This community innovation has been accepted at the 2026 DHIS2 Annual Conference as a digital poster.
Integrated DHIS2 Analytics in Malaria Surveillance
Malaria remains a major public health challenge in Nigeria, with persistently high morbidity in Kebbi State in the northwestern region. Understanding how ecological conditions, seasonal trends, and access to health services interact to shape malaria burden is essential for strengthening routine surveillance and guiding targeted interventions. Using DHIS2 as the primary malaria surveillance platform, this study examined spatio-temporal trends in malaria case notification rates (CNRs) from 2019 to 2024 and linked them to climate and environmental gradients. Microscopy and rapid diagnostic test–confirmed uncomplicated malaria cases were extracted from DHIS2 and analysed alongside climatic, land use, and health facility datasets. Autoregressive Integrated Moving Average (ARIMA) models were applied to project incidence to 2026. Spatial accessibility analysis mapped settlements located beyond 5 km buffers of public health facilities to identify underserved populations. Spatial autocorrelation and linear regression modelling were used to identify predictors of malaria CNRs. Malaria incidence showed strong seasonality with rising dry season troughs. High burden clusters expanded after 2022, while underserved settlement clusters with limited accessibility to public health facilities were identified, representing probable DHIS2 underreporting gaps. Health facility density (r = 0.55**, p = 0.01) and vegetation (r = 0.37*, p = 0.05) were positively associated with malaria CNRs, whereas farmland and area of surface water were negatively associated (r = 0.45* and 0.22*; p > 0.05). Precipitation and temperature showed weak, inconsistent associations and were not strong predictors in the final models. Ecological gradients, seasonal persistence, and patterns of healthcare access and reporting shaped malaria risk in Kebbi State. This study demonstrates how DHIS2, combined with spatial and environmental analytics, can reveal malaria surveillance gaps and priority areas for intervention.
Primary Author: Ifeoma Ezenyi
Keywords:
DHIS2; malaria surveillance; climate and health; spatial analysis; Nigeria; Kebbi State; ARIMA forecasting; health facility accessibility
