The East Africa region is amongst the most food insecure areas of the world and the number of crises affecting the East Africa region will likely continue to increase due to the effects of climate change. Multiple calls have been issued for better preparedness, early warning, and early action to reduce the scale of food insecurity. A recent study on food security information systems in East Africa noted that despite years of attention, the link from early warning-early action (EW-EA) is not as effective as it could be.
New technologies involving remote sensing, satellite imagery, computational modeling, and artificial intelligence are all competing to improve early warning and humanitarian information systems, but it is not always clear why these new technologies are being developed or what needs they fill.
This study was commissioned by the FAO Subregional Office for East Africa to examine the links between early warning and early action in East Africa and what role(s) predictive analytics and machine learning can play in supporting EW-EA. This study reviews existing systems and new trends in predictive modeling to make recommendations to FAO, to IGAD, and to IGAD member states.