Background: The Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) emerged initially in China in December 2019 causing the COVID-19 disease, which quickly spread worldwide. Iran was one of the first countries outside China to be affected in a major way and is now under the spell of a fourth wave. This study aims to investigate the epidemiological characteristics of COVID-19 cases in north-eastern Iran through mapping the spatiotemporal trend of the disease. Methods: The study comprises data of 4000 patients diagnosed by laboratory assays or clinical investigation from the beginning of the disease on Feb 14, 2020, until May 11, 2020. Epidemiological features and spatiotemporal trends of the disease in the study area were explored by classical statistical approaches and Geographic Information Systems. Results: Most common symptoms were dyspnoea (69.4%), cough (59.4%), fever (54.4%) and weakness (19.5%). Approximately 82% of those who did not survive suffered from dyspnoea. The highest Case Fatality Rate (CFR) was related to those with cardiovascular disease (27.9%) and/or diabetes (18.1%). Old age (≥60 years) was associated with an almost five-fold increased CFR. Odds Ratio (OR) showed malignancy (3.8), nervous diseases (2.2), and respiratory diseases (2.2) to be significantly associated with increased CFR with developments, such as hospitalization at the ICU (2.9) and LOS (1.1) also having high correlations. Furthermore, spatial analyses revealed a geographical pattern in terms of both incidence and mortality rates, with COVID-19 first being observed in suburban areas from where the disease swiftly spread into downtown reaching a peak between 25 February to 06 March (4 incidences per km²). Mortality peaked 3 weeks later after which the infection gradually decreased. Out of patients investigated by the spatiotemporal approach (n = 727), 205 (28.2%) did not survive and 66.8% of them were men. Conclusions: Older adults and people with severe co-morbidities were at higher risk for developing serious complications due to COVID-19. Applying spatiotemporal methods to identify the transmission trends and high-risk areas can rapidly be documented, thereby assisting policymakers in designing and implementing tailored interventions to control and prevent not only COVID-19 but also other rapidly spreading epidemics/pandemics.

There are different area-based factors affecting the COVID-19 mortality rate in urban areas. This research aims to examine COVID-19 mortality rates and their geographical association with various socioeconomic and ecological determinants in 350 of Tehran’s neighborhoods as a big city. All deaths related to COVID-19 are included from December 2019 to July 2021. Spatial techniques, such as Kulldorff’s SatScan, geographically weighted regression (GWR), and multi-scale GWR (MGWR), were used to investigate the spatially varying correlations between COVID-19 mortality rates and predictors, including air pollutant factors, socioeconomic status, built environment factors, and public transportation infrastructure. The city’s downtown and northern areas were found to be significantly clustered in terms of spatial and temporal high-risk areas for COVID-19 mortality. The MGWR regression model outperformed the OLS and GWR regression models with an adjusted R² of 0.67. Furthermore, the mortality rate was found to be associated with air quality (e.g., NO₂, PM₁₀, and O₃); as air pollution increased, so did mortality. Additionally, the aging and illiteracy rates of urban neighborhoods were positively associated with COVID-19 mortality rates. Our approach in this study could be implemented to study potential associations of area-based factors with other emerging infectious diseases worldwide.

Objectives: Homicide rate is associated with a large variety of factors and therefore unevenly distributed over time and space. This study aims to explore homicide patterns and their spatial associations with different socioeconomic and built-environment conditions in 140 neighbourhoods of the city of Toronto, Canada.Methods: A homicide dataset covering the years 2012 to 2021 and neighbourhood-based indicators were analysed using spatial techniques such as Kernel Density Estimation, Global/Local Moran’sIand Kulldorff’s SatScan spatio-temporal methodology. Geographically weighted regression (GWR) and multi-scale GWR (MGWR) were used to analyse the spatially varying correlations between the homicide rate and independent variables. The latter was particularly suitable for manifested spatial variations between explanatory variables and the homicide rate and it also identified spatial non-stationarities in this connection.Results: The adjusted R²of the MGWR was 0.53, representing a 4.35 and 3.74% increase from that in the linear regression and GWR models, respectively. Spatial and spatio-temporal high-risk areas were found to be significantly clustered in downtown and the north-western parts of the city. Some variables (e.g., the population density, material deprivation, the density of commercial establishments and the density of large buildings) were significantly associated with the homicide rate in different spatial ways.Conclusion: The findings of this study showed that homicide rates were clustered over time and space in certain areas of the city. Socioeconomic and the built environment characteristics of some neighbourhoods were found to be associated with high homicide rates but these factors were different for each neighbourhood.

