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Developing AI-Informed Vulnerability Index to Target Aid

 Eligibility and Distribution for

the Food Security Program in Zambia

In response to surging food insecurity in the wake of COVID-19, the Zambian government has tripled the
Food Security Pack (FSP) to the vulnerable population. This research project aims to develop an AI-
informed vulnerability index to assess food insecurity within communities in Zambia to shape aid
distribution. This study focuses on generating robust wealth estimates at a granular level, combining
satellite imagery, social media, and spatial analysis. This research is expected to have a high global
impact by offering enhanced data for key development actors to reach people and regions with extreme
poverty. The project's outputs, such as high-resolution poverty maps, will be shared with Innovation for
Poverty Action and the Ministry of Community Development and Social Services to expand the FSP
programs. These maps can be updated as new data becomes available to decision-makers in near real
time.

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NUMBER OF RECIPIENTS FOR THE FOOD SECURITY PACK

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In response to increasing food insecurity, exacerbated by COVID-19, Zambia is tripling the reach of its Food Security Program.        ​
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Image by Atlas Green

Research

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Dr. Jung's latest research project aims to develop a multimodal approach that combines daytime satellite imagery and social media to estimate the wealth and livelihood of regions and shape aid distribution.



              PARTNERS              ....................................................​
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Recipient of Microsoft's
AI for Humanitarian Action Project Initiatives'  $25,000 Azure Grant. 

              PARTNERS              ....................................................​
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              PARTNERS              ....................................................​
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Recipient of the Research Council's Collaborative Multidisciplinary Award

Each year, billions of dollars are allocated internationally to alleviate poverty. Our work can contribute globally by offering novel sources of data for Zambia and neighboring countries to expand their social protection programs. After this project validates its performance in Zambia, we can apply the model to the Republic of the Congo, a neighboring country without the Demographic and Health Surveys (DHS) wealth data and in an even more challenging data environment.

            RESEARCH TEAM       .................................................................​​

​The team consists of computer/data scientists

and social scientists:​

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  • Woojin Jung (PI) - Rutgers, School of Social Work

  • Dimitri Metaxas (Co-PI) - Rutgers, Dept. of Computer Science

  • Tawfiq Ammari (Co-PI) - Rutgers, School of Communications & Info.

  • Dimitrios Ntarlagiannis (Co-PI) - Rutgers, Dept. of Earth & Environmental Science

  • Saeed Ghadimi - University of Waterloo, AI Institute

  • Maryam Hosseini - New York University, Engineering

  • Charles Chear - Rutgers, School of Social Work

           3 STAGES        ................................................​​

  1. Assess the current machine learning model

  2. Develop new algorithms with a wider application

  3. Share this new field-tested and evaluated approach with policymakers

Research Duration
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July 2021 - Ongoing

The study will conclude with the dissemination of findings to policymakers.

            RESEARCH TEAM       .................................................................​​

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  • Tamara Billima - Innovations for Poverty Action

  • Bernard Tembo - Innovations for Poverty Action

  • Owen Siyoto - Zambia Statistics Agency

  • Cheelo Mwiinga - Innovations for Poverty Action

  • Benny Kabwela - Innovations for Poverty Action

  • Melissa Sartorius - Rutgers, School of Social Work

  • Harish Udaya Kumar - Rutgers, Computer Science

  • Varun Tej Nookala - Rutgers, Data Sciences

  • Devarsh Shah - Rutgers, Computer Science

The research team represents diversity across disciplines, countries/continents (Africa, Asia, US), languages, and cultures. Our study can contribute to broadening the reach of social protection programs by identifying vulnerable and poor populations in the Republic of the Congo and other countries where there is very limited, georeferenced wealth data.

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