Spatial prediction of human brucellosis susceptibility using an explainable optimized adaptive neuro fuzzy inference system.

Brucellosis, a zoonotic disease caused by Brucella bacteria, poses significant risks to human, livestock, and wildlife health, alongside economic losses from livestock morbidity and mortality. This study improves Human Brucellosis Susceptibility Mapping (HBSM) by integrating the Adaptive Neuro-Fuzzy Inference System (ANFIS) with meta-heuristic algorithms, including Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Boruta-XGBoost identified key covariates, while VIF and tolerance tests addressed collinearity, and Shapley additive explanation (SHAP) values enhanced model interpretability.

Spatial and temporal patterns of urban air pollution in tehran with a focus on PM and associated pollutants.

Understanding the spatial and temporal dynamics of air pollutants is crucial for effective urban air pollution management. This study focuses on the temporal dynamics of air quality monitoring stations (AQMSs) and the association among air pollutants, particularly PM, in Tehran, Iran. Using time series clustering and the Copula model, we analyzed data from 2019 to 2022.

Semantic similarity is not enough: A novel NLP-based semantic similarity measure in geospatial context.

In this study, we addressed two primary challenges: firstly, the issue of domain shift, which pertains to changes in data characteristics or context that can impact model performance, and secondly, the discrepancy between semantic similarity and geographical distance. We employed topic modeling in conjunction with the BERT architecture. Our model was crafted to enhance similarity computations applied to geospatial text, aiming to integrate both semantic similarity and geographical proximity.

Analyzing socio-environmental determinants of bone and soft tissue cancer in Indonesia.

Background: This study is designed to explore the potential impact of individual and environmental residential factors as risk determinants for bone and soft tissue cancers, with a particular focus on the Indonesian context. While it is widely recognized that our living environment can significantly influence cancer development, there has been a notable scarcity of research into how specific living environment characteristics relate to the risk of bone and soft tissue cancers.

Methods: In a cross-sectional study, we analyzed the medical records of oncology patients treated at Prof.

Spatio-temporal modeling of human leptospirosis prevalence using the maximum entropy model.

Background: Leptospirosis, a zoonotic disease, stands as one of the prevailing health issues in some tropical areas of Iran. Over a decade, its incidence rate has been estimated at approximately 2.33 cases per 10,000 individuals.

Socioeconomic and environmental determinants of foot and mouth disease incidence: an ecological, cross-sectional study across Iran using spatial modeling.

Foot-and-mouth disease (FMD) is a highly contagious animal disease caused by a ribonucleic acid (RNA) virus, with significant economic costs and uneven distribution across Asia, Africa, and South America. While spatial analysis and modeling of FMD are still in their early stages, this research aimed to identify socio-environmental determinants of FMD incidence in Iran at the provincial level by studying 135 outbreaks reported between March 21, 2017, and March 21, 2018. We obtained 46 potential socio-environmental determinants and selected four variables, including percentage of population, precipitation in January, percentage of sheep, and percentage of goats, to be used in spatial regression models to estimate variation in spatial heterogeneity.

Disentangling the impacts of climate and land cover changes on habitat suitability of common pheasant Phasianus colchicus along elevational gradients in Iran.

Climate and land cover change are critical drivers of avian species range shift. Thus, predicting avian species' response to the land and climate changes and identifying their future suitable habitats can help their conservation planning. The common pheasant (Phasianus colchicus) is a species of conservation concern in Iran and is included in the list of Iran's protected avian species.

A spatiotemporal analysis of the impact of the COVID-19 outbreak on noise pollution in Tehran, Iran.

Noise pollution is one of the non-natural hazards in cities. Long-term exposure to this kind of pollution has severe destructive effects on human health, including mental illness, stress, anxiety, hormonal disorders, hypertension and therefore also cardiovascular disease. One of the primary sources of noise pollution in cities is transportation.

Spatial modeling of zoonotic cutaneous leishmaniasis with regard to potential environmental factors using ANFIS and PCA-ANFIS methods.

This study compares two adaptive neuro-fuzzy inference system (ANFIS) and principal component analysis (PCA)-ANFIS techniques for spatial modeling and forecasting of zoonotic cutaneous leishmaniasis (ZCL) cases in rural districts of Golestan province, Iran. We collected and prepared data on ZCL cases and climatic, topographic, vegetation, and human population factors. By applying the PCA algorithm, the parameters affecting the ZCL incidence were decomposed into principal components (PCs), and their dimensions were reduced.

Spatial prediction of human brucellosis (HB) using a GIS-based adaptive neuro-fuzzy inference system (ANFIS).

Objective: This study pursues three main objectives: 1) exploring the spatial distribution patterns of human brucellosis (HB); 2) identifying parameters affecting the disease spread; and 3) modeling and predicting the spatial distribution of HB cases in 2012-2016 and 2017-2018, respectively, in rural districts of Mazandaran province, Iran.

Methods: We collected data on the disease incidence, demography, ecology, climate, topography, and vegetation. Using the Global Moran's I statistic, we measured spatial autocorrelation between log (number of HB cases).

A spatio-temporal agent-based approach for modeling the spread of zoonotic cutaneous leishmaniasis in northeast Iran.

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.

Spatiotemporal Variability of Zoonotic Cutaneous Leishmaniasis Based on Sociodemographic Heterogeneity. The Case of Northeastern Iran, 2011-2016.

Zoonotic cutaneous leishmaniasis (ZCL) is one of the most prevalent zoonoses in Iran, especially in central and northeastern Iran. This research aimed to examine whether there were spatiotemporal clusters of ZCL cases, and if so, whether there were differences in clustering according to age, sex, area of residence, and occupation. Spatial analysis, including global and local spatial autocorrelations, inverse distance weighting, and space-time scan statistics, were used to determine potential clusters in the villages of Golestan from 2011-2016.

A ubiquitous asthma monitoring framework based on ambient air pollutants and individuals' contexts.

Air pollutants and allergens are the main stimuli that have considerable effects on asthmatic patients' health. Seamless monitoring of patients' conditions and the surrounding environment, limiting their exposure to allergens and irritants, and reducing the exacerbation of symptoms can aid patients to deal with asthma better. In this context, ubiquitous healthcare monitoring systems can provide any service to any user everywhere and every time through any device and network.

Experimental data of designing an optimal system for storage, collection and transfer of household waste in the GIS environment: A case study of Tehran, district 22, Iran.

This study was conducted to correctly manage the system of storage, collection and transfer of wastes in district 22, Tehran. After reviewing existing methods, an optimal system was designed in the GIS environment and appropriate solutions were suggested. Analytical Hierarchy Process (AHP) method was used.

Space-time epidemiology of Crimean-Congo hemorrhagic fever (CCHF) in Iran.

Iran, as an endemic country of Crimean-Congo hemorrhagic fever (CCHF), has been suffering from severe health issues and substantial economic burdens imposed by the disease. We analyzed monthly and yearly spatial and temporal distributions of CCHF to better understand the epidemiology of the disease in Iran. A cross-sectional survey was performed on 1027 recorded cases between 2000 and 2014.