Decision making environment on Rift Valley fever in Ferlo (Senegal)

Area of Science:

• Veterinary Epidemiology
• Environmental Science
• Data Mining

Background:

• Rift Valley fever (RVF) is an anthropozoonosis first identified in Kenya in 1912.
• RVF is prevalent in tropical regions, with Senegal's Ferlo area being particularly affected.
• The Ferlo area features numerous ponds shared by humans, livestock, and vectors, increasing transmission risk.

Purpose of the Study:

• To develop a decision-making model for evaluating the impacts and interactions of environmental factors on RVF.
• To facilitate easier and more effective RVF monitoring and control strategies.
• To identify relationships between environmental variables and RVF transmission vectors.

Main Methods:

• Utilized data mining techniques to build a predictive model.
• Integrated diverse datasets, including environmental data and pond characteristics.
• Focused on the Ferlo region of Senegal for analysis.

Main Results:

• The proposed model aids in understanding the complex interplay of environmental factors in RVF outbreaks.
• Demonstrated the utility of data mining for analyzing spatial-temporal epidemiological data.
• Highlighted the importance of pond characteristics in relation to RVF vector presence.

Conclusions:

• The data mining approach offers advantages for RVF epidemiological monitoring.
• The model can assist trade experts in making informed decisions regarding RVF management.
• Understanding environmental drivers is crucial for mitigating RVF spread.