# Advanced Webinar Series on Spatiotemporal Modeling of Climate-Sensitive Diseases **URL:** \ **Category:** Announcements - Annonces\ **Tags:** webinar, climate\ **Created:** [9 December 2025 09:35 UTC](https://community.dhis2.org/t/advanced-webinar-series-on-spatiotemporal-modeling-of-climate-sensitive-diseases/69034 "2025-12-09T09:35:23Z")\ **Posts on this page:** 6\ **Page:** 1
**Author:** ![gsandve](https://dhis2.b-cdn.net/letter_avatar_proxy/v4/letter/g/aeb1de/32.png) [@gsandve](https://community.dhis2.org/u/gsandve)\ **Post date:** [9 December 2025 09:35 UTC](https://community.dhis2.org/t/advanced-webinar-series-on-spatiotemporal-modeling-of-climate-sensitive-diseases/69034/1 "2025-12-09T09:35:23Z")
# Advanced Webinar Series on Spatiotemporal Modeling of Climate-Sensitive Diseases ![spatiotemporal webinar-2](https://dhis2.b-cdn.net/uploads/default/original/3X/2/a/2a6bf9bec13f837cf358d439a90cdb954d906d59.jpeg) Join the HISP Centre at the University of Oslo for our **advanced webinar series on spatiotemporal modeling for climate-sensitive diseases**! On Wednesday, 17 December at 10-11 UTC, and every Wednesday from 7 January to 4 February at the same time, we will dive into **time series modelling, statistics and machine learning, climate-health relations, DHIS2 tools, GitHub workflows, the Chap Modeling Platform, climate data/GIS** , and more. While open to everyone, these webinars introduce advanced technical concepts and assume prerequisite knowledge. You’ll benefit most if you bring technical skills and real-world ambition to transform climate data into operational health forecasts. You’ll get the most value if you have: - Experience with Python or R programming. - Preferably some background in epidemiology, statistics, or data science - Motivation to develop or work with early warning systems for climate-sensitive diseases. Through practical examples tied to DHIS2 workflows, attendees will gain the tools to build more robust, defensible climate-health forecasts that support operational decision-making in public health systems. _Note: This webinar series was designed as a pre-requisite for an internal workshop being organized in Kigali, Rwanda from 23-27, February, 2026. This workshop is not open to the general public, but we are happy to invite the DHIS2 and modeling community to join us for the webinar series._ ## Learning aims: - Understand the full picture of spatiotemporal modelling for an operational context - the role of data, models and evaluation for an early warning system - Understand how climate-sensitive disease forecasting can be approached through modelling - Develop an understanding of the statistical underpinnings of various modelling approaches, as well as what different evaluation metrics and visualisations capture mathematically. - Establish an effective software setup for the development and use of spatiotemporal models using the open-source Chap modeling platform and DHIS2 modeling app - a setup including version control and sharing of developed code, streamlined installation of libraries, streamlined running of developed code, and good organisation of code and data. ## Further material Theoretical and practical Introduction to spatiotemporal modelling for climate-sensitive disease (still under development): [Learning Modelling - Climate Health](https://dhis2-chap.github.io/chap-core/external_models/learn_modelling.html) ## Dates and Topics: ### Introduction to spatiotemporal modeling and the webinar series 🕑📅 Webinar Date: 2025-12-17T10:00:00Z→2025-12-17T11:00:00Z _(note: the date and time are being updated to your local timezone)_ - Introduce climate-health relations - DHIS2 context and technical setup - Webinar/workshop/process overview - Use case brainstorming Recording: [https://youtube.com/live/THwdco2\_JAk](https://youtube.com/live/THwdco2_JAk) ### Working with codebases 🕑📅 Webinar Date: 2026-01-07T10:00:00Z→2026-01-07T11:00:00Z _(note: the date and time are being updated to your local timezone)_ - Working with codebases and version control (essentials to work effectively with the learning material and use cases - GitHub, virtual environments, installation). Register here: [Meeting Registration - Zoom](https://us06web.zoom.us/meeting/register/XTpSOZ5TTGS9GrStE58xZg) ### Introduction to the Chap modeling platform 🕑📅 Webinar Date: 2026-01-14T10:00:00Z→2026-01-14T11:00:00Z _(note: the date and time are being updated to your local timezone)_ - A basic working example with input data, model training, disease forecasting and evaluation of predictions (using the Chap platform to streamline the process). Register here: [Meeting Registration - Zoom](https://us06web.zoom.us/meeting/register/cwHJlf4qSbSmk-BP0wxR0Q) ### Climate factors, climate change, and their health impacts 🕑📅 Webinar Date: 2026-01-21T10:00:00Z→2026-01-21T11:00:00Z _(note: the date and time are being updated to your local timezone)_ - Climate-sensitive diseases and climate change: from global to local. - Climate information for public health decision-making. - Climate-informed dengue early warning system Register here: [Meeting Registration - Zoom](https://us06web.zoom.us/meeting/register/GnpAZ018TmGVq_Llw6V9aw) ### Fundamentals