data mining

To inventory all existing collected data on COVID-19 in Rwanda, assess its quality, periodicity, and readiness towards the common data model.

data collection

To collect OMOP CDM based prospective enriched data on COVID-19 from the community through mobile surveys applications, face-to-face validation survey, and potentially other sources if available like geofencing data.

data harmonisation

To create the framework for data harmonization to the OMOP Common data model (OMOP CDM): We will start by mapping full hospital patients records, focusing on 8 hospitals located in regions with high number of COVID-19 patients and completing with other isolated datasets.

analytical tools

To build a common data query interface with analytical tools including OHDSI open source tools and ML.


To leverage both traditional mathematical modelling techniques, statistical methods and machine learning methods for prediction models for the burden of COVID-19 in the community but also the potential impact on hospital admissions or overall infection rates and the impact of various public health measures on 1)the pandemic evolution in the country; 2)on social-economic situation, and on 3)mental health (stratified by gender and other vulnerable groups).

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