Leveraging Artificial Intelligence and Data Science Techniques in Harmonizing, Accessing and Analysing SARS-COV-2/Covid-19 Data in Rwanda (LAISDAR)

Full 2

Leveraging Artificial Intelligence and Data Science Techniques in Harmonizing, Accessing and Analysing SARS-COV-2/COVID-19 Data in Rwanda (LAISDAR Project)
Full 2
Full 2
Longitudinal datasets hub for predicting and monitoring COVID-19 evolution in the community and mitigation measures outcomes in Rwanda (Predict Project)
Full 2
previous arrow
next arrow

Project rationale

Why LAISDAR?

The SARS-COV-2/COVID-19 data has the potential to transform our disease understanding and advance science but also to understand outcomes which enable efficient preventive or treatment measure.

However, in Rwanda like in other countries this data is currently fragmented, incomplete and scattered across multiple institutions including hospitals, clinics and testing sites that have captured vast amounts of data on the disease.

Analysing those fragmented COVID-19, datasets brings poor evidence. Pooling all those datasets together in one single dataset is challenging as they have different data structure and data owner may fear break in data privacy. Therefore, we need an innovative approach to analysed all data together.

Federated data from institutions and repository center across the country

about laisdar

LAISDAR is a project managed by the UR alongside Rwanda Biomedical Center, University of Ghent and the Regional Alliance for Sustainable Development (RASD), Rwanda.

UR/SPIU, Kicukiro Campus
Funded by Canada’s International Development Research Centre (IDRC) and the Swedish International Development Cooperation Agency (Sida), under the Global South AI4COVID Program