of statistical modeling 🕑📅 Webinar Date: 2026-01-28T10:00:00Z→2026-01-28T11:00:00Z _(note: the date and time are being updated to your local timezone)_ - Spatiotemporal epidemiological and climate data for model development. - Introduction to hierarchical generalized linear models and Bayesian inference. - Model development: Fixed and random effects. - Applications to disease forecasting for early warning systems. Register here: [Meeting Registration - Zoom](https://us06web.zoom.us/meeting/register/ZCY5ayp7QjiDpyK0XDgOtw) ### Climate data tools, GIS, and data harmonization strategies 🕑📅 Webinar Date: 2026-02-04T10:00:00Z→2026-02-04T11:00:00Z _(note: the date and time are being updated to your local timezone)_ - Climate factors, climate and weather observations, and available climate data from global and local sources - GIS and the harmonisation of climate data to match regions for which health data is available Register here: [Meeting Registration - Zoom](https://us06web.zoom.us/meeting/register/tsh4RGbCQze0DvYe5zXyqg) ### Organizers: HISP Centre at University of Oslo (HISP UiO) Ministry of Health, Rwanda HISP Rwanda CSID Network Data Lab for Social Good Research Group, Cardiff University, UK. TRUST - The Norwegian Centre for Trustworthy AI ---
**Author:** ![system](https://dhis2.b-cdn.net/uploads/default/original/1X/9f553c5c5bb105561154f8aa806ed6e572df30be.png) [@system](https://community.dhis2.org/u/system)\ **Post date:** [6 January 2026 14:02 UTC](https://community.dhis2.org/t/advanced-webinar-series-on-spatiotemporal-modeling-of-climate-sensitive-diseases/69034/2 "2026-01-06T14:02:53Z")
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**Author:** ![Gassim](https://dhis2.b-cdn.net/user_avatar/community.dhis2.org/gassim/32/35347_2.png) [@Gassim](https://community.dhis2.org/u/Gassim)\ **Post date:** [13 January 2026 07:53 UTC](https://community.dhis2.org/t/advanced-webinar-series-on-spatiotemporal-modeling-of-climate-sensitive-diseases/69034/3 "2026-01-13T07:53:26Z")
> [@gsandve](#): > > Introduction to spatiotemporal modeling and the webinar series https://www.youtube.com/embed/THwdco2_JAk?si=B9VTk1fMqI6NMuJk > **AI generated summary of the video: Introduction to spatiotemporal modeling and the webinar series** > > This webinar is the opening session of the **Advanced Webinar Series on Spatiotemporal Modeling of Climate‑Sensitive Diseases** , led by the HISP Centre (University of Oslo) together with partners like the Ministry of Health Rwanda, HISP Rwanda, CSID Network, Cardiff University’s Data Lab for Social Good, and the TRUST AI Centre. It introduces the overall process, tools, and learning path for using DHIS2‑based infrastructure and the CHAP platform for climate‑sensitive disease forecasting. > > The key themes are: > > - **Time series and spatiotemporal modeling basics** : > The speaker explains time series as data observed repeatedly over time (e.g. monthly malaria cases), and “spatiotemporal” as the same type of series across many regions (districts, provinces). Models learn from historical disease data and climate covariates (e.g. rainfall, temperature) to forecast future values. Forecasts include **uncertainty intervals** (e.g. ranges covering 50% and 90% probability), and are evaluated by comparing past predictions against what actually happened. > > - **Statistical underpinnings and evaluation** : > The series will later cover statistical foundations such as generalized linear mixed models and Bayesian hierarchical models, along with how to choose appropriate evaluation metrics and visualizations to rigorously assess model performance. > > - **Climate‑sensitive health impacts and early warning** : > Climate change is presented as a major health threat affecting a wide range of outcomes (heat‑related illness, water‑borne disease, vector‑borne disease). Malaria and dengue are used as illustrative examples of climate‑sensitive diseases where early warning systems can support targeted interventions, stock‑preparation, and planning. > > - **Software setup and workflows (Git/GitHub, environments, DHIS2 tools)**: > A central message is that a bit of **extra setup effort upfront** (version control with Git/GitHub, virtual environments, standardized project structure) greatly reduces the “sweating and desperation” phase of data science. The webinar outlines how climate and health data can be harmonized using the **DHIS2 Climate App and Climate Tools** , stored in DHIS2, and then exported as standardized CSVs for modeling. > > - **“Hello world” modeling approach** : > The speaker proposes a **“hello world” style** learning path: start with an intentionally simple model (e.g. a linear regression or random forest trained on a toy climate–disease dataset), but embed it in the _full_ infrastructure – proper data files, evaluation pipeline, and a GUI front‑end – so that it already runs inside the same framework used for real cases. Once that works end‑to‑end, participants can iteratively improve the model and data. > > - **CHAP modeling platform and DHIS2 Modeling App** : > The webinar introduces **CHAP (Climate Health Analytics Platform)** as a reusable, open‑source platform that standardizes data