A web app for accessible, reproducible, multi-scale regression models for mapping climate driven infectious diseases.


  • Dr Rory Gibb

    London School of Hygiene & Tropical Medicine, United Kingdom

  • Dr Tim Lucas

    University of Leicester, United Kingdom

  • Dr David Redding

    University College London, United Kingdom

  • Prof Ibrahim Abubakar

    University College London, United Kingdom

Project summary

The ability to predict risk of climate-driven, vector-borne and zoonotic diseases, at fine spatial resolutions, is important for directing public health policy, such as optimal targeting of vaccinations and managing health interventions. With disease cases commonly reported at a coarse county or state level, such high-resolution predictions are crucially not often available. Disaggregation regression is a validated method that can address this gap, but is restricted due to the lack of user-friendly tools.

Here, we aim to create an online app that reads in case data, fetches environmental data, fits disaggregation models and finally summarises predictions in policy-relevant ways. Importantly, we have agreements in place to co-design this tool with public health bodies working on vaccination programmes, and in countries affected by high-burden zoonotic and vector-borne diseases. We will use Lassa fever, a climate-sensitive, zoonotic disease as a case study, to demonstrate a user-friendly workflow that predicts fine-scale cases from areal level case data in Nigeria, in order to optimise vaccine distribution. We will then showcase the potential public health outcomes that can benefit from our tool, across a wide variety of diseases and geographical locations.