Multi-disease surveillance, prediction and early warning
In Africa, 94% of global malaria, TB and HIV cases occur annually, totaling more than 300 million cases. Children under 5 make up 80% of these cases. Additionally, 85% of African countries are impacted by climate-related issues. Conversely, 35% of these countries lack integrated disease surveillance systems, leading to an average 4-week delay in reporting cases. Climate change contributes to a tenfold increase in malaria cases, resulting in a loss of 2.1 billion working hours. A climate-aware solution has been developed to address the malaria, TB and HIV crisis by using AI for disease surveillance, outbreak prediction, and early warnings. This system offers real-time analytics, improved outbreak prediction, seamless integration, and early warning capabilities. It aims to predict malaria outbreaks in advance and facilitate timely responses.
Artificial Intelligence And Digital Services
Artificial intelligence
1 year
Ethiopia, and other six African countries
1. Real-time disease Incidence Analytics and Outbreak Prediction - The solution uses high-tech, high-precision analytics, and predictive AI models to analyze and predict the existence of malaria/TB/HIV outbreaks. 2. Enhanced Climate-Effect Prediction - The system utilizes predictive AI models to predict malaria-critical climate variables and their impact on malaria distribution. 3. Disease Outbreak Early Warning - The solution warns of the probable occurrence of a malaria/TB/HIV outbreak at least 4 months ahead based on the disease prediction results.
UNDP's digital innovation 2024 Award in Ethiopia
N.A.