input, harmonization, model training, forecasting, and rigorous evaluation (including automated generation of prediction–vs–truth plots). CHAP is tightly integrated with DHIS2 through the **DHIS2 Modeling App** , which lets users: > > - **Operational focus and real‑world impact** : > Beyond method development, the series stresses **operational use** : models must match local context, be assessed rigorously on historical data, and be usable by disease programs for real decision‑making (e.g. when and where to expect outbreaks, how far ahead forecasts remain useful). > > - **Open community and workshop in Kigali** : > Participants are invited to join an **open, collaborative community** around CHAP and DHIS2, sharing models and evaluation approaches so countries can reuse and adapt each other’s work. The webinar also mentions a follow‑up **in‑person workshop in Kigali (February 2026)**, with a hackathon‑style format for working on concrete use cases, for which this webinar series is a prerequisite. > > The overall message is that this series will help technically prepared participants (with R/Python and some epi/stats background) learn not only _how_ to build spatiotemporal models for climate‑sensitive diseases, but _how to make them operational_ in DHIS2 using CHAP, with shared open‑source code, standardized workflows, and strong links to real country use cases. > [@gsandve](#): > > Working with codebases https://www.youtube.com/embed/59IjIK3lNvQ?si=eqpRxAv1HPGJBidN > **AI generated summary of the video: Working with codebases** > > This webinar, **“Working with codebases”** , is the second session in the advanced DHIS2 / CHAP webinar series on spatiotemporal modeling of climate‑sensitive diseases. It focuses on setting up _practical, reproducible workflows_ so that modeling code can be shared and reliably run by others, not just “on my machine.” > > The session is structured around three pillars: > > * * * > > ##### 1. Terminal basics and navigation > > Knut introduces the command‑line terminal as the primary interface for working with code projects in a consistent way across operating systems. > > - **Environment setup** > > - **Core navigation commands** > Inside the terminal, he demonstrates how to: > > - **Productivity tips** > > The goal is to be comfortable navigating to the right project folder before running any modeling tools. > > * * * > > ##### 2. Version control with Git and GitHub > > Next, the webinar moves to **Git** for version control and **GitHub** for sharing and collaboration. > > - **Installing Git** > > - **Cloning and forking repositories** > Participants are shown how to: > > - **Basic Git workflow** > Inside the cloned project, Knut demonstrates the standard cycle: > > - **Authentication and pushing to GitHub** > To avoid typing passwords repeatedly, he configures authentication with the **GitHub CLI** (`gh`), logging in via the browser once. After that, `git push` sends local commits to the remote repository, providing both backup and a way to share code with collaborators. > > The emphasis is that Git history and good commit messages make it easy to see what changed and to roll back if a new change “breaks” the model. > > * * * > > ##### 3. Dependency management & virtual environments (Python and R) > > The third block tackles reproducibility: ensuring others can _actually run_ your code with the same package versions. > > ###### Python – `uv`, `pyproject.toml`, and lock files > > - Knut introduces **uv** as a modern tool to manage Python environments per project. > > - The project contains a `pyproject.toml` listing dependencies (e.g. `pydantic`, `pandas`), and `uv` uses this to: > > - Running code with `uv run python hello.py` both: > > - When new packages are needed (e.g. `pandas`), `uv add pandas` updates both the environment and the lock file, so collaborators can reproduce the same setup. > > ###### R – `renv` and lock files > > - For R, the equivalent solution is **`renv`** : > > - This ensures that anyone checking out the project and running `renv::restore()` ends up with the same package versions, which is crucial when sharing models with ministries or research partners. > > Both approaches directly address the common “it works on my machine” problem by making dependencies explicit and reproducible, which the presenters note is a frequent issue even for published academic modeling code. > > * * * > > ##### Motivation, homework, and links to CHAP / DHIS2 > > Throughout, the presenters stress that these tools (terminal, Git, uv/renv) may feel like extra work at first, but they _reduce manual fiddling_ and errors later, and are essential if CHAP models are to be reusable across countries and teams. > > Participants are asked to: > > - Work through written guides on: > > - These guides are linked from the **2026 modeling webinar series** page, which is accessible via the CHAP site and the “Learning Modelling” material. > > This setup is **homework before the next webinar** (“Introduction to the Chap modeling platform”), where participants will start running actual spatiotemporal models for climate‑sensitive diseases using these standardized workflows. > [@gsandve](#): > > Introduction to