DHIS2 Climate & Health work in Tanzania leverages existing DHIS2 infrastructure and capacity, while testing innovative DHIS2 tools for climate data integration and predictive modeling.
Health information systems & capacity
DHIS2 has been used as Tanzania’s national health management information system (HMIS) since 2013, replacing the DHIS v1 system that had been in use since 2007. Both the Ministries of Health of mainland Tanzania and Zanzibar are responsible for owning and managing their HMIS systems, and receive technical assistance and capacity building support from HISP Tanzania. DHIS2 is also used in Tanzania for disease surveillance, community health, and a wide range of health programmes.
Climate data integration
The DHIS2 Climate App has been installed in a DHIS2 training instance, enabling upload and integration of climate data and HMIS data in DHIS2. HISP Tanzania has worked with the MoH National Malaria Control Programme (NMCP) to identify and map relevant climate variables from globally available data sets, including precipitation, temperature, and relative humidity, and used these to develop information products in the DHIS2 testing instance that show malaria-climate relationships.
HISP Tanzania has also engaged with the Tanzanian Meteorological Authority (TMA) to explore pushing local climate data into DHIS2 and collaborated with key partners to design data flow pathways and an integration architecture that can be implemented once a data sharing memorandum with the MoH is signed.
Predictive modeling
HISP Tanzania has installed the Chap Modeling Platform and DHIS2 Modeling App on a DHIS2 testing instance and conducted testing sessions jointly with the NMCP that validated model outputs against historical malaria surveillance data and identified configuration requirements for production deployment. A primary area of focus for the NMCP is using the Chap EWARS model to extend the existing Malaria Epidemic Threshold Early Warning Dashboard, which currently shows epidemic thresholds and alerts using malaria data only (manually calculated in Excel and uploaded to DHIS2), by integrating climate data for more accurate prediction and alert triggering and automating the prediction workflow by leveraging Chap and the Modeling App.
At the request of the MoH, HISP Tanzania is also working on developing descriptive models that recommend optimal interventions for areas with predicted high malaria surges–including LLIN quantities, antimalarial drug requirements, and cost estimates–and has engaged in capacity building on mechanistic modeling approaches with the University of Dar es Salaam to support these requirements. These approaches are already being applied in HISP Tanzania’s pilot work in South Sudan.