Objectives: Hypertension is a prevalent chronic disease globally. A multifaceted combination of risk factors is associated with hypertension. Scientific literature has shown the association among individual and environmental factors with hypertension, however, a comprehensive database including demographic, environmental, individual attributes and nutritional status has been rarely studied. Moreover, an integrated spatial-epidemiological approach has been scarcely researched. Therefore, this study aims to provide and describe a geodatabase including individual-based and socio-environmental data related to people living in the city of Mashhad, Iran in 2018. Data description: The database has been extracted from the PERSIAN Organizational Cohort study in Mashhad University of Medical Sciences. The data note includes three shapefiles and a help file. The shapefile format is a digital vector storage format for storing geometric location and associated attribute information. The first shapefile includes the data of population, air pollutants and amount of available green space for each census block of the city. The second shapefile consists of aggregated blood pressure data to the census blocks of the city. The third shapefile comprises the individual characteristics data (i.e., demographic, clinical, and lifestyle). Finally, the fourth file is a guide to the previous data files for users.

Background: The lives of babies and mothers are at risk due to the uneven distribution of healthcare facilities required for emergency cesarean sections (CS). However, CS without medical indications might cause complications for mothers and babies, which is a global health problem. Identifying spatiotemporal variations of CS rates in each geographical area could provide helpful information to understand the status of using CS services. Methods: This cross-sectional study explored spatiotemporal patterns of CS in northeast Iran from 2016 to 2020. Space–time scan statistics and spatial interaction analysis were conducted using geographical information systems to visualize and explore patterns of CS services. Results: The temporal analysis identified 2017 and 2018 as the statistically significant high clustered times in terms of CS rate. Five purely spatial clusters were identified that were distributed heterogeneously in the study region and included 14 counties. The spatiotemporal analysis identified four clusters that included 13 counties as high-rate areas in different periods. According to spatial interaction analysis, there was a solid spatial concentration of hospital facilities in the political center of the study area. Moreover, a high degree of inequity was observed in spatial accessibility to CS hospitals in the study area. Conclusions: CS Spatiotemporal clusters in the study area reveal that CS use in different counties among women of childbearing age is significantly different in terms of location and time. This difference might be studied in future research to identify any overutilization of CS or lack of appropriate CS in clustered counties, as both put women at risk. Hospital capacity and distance from population centers to hospitals might play an essential role in CS rate variations and spatial interactions among people and CS facilities. As a result, some healthcare strategies, e.g., building new hospitals and empowering the existing local hospitals to perform CS in areas out of service, might be developed to decline spatial inequity.

Objective: In March 2020, Iran tackled the first national wave of COVID-19 that was particularly felt in Mashhad, Iran’s second-most populous city. Accordingly, we performed a spatio-temporal study in this city to investigate the epidemiological aspects of the disease in an urban area and now wish to release a comprehensive dataset resulting from this study.Data description: These data include two data files and a help file. Data file 1: “COVID-19_Patients_Data” contains the patient sex and age + time from symptoms onset to hospital admission; hospitalization time; co-morbidities; manifest symptoms; exposure up to 14 days before admission; disease severity; diagnosis (with or without RT-PCR assay); and outcome (recovery vs. death). The data covers 4000 COVID-19 patients diagnosed between 14 Feb 2020 and 11 May 2020 in Khorasan-Razavi Province. Data file 2: “COVID-19_Spatiotemporal_Data” is a digital map of census tract divisions of Mashhad, the capital of the province, and their population by gender along with the number of COVID-19 cases and deaths including the calculated rates per 100,000 persons. This dataset can be a valuable resource for epidemiologists and health policymakers to identify potential risk factors, control and prevent pandemics, and optimally allocate health resources.