Modeling schistosomiasis spatial risk dynamics over time in Rwanda using zero-inflated Poisson regression.

Area of Science:

• Epidemiology
• Spatial Analysis
• Public Health

Background:

• Schistosomiasis mansoni (S. mansoni) surveillance data often exhibit excessive zero-records, indicating potential underreporting or true absence of infection.
• Standard statistical methods struggle to address data insufficiency, hindering accurate risk assessment.
• Understanding spatiotemporal variations in S. mansoni risk is crucial for effective control strategies.

Purpose of the Study:

• To develop and apply a zero-inflated Poisson model for analyzing S. mansoni risk at a fine spatial scale.
• To identify environmental factors influencing S. mansoni transmission.
• To project future S. mansoni infection risk for the year 2050.

Main Methods:

• Utilized a zero-inflated Poisson model to account for excess zero-records in clinical S. mansoni data.
• Incorporated environmental data at the primary health facility service area level as explanatory variables.
• Employed spatial and temporal analysis to explore risk variations and project future risk.

Main Results:

• The zero-inflated Poisson model revealed a significant increase in S. mansoni relative risk over a decade.
• Identified key risk factors: rice cultivation (69% higher risk), proximity to rice farms (29% higher risk), and proximity to water bodies (50% higher risk).
• Forecasted risk maps indicate persistent and emerging areas of high S. mansoni infection risk by 2050.

Conclusions:

• The developed model effectively addresses data limitations in schistosomiasis surveillance.
• Environmental factors, particularly those related to wetland ecosystems and agriculture, significantly drive S. mansoni transmission.
• Prediction and forecasting maps are vital tools for targeted schistosomiasis control and public health planning in Rwanda.