the Chap modeling platform https://www.youtube.com/embed/D8evvY3jTk4?si=uwEHjDkr1zFh-boP > **AI generated summary of the video: Introduction to the Chap modeling platform** > > This video is the third session in an advanced webinar series on spatiotemporal modeling of climate‑sensitive diseases, and it gives a practical introduction to the **Chap (Climate Health Analytics Platform) modeling platform**. > The goal is to walk technically prepared participants (with Python/R and epi/stats skills) through building and running their first forecasting model in Chap, using a complete live workflow from data preparation to evaluation. > > Key steps and functionalities highlighted in the webinar include: > > - **Getting Chap running** > The session shows how Chap Core is installed and run in a local environment, so participants can execute Chap commands on their own machine or server and reproduce the examples. > > - **Implementing a model from a minimalist example** > Participants are introduced to a **minimalist example model** hosted on GitHub. They are shown how to copy (fork/clone) this repository so they can customize it. > The structure of this minimalist model is explained: it exposes two core functions, `train` and `predict`, which read simple CSV files, fit a model, and write out forecasts. In the basic version, a **linear regression** is trained to predict disease cases (e.g. dengue) from simple features such as rainfall and temperature; the presenter explicitly notes that this is intentionally oversimplified and not yet meaningful for real‑world early warning. Participants see how to run this model locally and inspect the generated predictions for a toy dataset. > The code is then briefly modified to swap the linear regression with a **random forest regressor** , illustrating how model developers can change algorithms while keeping the Chap interface the same. > > - **Running models through Chap** > The video explains the small configuration file (based on MLflow conventions) that tells Chap how to run the model—what command to invoke for training and for prediction, and which parameters (such as input and output file paths) are expected. > A live demonstration shows the use of the `chap evaluate` command, which runs the model repeatedly on a larger _real_ dataset (e.g. public dengue data from Brazil) and generates an automatic PDF report with time‑series plots comparing predicted and observed cases. > The report clearly reveals that the minimalist model performs poorly on real data, underlining that Chap is a framework for **rigorous evaluation** , not a guarantee that any simple model will work. > > - **Exploring the Chap models repository** > The presenter shows that beyond the minimalist example, there is a growing **repository of more advanced, open‑source models** contributed by the community, including deep learning and Bayesian approaches. These are made Chap‑compatible so that they can be run and evaluated with exactly the same workflow. > > - **Integration with the DHIS2 Modeling App** > The second part of the session demonstrates how Chap models can be used through the **DHIS2 Modeling App** , a graphical front‑end inside DHIS2 that connects to Chap as a backend service. > From this GUI, users can: > > - **Closing messages and next steps** > The session closes by stressing that this “hello‑world” focused webinar is mainly about **technical setup and workflow** —getting participants to the point where they can run, evaluate, and iterate on models in Chap and DHIS2. Once this pipeline is in place, modelers can collaborate with public health experts to develop highly tailored forecasting models and share them as open resources for other countries > Subsequent webinars in the series are announced as covering more conceptual topics: climate factors and health impacts, statistical modeling foundations, and advanced methods for spatiotemporal disease forecasting. > [@gsandve](#): > > Climate factors, climate change, and their health impacts https://www.youtube.com/embed/Os3uxISDKX8?si=wy7AZ94wxFila3on > **AI generated summary of the video: Webinar: Climate factors, climate change, and their health impacts** > > This webinar in the _Spatiotemporal Modeling of Climate‑Sensitive Diseases_ series introduces how climate factors, climate change, and non‑climatic drivers interact to affect health, with a focus on climate‑sensitive infectious diseases and their use in early warning systems. > Two researchers from the Barcelona Supercomputing Center use dengue as a recurring case study to illustrate concepts. > > **Climate‑sensitive diseases and hazards** > The presenters first explain how climate variables such as temperature, precipitation, and humidity give rise to hazards (heatwaves, floods, droughts), which can impact health directly (e.g. heat stress, injuries) or indirectly through malnutrition and diarrhoeal diseases. > They then focus on climate‑sensitive infectious diseases: > > - **Vector‑borne diseases** like malaria, dengue, chikungunya, where vectors are highly sensitive to temperature, moisture, and water availability. > - **Water‑borne diseases** like cholera and leptospirosis, where flooding, sea‑level