From floods to fever: integrating climate early warning data with malaria surveillance for epidemic preparedness in northern nigeria
1
Independent Researcher, ABUJA, Nigeria
Popul. Med. 2026;8(Supplement Supplement 1):A257
ABSTRACT
INTRODUCTION:
climate variability increasingly drives malaria transmission dynamics across sub-saharan africa. in nigeria’s northern states kebbi, sokoto, and zamfara seasonal flooding coincides with peak malaria periods, disrupting access to health services and supply chains. integrating climate early warning data with malaria surveillance could strengthen epidemic preparedness by predicting seasonal upsurges and ensuring timely response. this study explores how climatic indicators can complement dhis2 routine malaria data to improve the anticipation and management of malaria outbreaks in flood-prone regions.
METHODS:
a retrospective mixed-methods analysis was conducted using dhis2 malaria indicators (rdt tests, confirmed malaria cases, and act treatments) from january 2022 to may 2024 across the three northern states. rainfall, temperature, and flood-event data were sourced from the nigeria meteorological agency and the national emergency management agency. spearman correlation and regression models examined relationships between climatic variables and malaria case trends. qualitative interviews with logistics management coordination unit (lmcu) officers and malaria program managers explored the operational use of climate data in planning.
RESULTS:
a strong positive correlation (r=0.72) was observed between cumulative rainfall and malaria incidence, with confirmed cases peaking within four to six weeks after heavy precipitation. furthermore, analysis of dhis2 data revealed high internal consistency (r>0.99) between confirmed rdt-positive cases and reported act treatments, validating surveillance data quality. flood events significantly disrupted commodity distribution and facility reporting timeliness. stakeholders reported that climate alerts were rarely integrated into malaria program decision-making, yet facilities that pre-positioned stocks based on early weather forecasts maintained consistent act availability during peak transmission.
CONCLUSIONS:
integrating climate early warning systems with validated malaria surveillance offers a low-cost, data-driven pathway to epidemic preparedness in nigeria. by embedding meteorological data into dhis2 dashboards, malaria programs can forecast high-risk periods, allocate resources proactively, and safeguard service continuity during floods. this approach demonstrates a scalable model for climate-resilient disease surveillance across sub-saharan africa.
climate variability increasingly drives malaria transmission dynamics across sub-saharan africa. in nigeria’s northern states kebbi, sokoto, and zamfara seasonal flooding coincides with peak malaria periods, disrupting access to health services and supply chains. integrating climate early warning data with malaria surveillance could strengthen epidemic preparedness by predicting seasonal upsurges and ensuring timely response. this study explores how climatic indicators can complement dhis2 routine malaria data to improve the anticipation and management of malaria outbreaks in flood-prone regions.
METHODS:
a retrospective mixed-methods analysis was conducted using dhis2 malaria indicators (rdt tests, confirmed malaria cases, and act treatments) from january 2022 to may 2024 across the three northern states. rainfall, temperature, and flood-event data were sourced from the nigeria meteorological agency and the national emergency management agency. spearman correlation and regression models examined relationships between climatic variables and malaria case trends. qualitative interviews with logistics management coordination unit (lmcu) officers and malaria program managers explored the operational use of climate data in planning.
RESULTS:
a strong positive correlation (r=0.72) was observed between cumulative rainfall and malaria incidence, with confirmed cases peaking within four to six weeks after heavy precipitation. furthermore, analysis of dhis2 data revealed high internal consistency (r>0.99) between confirmed rdt-positive cases and reported act treatments, validating surveillance data quality. flood events significantly disrupted commodity distribution and facility reporting timeliness. stakeholders reported that climate alerts were rarely integrated into malaria program decision-making, yet facilities that pre-positioned stocks based on early weather forecasts maintained consistent act availability during peak transmission.
CONCLUSIONS:
integrating climate early warning systems with validated malaria surveillance offers a low-cost, data-driven pathway to epidemic preparedness in nigeria. by embedding meteorological data into dhis2 dashboards, malaria programs can forecast high-risk periods, allocate resources proactively, and safeguard service continuity during floods. this approach demonstrates a scalable model for climate-resilient disease surveillance across sub-saharan africa.
| eISSN: | 2654-1459 |
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