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

Area of Science:

• Environmental epidemiology
• Spatial analysis
• Disease modeling

Background:

• Leptospirosis is a significant zoonotic disease in tropical Iran, with an incidence of 2.33 per 10,000.
• Understanding spatiotemporal patterns is crucial for public health policy and targeted interventions.

Purpose of the Study:

• To analyze spatiotemporal clustering of Leptospirosis in Iran.
• To develop a disease prevalence model using environmental factors.

Main Methods:

• Employed SaTScan and Maximum Entropy (MaxEnt) modeling.
• Utilized high-resolution (1km x 1km) environmental covariates including DEM, slope, water bodies, land cover, NDVI, precipitation, and temperature.
• Evaluated model accuracy using Area Under the Receiver Operating Characteristic Curve (AUC).

Main Results:

• Identified a significant primary cluster in western Gilan province (July 2013-July 2015).
• Detected four additional clusters near Someh Sara, Neka, Gorgan, and Rudbar.
• Risk mapping indicated potential disease expansion to western/northwestern regions. AUC values were 0.956 (training) and 0.952 (testing).
• Altitude and precipitation were primary determinants; slope and distance to water bodies had minimal influence.

Conclusions:

• Generated risk maps can enhance public awareness and inform effective Leptospirosis control policies.
• Maps aid in tracking disease incidence and directing interventions to high-risk areas.