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. Then, PCs were used to train the ANFIS model. To evaluate the proposed approaches in model assessment phase, we used test data in 2016. In this phase, we showed that PCA-ANFIS model with values ​​of R2 = 0.791, MAE = 0.681, RMSE = 0.904 compared to ANFIS model with values ​​of R2 = 0.705, MAE = 0.827, RMSE = 1.073 has better performance in prediction of the ZCL cases. Actual and predicted maps of ZCL cases in 2016 by both models demonstrated that the high-risk regions of the disease are located in the northeastern, northern parts, and some central rural districts of Golestan province. Sensitivity analysis of the ANFIS model showed that population, vegetation, average wind speed, elevation, and average soil temperature, respectively, are the most significant factors in predicting the ZCL cases. The findings indicated the importance of machine learning (ML) techniques (ANFIS and PCA-ANFIS) in medical geography studies. By using these approaches, with less cost and shorter time, high-risk areas of diseases can be predicted, and the most effective factors on the spatial prediction of diseases can be identified. Public health policymakers can use these useful tools to control and prevent the disease and to allocate resources to disease-prone areas.

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). We applied the Getis-Ord Gi* statistic to identify areas with high and low risk of the disease. To investigate the relationships between the factors affecting the incidence of HB as input variables together and the factors with the log (number of HB cases) as an output variable, we used the statistical linear regression model and the Pearson correlation coefficient. Then, we implemented a GIS-based adaptive neuro-fuzzy inference system (ANFIS) with two subtractive clustering and fuzzy c-means (FCM) clustering methods to model and predict the spatial distribution of HB. RESULTS: Global Moran's I spatial autocorrelation analysis indicated that the type of HB distribution is clustered in all years except 2014 and 2017, which are random. According to the Getis-Ord Gi* analysis, the location of the hot spots varied during 2012-2018. In 2012 and 2013, most of the hot spots were seen in the west of the province. While in 2018, they were mostly concentrated in the eastern regions of the province. The linear regression model indicated that the parameters affecting the incidence of HB are independent of each other and can explain only 25.3% of the total changes in the log (number of HB cases). The results of the Pearson correlation coefficient showed that there were positive relationships between vegetation, log (population), and the number of sheep and cattle (p-value < 0.05). The above-mentioned factors had the strongest positive correlation with the log (number of HB cases) (p-value < 0.01). These results may be due to the fact that vegetation regions are suitable for livestock grazing, attracting large crowds of people. Therefore, this will increase HB cases. We compared the results of subtractive clustering and FCM clustering methods by evaluation criteria (e.g., linear correlation coefficient (LCC) and mean absolute error (MAE)) in two phases of development and assessment of the ANFIS model. In the assessment phase, we predicted the spatial distribution of log (number of HB cases) in 2017 and 2018 by subtractive clustering (R2 = 0.699, LCC or R = 0.692, MAE = 0.509, MSE = 0.455) and by FCM clustering (R2 = 0.704, LCC or R = 0.697, MAE = 0.512, MSE = 0.448) that showed FCM clustering outperformed the subtractive clustering. CONCLUSION: The findings may have important implications for public health. The emergence of the hot spots in the east of the province can be a warning to the health system. Health authorities can use the findings of this study to predict the spread of HB and perform HB prevention programs. They can also investigate the factors affecting the prevalence of the disease, identify high-risk areas, and ultimately allocate resources to high-risk regions.

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. Several spatially significant (p < 0.05) clusters were observed in the north and northeastern regions, and most persisted until the last year of the study period. Children (0-10 years) living in rural settings were more likely to have an infection than those living in other areas. Although the disease was centered 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 indicated that the outbreak mainly began in late summer, peaked in October, and diminished in December. By exploring spatiotemporal variations of ZCL by sociodemographic information, this study was able to identify priority areas for decision-makers in healthcare and resource allocation.

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. 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 .