatial-time distribution of COVID-19 in the first outbreak of COVID-19 in the second major city of Iran (Mashhad). The results will pave the way for better tracking of COVID-19.Methods: Data were collected from two tertiary hospitals in Mashhad in June 2020. They included demographic findings and residential address of the patients with confirmed COVID-19 disease by polymerase chain reaction test. The univariate logistic regression model was used to assess the influence of age and sex on mortality. For spatial-time analysis, after calculating empirical Bayesian rate for every neighborhood, the local Moran's I statistic was used to quantify spatial autocorrelation of COVID-19 frequency at the city neighborhood level.Results: Of 1,535 confirmed cases of COVID-19 included in this study, 951 (62%) were male. Odds of death for patients over 60 years of age was more than three times higher (odds ratio [OR]: 3.7, CI [2.8-4.8]) than for those under the 60 years. In addition, the ratio of relative mortality for male patients was significantly higher than the female (OR: 1.58, CI [1.2-2]). The univariate regression model also revealed that odds of death increased along with increase in duration of hospitalization secondary to COVID-19 disease (OR: 1.02, IQR [1.01-1.02]). The downtown area had a significant high-high cluster throughout much of the study period (March-May 2020). Conclusions: Collection of geographic information system (GIS) map data on SARS-CoV-2 provides insight into clusters of infection and high risk places for COVID-19 transmission. GIS-