This community innovation has been accepted at the 2025 DHIS2 Annual Conference
Climate sensitive disease forecasting in DHIS2
Climate change influences disease patterns globally, particularly for climate-sensitive diseases such as water-borne and vector-borne diseases, making prediction systems essential for timely response. In Lao People’s Democratic Republic (Lao PDR), we integrated climate data, such as temperature and rainfall from weather stations, into the District Health Information Software version 2 (DHIS2)-based Health Management Information System (HMIS) to enhance disease forecasting capabilities. The objective of this initiative was to integrate climate data into routine health information systems to predict and respond to disease outbreaks effectively. As the first step, we linked climate data with HMIS surveillance data and demonstrated an association between climate variables and diseases like dengue and diarrhea. Subsequently, we combined weekly indicator-based surveillance data and climate variables in a predictive engine, the Early Warning, Alert, and Response System (EWARS). Using statistical models based on R, this engine analyzed trends and provided forecasts for diseases such as dengue. Predictions generated by EWARS were then pushed back to DHIS2 for visualization, enabling health authorities to act proactively. We tested the system using historical data up to 2022 to forecast dengue outbreaks and verified its accuracy, with 2023 predictions closely matching actual data. This integration demonstrates the feasibility of integrating climate data into health information systems to predict outbreaks efficiently. However, challenges remain, including data-sharing agreements among different agencies own them, limited granularity of climate data, and variability in prediction accuracy. Addressing these gaps requires high-level coordination and ongoing refinement of predictive models. Strengthening the integration of climate data into health systems, improves preparedness, and strengthens the response to climate-sensitive diseases, ensuring better health outcomes in Lao PDR.
Primary Author: Achala Upendra Jayatilleke
Keywords:
Climate change, climate sensitive disease, disease forecasting, preparedness, response, DHIS2, HMIS
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