Background Zoonotic cutaneous leishmaniasis (ZCL) is a neglected tropical disease worldwide, especially the Middle East. Although previous works attempt to model the ZCL spread using various environmental factors, the interactions between vectors (Phlebotomus papatasi), reservoir hosts, humans, and the environment can affect its spread. Considering all of these aspects is not a trivial task. Methods An agent-based model (ABM) is a relatively new approach that provides a framework for analyzing the heterogeneity of the interactions, along with biological and environmental factors in such complex systems. The objective of this research is to design and develop an ABM that uses Geospatial Information System (GIS) capabilities, biological behaviors of vectors and reservoir hosts, and an improved Susceptible-Exposed-Infected-Recovered (SEIR) epidemic model to explore the spread of ZCL. Various scenarios were implemented to analyze the future ZCL spreads in different parts of Maraveh Tappeh County, in the northeast region of Golestan Province in northeastern Iran, with alternative socio-ecological conditions. Results The results confirmed that the spread of the disease arises principally in the desert, low altitude areas, and riverside population centers. The outcomes also showed that the restricting movement of humans reduces the severity of the transmission. Moreover, the spread of ZCL has a particular temporal pattern, since the most prevalent cases occurred in the fall. The evaluation test also showed the similarity between the results and the reported spatiotemporal trends. Conclusions This study demonstrates the capability and efficiency of ABM to model and predict the spread of ZCL. The results of the presented approach can be considered as a guide for public health management and controlling the vector population.

Background: Seatbelts are a relatively low-cost safety device that provides easy basic protection for occupants of 4-wheeled vehicles. Objectives: This study investigates frequency of seatbelt use and its related factors among drivers involved in a vehicle crash. Materials and Methods:In this cross-sectional study, all crash profiles recorded in a province from March 2010 to March 2011 were reviewed. Necessary information was extracted from crash reports in which at least one 4-wheeled vehicle was involved. Data were analyzed using binary and multinomial logistic regression. Results: Of a total of 1427 motor vehicle crashes, a seatbelt was used by 58.2% of drivers. In the univariate analysis, the following were significantly associated with seatbelt use: driver age, education, and occupation along with front seat passenger's sex and seatbelt use, type and make of vehicle, speed, road surface condition, and type of road. In the multivariate model, the following remained significant: driver education, seatbelt use by front seat passenger, and type of road. Furthermore, a restraining seatbelt protected drivers from severe injury and death. Unbelted drivers were 7 and 17.4 times more likely to experience injury and death respectively than belted drivers. Conclusions: The seatbelt wearing rate among the study participants was much lower than the 90% rate reported among Iranian drivers in 2010. Mandating seatbelt use, as in most countries, will be more effective if a combination of factors such as changes in vehicle design, road safety, and driver and passenger behavior are taken into account.

Zoonotic Cutaneous Leishmaniasis (ZCL) is one of the most prevalent zoonoses in Iran, especially in its central and northeast parts. This research aims to examine if there are spatiotemporal clusters of the ZCL cases, and if so, whether there are disparities in clustering according to age, gender, home situation, and occupation. The spatial analysis, including global and local spatial autocorrelations, inverse distance weighting, and space-time scan statistics were applied to determine potential clusters in Golestan villages during 2011-2016. Several spatially significant (p < 0.05) clusters were observed in the north and the northeastern regions, where most of them persisted for the last years of the study period. Children (0-10 years) living in rural settings were more likely to have the infection than those living in other areas. Despite the focus of the disease in the northern regions, housekeepers, females, and patients aged 21-30 and 41-50 years were found to be the high-risk groups in the southern areas. The seasonal pattern indicates that the outbreak mainly begins in late summer, peaks in October, and diminishes in December. By exploring spatiotemporal variations of ZCL by sociodemographic information, this study can identify priority areas for health decision-makers and resource allocation.

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This study is dedicated to modeling the spatial variation in COVID-19 prevalence using the adaptive neuro-fuzzy inference system (ANFIS) when dealing with nonlinear relationships, especially useful for small areas or small sample size problems. We compiled a broad range of socio-demographic, environmental, and climatic factors along with potentially related urban land uses to predict COVID-19 prevalence in rural districts of the Golestan province northeast of Iran with a very high-case fatality ratio (9.06%) during the first year of the pandemic (2020–2021). We also compared the ANFIS and principal component analysis (PCA)-ANFIS methods for modeling COVID-19 prevalence in a geographical information system framework. Our results showed that combined with the PCA, the ANFIS accuracy significantly increased. The PCA-ANFIS model showed a superior performance (R2 (determination coefficient) = 0.615, MAE (mean absolute error) = 0.104, MSE (mean square error) = 0.020, and RMSE (root mean square error) = 0.139) than the ANFIS model (R2 = 0.543, MAE = 0.137, MSE = 0.034, and RMSE = 0.185). The sensitivity analysis of the ANFIS model indicated that migration rate, employment rate, the number of days with rainfall, and residential apartment units were the most contributing factors in predicting COVID-19 prevalence in the Golestan province. Our findings indicated the ability of the ANFIS model in dealing with nonlinear parameters, particularly for small sample sizes. Identifying the main factors in the spread of COVID-19 may provide useful insights for health policymakers to effectively mitigate the high prevalence of the disease.