Spatial Modeling and Risk Mapping of Human Fascioliasis in Iran: A Geographical Information System-Based Survey from 2013 - 2022

Background: Human fascioliasis, a growing health concern, has increased significantly over the last few decades and serves as a notable example of emerging and re-emerging disease foci in numerous countries. Objectives: This study aimed to evaluate the incidence, spatial distribution, and hotspot regions of Fasciola infection in Iran using geographic information system (GIS) analyses from 2013 to 2022. Methods: Data on Fasciola cases and populations at risk across various provinces were obtained from the Ministry of Health and Medical Education, Tehran, Iran, and other relevant organizations for the years 2013 to 2022. A map illustrating the geographical distribution of fascioliasis was generated. Spatial analyses were conducted using ArcGIS 10.5 software to identify hotspot regions of fascioliasis in Iran. The correlation between temperature, relative humidity, the normalized difference vegetation index (NDVI), and the incidence of fascioliasis (variables influencing the disease) was assessed using geographically weighted regression (GWR) in ArcGIS 10.5. Data analysis was performed using linear regression and SPSS version 21 software. Results: Hotspot provinces for fascioliasis were identified in Gilan, Kermanshah, and Khorramabad. Geographically weighted regression analysis revealed a high correlation between humidity, temperature, vegetation density, and the incidence of Fasciola infection in the provinces of Gilan, Kermanshah, Khorramabad, Kurdistan, Semnan, and South Khorasan (P-value = 0.025). Conclusions: This study demonstrated a significant relationship between relative humidity, mean annual temperature, NDVI, and the incidence of Fasciola infection in Iran. Geographic information system analysis proved to be an effective tool for identifying risk factors and assessing endemic areas of fascioliasis within specific regions.