New machine learning method provides real-time estimates of local food prices in crisis-affected areas

The recent global surge in inflation has impacted livelihoods around the world, particularly in crisis-affected areas. This additional shock has significantly affected households that were already living in fragile situations. However, in many crisis situations, where conflict may make markets inaccessible, detailed price data is not regularly collected. These disruptions often coincide with periods and locations of high price instability. What if relief agencies could monitor food prices in real-time using alternative methods, even in remote locations during situations of conflict and violence? The information could identify the early onset or worsening of food crises, guide response efforts, and estimate the necessary response magnitude.