Using DHIS2 and Climate Data for Spatial Modelling

Implementation - Implémentation Malaria
, , ,
1

This community innovation has been accepted at the 2026 DHIS2 Annual Conference as a digital poster.


Using DHIS2 and Climate Data for Spatial Modelling

karinateDHIS2 Conference poster2026
karinateDHIS2 Conference poster20261920×4518 1.56 MB

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

2

Fascinating work! I see a couple of posters related to C&H in Nigeria, have you all connected? I was in Abuja last month discussing C&H and am very interested in learning more as well. Thanks!

3

This is very interesting work, especially the use of DHIS2 routine malaria data with rainfall and temperature data for spatiotemporal modelling. How do you see this approach being operationalized for routine malaria program decision-making within DHIS2?

4

Interesting!

5

Interesting work Cyril

6

Intresting work! I hope it is applicable for other diseases too.

7

Very interesting. Would be interesting to apply in other health areas.

8

The work clearly demonstrates how DHIS2 and climate data can be integrated to generate actionable malaria risk insights and support targeted, climate-resilient malaria control strategies at the subnational level.

9

Interesting case study

10

Interesting work.

11

Thanks. I have really connected. I’ll be happy to meet you when next you’re around. We can connect on LinkedIn

12

Yes, that makes a lot of sense. Conversations are ongoing