MIRA – Machine Learning Food Security Forecasting for Anticipatory Action (Southern Malawi)

Overview

The Measurement Indicators for Resilience Analysis (MIRA) system is a machine-learning-enabled food security forecasting tool developed by Catholic Relief Services (CRS) in collaboration with Cornell University's Charles H. Dyson School of Applied Economics and Management, with subsequent research collaboration with Microsoft's AI for Good Lab. The system was designed to forecast near-term household food security outcomes up to approximately four months ahead, enabling anticipatory action, targeting of assistance, and early warning within humanitarian and social assistance programming in southern Malawi (Gholami et al., 2022, Data & Policy, Vol. 4).

MIRA was conceived and piloted within the context of the United in Building and Advancing Life Expectations (UBALE) programme, a five-year, USD 63 million USAID Food for Peace-funded Development Food Assistance Program managed by CRS-Malawi. UBALE served approximately 250,000 vulnerable households across 284 communities in three of Malawi's poorest and most disaster-prone districts: Chikwawa, Nsanje, and Rural Blantyre (CIO, 2019). The MIRA data collection protocol was officially launched in May 2016, beginning with a baseline evaluation, and was subsequently piloted over three months in Chikwawa District before expansion (CIO, 2019).

The MIRA protocol employs 'embedded enumerators' — community members residing in selected sentinel sites — who administer a high-frequency household survey on a monthly basis to the same panel of households. This longitudinal data collection approach captures information on demographics, livelihood and economic status, shock exposure history (including new shocks each month), food security outcomes measured through the reduced Coping Strategies Index (rCSI), household well-being indicators, and household capacities such as land assets, livestock ownership, education levels, and disability status (CIO, 2019). Surveyed households were originally selected using flood exposure maps from the Dartmouth Flood Observatory, and results were organised by flood risk groups and resilience variables tracking vulnerability to natural disasters (CIO, 2019).

The machine learning component of MIRA was developed through a research collaboration between CRS and Microsoft's AI for Good Lab. The peer-reviewed study published in Data & Policy (Gholami et al., 2022) benchmarked multiple supervised machine learning algorithms on the MIRA sentinel site data, including Random Forest, neural networks, and classical statistical models. The study used a binary classification approach to predict household food security outcomes, with the rCSI score dichotomised at a threshold of 16 to distinguish food-secure from food-insecure households. The Random Forest model outperformed other algorithms when focusing on predictors of community-level vulnerability. The best-performing model configuration achieved an F1 score of approximately 81 percent and an accuracy of approximately 83 percent in predicting food security outcomes, using historical food security scores combined with 20 predictor variables selected through an AI explainability framework (Gholami et al., 2022).

To ensure model interpretability, the researchers applied Shapley Additive Explanations (SHAP), a game-theoretic framework that quantifies the contribution of each predictor feature to the final prediction. SHAP analysis revealed that geographic location and self-reported welfare indicators were the most important predictors of food insecurity at the community level (Gholami et al., 2022). This cross-validation and model benchmarking approach, combined with SHAP-based feature importance analysis, provided a degree of interpretability and transparency to the forecasting outputs.

Operationally, early warning information generated through MIRA has been shared with local village development committees to guide community-level responses to emerging food security threats (CIO, 2019). MIRA data delivered to communities has enabled them to inform local government actors of problem hotspots, such as early reports of Fall Armyworm infestations, and has helped small-scale aid organisations, including church groups, to design localised interventions (Oxford Policy Management, 2022). During the COVID-19 pandemic, MIRA data was also used in research that revealed the disproportionate impact of the pandemic on already food-insecure households (Oxford Policy Management, 2022).

Microsoft awarded CRS an AI for Humanitarian Action Grant to develop a plug-and-play version of the MIRA solution, with the goal of enabling any CRS country programme to identify food insecurity drivers and pre-position aid more efficiently (CIO, 2019). The MIRA approach has been implemented or piloted in additional countries, including Madagascar and Ethiopia. CRS won a FutureEdge 50 Award for the MIRA system's emerging technology applications (CIO, 2019).

The system operates in a human-on-the-loop (HOTL) paradigm: model outputs inform decisions by programme staff and community actors, but there is no evidence of automatic rights-affecting actions being triggered by algorithmic outputs. The decision criticality is assessed as low, as the system provides informational and triage-level advisory analytics rather than determining individual benefit eligibility or entitlements. However, no public documentation of a formal Data Protection Impact Assessment, standard operating procedures, or governance artefacts specific to the MIRA Malawi implementation has been located.

Classification

Programme Details

UBALE was a five-year, USD 63 million USAID Food for Peace-funded Development Food Assistance Program managed by CRS-Malawi, serving approximately 250,000 vulnerable households across 284 communities in Chikwawa, Nsanje, and Rural Blantyre districts. MIRA was the high-frequency resilience monitoring and food security forecasting component embedded within UBALE.

Implementation Details

Risk & Oversight

Deployment & Outcomes

Sources

1. SRC-002-MWI-002 Florance, M. (2019) 'Catholic Relief Services Leverages Machine Learning to Fight Hunger', CIO, 7 March. Available at: https://www.cio.com/article/196076/catholic-relief-services-leverages-machine-learning-to-fight-hunger.html (Accessed: 24 March 2026).
   https://www.cio.com/article/196076/catholic-relief-services-leverages-machine-learning-to-fight-hunger.html
2. SRC-004-MWI-002 Microsoft Research (2022) 'Food Security Analysis and Forecasting: A Machine Learning Case Study in Southern Malawi', Microsoft Research Publications. Available at: https://www.microsoft.com/en-us/research/publication/food-security-analysis-and-forecasting-a-machine-learning-case-study-in-southern-malawi/ (Accessed: 24 March 2026).
   https://www.microsoft.com/en-us/research/publication/food-security-analysis-and-forecasting-a-machine-learning-case-study-in-southern-malawi/
3. SRC-003-MWI-002 Oxford Policy Management (2022) 'Can Better Data Help Policy-Makers and Communities Build Resilience?'. Oxford: Oxford Policy Management. Available at: https://www.opml.co.uk/insights/can-better-data-help-policy-makers-and-communities-build-resilience (Accessed: 24 March 2026).
   https://www.opml.co.uk/insights/can-better-data-help-policy-makers-and-communities-build-resilience
4. SRC-001-MWI-002 Gholami, S., Knippenberg, E., Campbell, J., Andriantsimba, D., Kamle, A., Parthasarathy, P., Sankar, R., Birge, C. and Lavista Ferres, J.M. (2022) 'Food Security Analysis and Forecasting: A Machine Learning Case Study in Southern Malawi', Data & Policy, 4, e38. doi:10.1017/dap.2022.24.
   https://doi.org/10.1017/dap.2022.24