DHIS2 Workshop on Spatiotemporal Modeling Hosted in Kigali, Rwanda

Implementation - Implémentation
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HISP Rwanda, in partnership with the Ministry of Health Rwanda, is hosting a five-day practical DHIS2 Workshop on Spatiotemporal Modelling of Climate-Sensitive Diseases in Kigali.

The workshop brings together over 60 participants from more than 15 countries across Africa, Europe, and Asia, including national health programme teams and DHIS2 implementing partners. It is designed to strengthen country capacity to analyse, model, and interpret climate and health data for improved forecasting and response to climate-sensitive diseases.

Organized by HISP Center(University of Oslo) in collaboration with MOH Rwanda, CSID Network, SOSCHI, the Data Lab for Social Good, and TRUST, the workshop provides participants with hands-on experience in applying spatiotemporal modelling approaches to real-world country use cases.

Throughout the week, participants are developing and evaluating predictive models that capture spatial and temporal patterns of disease risks influenced by climate variability. The workshop also introduces how DHIS2-based tools, including the CHAP modelling platform and the DHIS2 Climate App, can be leveraged to integrate climate data and modelling outputs into routine surveillance systems and climate-informed early warning mechanisms.

By building practical modelling skills among country teams and DHIS2 partners, this initiative contributes to strengthening data-driven preparedness and response to emerging climate-related public health risks.

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#hartinye
The Kigali-based DHIS2 Workshop on Spatiotemporal Modelling of Climate-Sensitive Diseases achieves an essential milestone towards operationalizing climate-informed public health intelligence. The most recent scholarship shows again and again that adding high-resolution climate data to daily surveillance platforms like DHIS2 significantly improves the accuracy of early warning systems, as well as their geographic targeting and resource allocation for diseases including malaria, cholera, dengue, and diarrheal disease. Evidence from Africa and other climate-vulnerable regions indicates that robust spatiotemporal modeling does improve outbreak forecasting, facilitate local decision-making, and bolster resilience with climate-smart public health systems. Importantly, several studies emphasize how integrating predictive models into DHIS2-based workflows increases usability by national health actors and also guarantees inherence of these systems beyond pilot research programs.

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