This community innovation has been accepted at the 2026 DHIS2 Annual Conference as a digital poster.
Using DHIS2 and Climate Data for Spatial Modelling
Globally, Nigeria contributes the highest percentage of malaria cases (24.3%) and deaths (30.3) and more than half of the cases in the WHO Africa Region. Being a climate sensitive disease, malaria provides a use case for integrating routine health surveillance data with environmental information to strengthen climate resilient health systems Bayelsa and Rivers States, in Nigeria, are marked by all round rainfall, yet display marked spatial and temporal differences in malaria burden. Leveraging DHIS2 as the national routine health information platform, this study examines how rainfall and temperature interact with local context to shape malaria transmission dynamics, identify persistent transmission LGAs, and generate actionable subnational insights for malaria control. Monthly malaria case data (2012-2023) were extracted from DHIS2 alongside rainfall and temperature data from 2015 to 2023 for both States. A Bayesian hierarchical spatiotemporal modelling framework was implemented using integrated nested Laplace approximations to estimate the malaria risk across the LGAs and over time. We found that rainfall and temperature demonstrated non-linear associations with malaria transmission. Malaria cases increased with rising rainfall and temperature up to an optimal threshold, beyond which the cases declined. Consequently, Brass, Ekeremor, Sagbama and Yenagoa LGAs emerged as high-risk malaria zones in Bayelsa State, while Abua/Odual, AkukuToru, Obio/Akpor, and Port Harcourt LGAs in Rivers State. This study demonstrates the utility of DHIS2 for integrating routine malaria surveillance data with climate information to generate actionable, subnational insights on malaria risk. While climatic conditions could create conditions suitable for transmission, they do not solely determine the malaria burden. DHIS2-enabled climate-informed analytics can support targeted malaria control strategies and strengthen adaptive disease surveillance in climate-vulnerable settings.
Primary Author: Karinate Cyril-Egware
Keywords:
DHIS 2, Spatio-temporal, Malaria transmission, rainfall, temperature, transmission dynamics, malaria risk