Title: AI and Hybrid modeling for Community-based early detection of zoonotic disease in the context of climate change in Senegal
Sylvain Landry Faye
The general objective of this project is: To enhance the epidemiological surveillance system in Senegal by developing and testing a community-based, gender-sensitive early detection and warning systems for zoonotic diseases using AI, data science and a One Health approach.
1. To identify with communities and policy-makers the factors that influence infectious diseases in Senegal, particularly in relation to climate change and ecological upheaval.
2. To establish strong community-based surveillance systems for the early detection and reporting of zoonotic diseases in Senegal based on equitable partnership and community engagement.
3. To develop AI and data science applications based on local realities and contextual data that can rapidly detect, predict, and respond to zoonotic disease outbreaks in Senegal.
4. To ensure that the AI and data science applications are sensitive to local realities, including ecological, geographical, medical, economic, and sociocultural factors, to reduce the risk of exclusion and discrimination.
5. To evaluate the effectiveness of the AI and data science applications in improving the early detection and prevention of zoonotic diseases in Senegal.