Dahdaleh Researchers Awarded Connected Minds Grant to Co-Design Novel Machine Learning-Enabled Public Health Risk Assessment Tools in Uganda

A group of Dahdaleh Institute researchers has been awarded a Connected Minds grant. This grant will support new research on co-designing a novel machine learning-enabled public health risk assessment tool using quantitative microbial risk assessment (ML-QMRA) in Uganda with a local collaborator, Nsamizi Training Institute of Social Development. Dahdaleh Global Health Graduate Scholar Michael De Santi spearheaded the development of the application, Faculty Fellows Usman Khan and Shital Desai are Co-PIs, and Research Fellow Syed Imran Ali is a collaborator.

Waterborne illnesses are a leading health risk facing displaced populations. The Kyaka II refugee settlements in Uganda, for instance, has faced outbreaks of cholera, shigellosis, and acute watery diarrhea. To keep water safe, humanitarian water treatment must respond to challenging and variable site conditions. There is an opportunity to leverage advanced modelling to unlock operational insights from routine water quality monitoring data so operators can optimize treatment and protect public health. However, to achieve these impacts, these models must integrate within local ways of working and solutions.

York University and the Nsamizi Training Institute of Social Development are partnering to answer the question: how can we co-create a new modelling tool that combines machine learning (ML) water quality modelling with quantitative microbial risk assessment (QMRA) health risk modelling to help Nsamizi optimize water treatment at Kyaka II? The goals of this research are to:

1.       Prototype, pilot, and evaluate a novel ML-integrated QMRA model to forecast waterborne illness risk and optimize treatment using routine monitoring data.

2.       Understand how the model fits within Kyaka II’s techno-social operating environment through co-creation with frontline responders and users.

These goals will be achieved by first prototyping the ML-QMRA model and then piloting this prototype in a 12-month field deployment. The tool will be co-created with Nsamizi to understand the role the model needs to fill in Nsamizi’s operations and to continuously improve the model’s fit within Nsamizi’s techno-social operating environment.

This project will produce the first ever ML-integrated QMRA model and will develop an understanding of how to integrate the model into local operations, enabling Nsamizi to provide safe water in Kyaka II. This advances the long-term goals of Connected Minds, improving understanding of the interplay of ML enabled risk modelling tools with humanitarian responders and the communities where they work.

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