Each dataset will be mapped to a common data model, already in use for other observational studies thanks to the Observational Health Data Sciences and Informatics (OHDSI) community especially the OHDSI Common Data Model (CDM) through the Observational Medical Outcomes Partnership (OMOP) initiative. The data will remain under complete control of the original data owner, thereby ensuring ethical and local data privacy rules are respected. The harmonized data will include not only COVID-19 diagnosed/serotyped but also non-infected individuals as they will come from normal hospitals electronic health records (EHRs) or testing databases with positive and negative results. In the second stage, this project will collect new data in a longitudinal way. Those new longitudinal data will be enriched with patient reported outcome (PROs) and will be in the same standardized model by design. The surveys will be conducted through mobile application questionnaires completed by direct phone calls and face-to-face surveys. anonymous geofencing data will be collected as well.
T he project outcome will be leveraging all federated data with Machine Learning (ML) and other mathematical methods to drive evidences. Those evidence fulfil the government of Rwanda priorities and need in predicting and monitoring the burden of COVID-19 in the Rwandan community, on hospital admissions and overall infection rates and monitor the impact of various public health measures on the pandemic evolution in the country. Finally, the proposed approach is scalable by extending the list of new datasets or updating the existing one and all data will remain available for future usage. The same approach is also applicable for other diseases and pandemics like Ebola virus, Influenza, and others.