# Using DHIS2 and Climate Data for Spatial Modelling **URL:** \ **Category:** Malaria\ **Tags:** malaria, climate, dac2026, dac2026-posters\ **Created:** [5 May 2026 09:29 UTC](https://community.dhis2.org/t/using-dhis2-and-climate-data-for-spatial-modelling/72184 "2026-05-05T09:29:24Z")\ **Posts on this page:** 12\ **Page:** 1
**Author:** ![Pedyegware](https://dhis2.b-cdn.net/user_avatar/community.dhis2.org/pedyegware/32/49142_2.png) [@Pedyegware](https://community.dhis2.org/u/Pedyegware)\ **Post date:** [5 May 2026 09:29 UTC](https://community.dhis2.org/t/using-dhis2-and-climate-data-for-spatial-modelling/72184/1 "2026-05-05T09:29:24Z")
_This community innovation has been accepted at the [2026 DHIS2 Annual Conference](https://dac2026.dhis2.org/) as a digital poster._ **Using DHIS2 and Climate Data for Spatial Modelling** ![karinateDHIS2 Conference poster2026](https://dhis2.b-cdn.net/uploads/default/original/3X/c/0/c0f89f7aa2a921fbee214510415a828ea307940c.jpeg) 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 ---
**Author:** ![KimHill](https://dhis2.b-cdn.net/letter_avatar_proxy/v4/letter/k/f1d935/32.png) [@KimHill](https://community.dhis2.org/u/KimHill)\ **Post date:** [16 June 2026 06:42 UTC](https://community.dhis2.org/t/using-dhis2-and-climate-data-for-spatial-modelling/72184/2 "2026-06-16T06:42:55Z")
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! ---
**Author:** ![Sehar\_UnNisa](https://dhis2.b-cdn.net/user_avatar/community.dhis2.org/sehar_unnisa/32/38426_2.png) [@Sehar\_UnNisa](https://community.dhis2.org/u/Sehar_UnNisa)\ **Post date:** [16 June 2026 06:45 UTC](https://community.dhis2.org/t/using-dhis2-and-climate-data-for-spatial-modelling/72184/3 "2026-06-16T06:45:42Z")
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? ---
**Author:** ![Nthatile](https://dhis2.b-cdn.net/user_avatar/community.dhis2.org/nthatile/32/36126_2.png) [@Nthatile](https://community.dhis2.org/u/Nthatile)\ **Post date:** [16 June 2026 06:46 UTC](https://community.dhis2.org/t/using-dhis2-and-climate-data-for-spatial-modelling/72184/4 "2026-06-16T06:46:33Z")
Interesting! ---
**Author:** ![Williams\_Agir](https://dhis2.b-cdn.net/user_avatar/community.dhis2.org/williams_agir/32/47981_2.png) [@Williams\_Agir](https://community.dhis2.org/u/Williams_Agir)\ **Post date:** [16 June 2026 06:46 UTC](https://community.dhis2.org/t/using-dhis2-and-climate-data-for-spatial-modelling/72184/5 "2026-06-16T06:46:37Z")
Interesting work Cyril ---
**Author:** ![Kuber\_Adhikari](https://dhis2.b-cdn.net/user_avatar/community.dhis2.org/kuber_adhikari/32/44468_2.png) [@Kuber\_Adhikari](https://community.dhis2.org/u/Kuber_Adhikari)\ **Post date:** [16 June 2026 06:47 UTC](https://community.dhis2.org/t/using-dhis2-and-climate-data-for-spatial-modelling/72184/6 "2026-06-16T06:47:43Z")
Intresting work! I hope it is applicable for other diseases too. ---
**Author:** ![micnice](https://dhis2.b-cdn.net/user_avatar/community.dhis2.org/micnice/32/49678_2.png) [@micnice](https://community.dhis2.org/u/micnice)\ **Post date:** [16 June 2026 06:50 UTC](https://community.dhis2.org/t/using-dhis2-and-climate-data-for-spatial-modelling/72184/7 "2026-06-16T06:50:45Z")
Very interesting. Would be interesting to apply in other health areas. ---
**Author:** ![banga](https://dhis2.b-cdn.net/user_avatar/community.dhis2.org/banga/32/28064_2.png) [@banga](https://community.dhis2.org/u/banga)\ **Post date:** [16 June 2026 06:51 UTC](https://community.dhis2.org/t/using-dhis2-and-climate-data-for-spatial-modelling/72184/8 "2026-06-16T06:51:04Z")
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. ---
**Author:** ![bridget](https://dhis2.b-cdn.net/user_avatar/community.dhis2.org/bridget/32/46444_2.png) [@bridget](https://community.dhis2.org/u/bridget)\ **Post date:** [16 June 2026 06:54 UTC](https://community.dhis2.org/t/using-dhis2-and-climate-data-for-spatial-modelling/72184/9 "2026-06-16T06:54:30Z")
Interesting case study ---
**Author:** ![Kenyuri](https://dhis2.b-cdn.net/user_avatar/community.dhis2.org/kenyuri/32/49666_2.png) [@Kenyuri](https://community.dhis2.org/u/Kenyuri)\ **Post date:** [16 June 2026 06:59 UTC](https://community.dhis2.org/t/using-dhis2-and-climate-data-for-spatial-modelling/72184/10 "2026-06-16T06:59:12Z")
Interesting work. ---
**Author:** ![Pedyegware](https://dhis2.b-cdn.net/user_avatar/community.dhis2.org/pedyegware/32/49142_2.png) [@Pedyegware](https://community.dhis2.org/u/Pedyegware)\ **Post date:** [16 June 2026 11:17 UTC](https://community.dhis2.org/t/using-dhis2-and-climate-data-for-spatial-modelling/72184/11 "2026-06-16T11:17:22Z")
Thanks. I have really connected. I’ll be happy to meet you when next you’re around. We can connect on LinkedIn ---
**Author:** ![Pedyegware](https://dhis2.b-cdn.net/user_avatar/community.dhis2.org/pedyegware/32/49142_2.png) [@Pedyegware](https://community.dhis2.org/u/Pedyegware)\ **Post date:** [16 June 2026 11:19 UTC](https://community.dhis2.org/t/using-dhis2-and-climate-data-for-spatial-modelling/72184/12 "2026-06-16T11:19:23Z")
Yes, that makes a lot of sense. Conversations are ongoing