rise, and drought affect water and sanitation systems. > These diseases typically show strong seasonality, distinct geographic ranges, and multi‑year variability. > > **Climate drivers and disease patterns** > The webinar explores how specific climate conditions shape risk. For example, drought can increase dengue risk when people store water in containers that favour Aedes aegypti breeding, whereas very intense rainfall can flush out larvae and reduce risk. > Large‑scale ocean phenomena such as El Niño and La Niña are discussed as drivers of temperature and rainfall anomalies that propagate into disease patterns. > > **Key properties of climate–disease relationships** > Three recurring properties are highlighted: > > - **Non‑linearity** : relationships are often hump‑shaped—both “too little” and “too much” rain or heat can reduce transmission by disrupting vectors or pathogens. > - **Lags** : there is usually a delay between climate anomalies and observed cases, driven by vector life cycles, pathogen incubation, and human behaviour. > - **Modulation by non‑climatic factors** : socioeconomic conditions, demographics, land use, water/sanitation infrastructure, and behaviour can strengthen or weaken climate effects, and interact with each other and with climate in complex ways. > > **Climate‑informed early warning systems (EWS)** > The second half of the webinar shows how these insights are translated into climate‑informed EWS that combine climate and epidemiological data to forecast outbreaks and support more timely, targeted responses. Typical lead times for vector‑borne disease EWS are on the order of 1–6 months, to allow time for interventions. Building such systems involves: > > - Stakeholder engagement and problem definition (disease, lead time, spatial scale). > - Assessing evidence on relevant climate drivers. > - Assembling and harmonizing data (climate, environmental, demographic, surveillance). > - Model development and **validation against historical data** , explicitly checking how well predictions would have worked in the past. > - Risk communication and co‑definition of actions linked to different alert levels (e.g. enhanced surveillance, vector control, community communication, facility preparedness). > - Continuous evaluation and iteration in real use. > > **Modeling approaches for EWS** > Three families of models are compared: > > - **Statistical models** (e.g. time‑series regression) that relate cases to predictors under probabilistic assumptions, work well with long historical series, and give principled uncertainty estimates. > - **Machine‑learning models** (e.g. random forests, neural networks), which are flexible and potentially more accurate but often require more data and are harder to interpret and to use for uncertainty quantification. > - **Mechanistic models** (e.g. compartmental or agent‑based models), which encode biological and behavioural processes such as immunity, contact patterns, and mobility; these can model scenarios but depend on strong assumptions and detailed system knowledge. > > **Data needs and preprocessing** > The presenters outline four main data categories needed for climate‑informed EWS: > > - **Weather and climate data** from national meteorological stations and/or global reanalysis products. > - **Environmental data** , including land use/cover, vegetation indices (e.g. NDVI), and water indices derived from remote sensing. > - **Demographic and socioeconomic data** to represent population at risk and vulnerability. > - **Disease surveillance data** , usually case counts reported at weekly or monthly resolution. > > They then describe three core preprocessing strategies: > > - **Rolling statistics** : computing moving averages (e.g. 3–12‑month temperature means) or moving sums of precipitation to capture persistent warm/cold periods, droughts, or extended wet spells. > - **Anomalies** : expressing current climate values relative to a long‑term baseline (e.g. 30‑year mean and variance) to highlight departures from “normal” conditions and identify extremes. > - **Lags** : explicitly including delayed versions of climate variables (e.g. rainfall 1–6 months ago) so models can predict future risk using _observed_ past climate, reducing dependence on uncertain forecasts. > > The webinar closes by linking these methodological pieces back to the broader series and to tools like Chap, which aim to make rigorous climate‑informed modeling and evaluation more accessible to public‑health practitioners and modelers working with DHIS2 data. > [@gsandve](#): > > Fundamentals of statistical modeling https://www.youtube.com/embed/2fW6vLaTNHs?si=4hxPhv-9GAgPRTlF > **AI generated summary of the video: Webinar: Fundamentals of statistical modeling** > > This webinar on _Fundamentals of Statistical Modeling_ is part of an advanced series on spatiotemporal modeling of climate‑sensitive diseases. It aims to equip technically prepared participants with core statistical concepts needed to build and evaluate forecasting models for diseases such as dengue or malaria, especially in the context of early warning systems. > The session focuses on how to move from simple linear models to more appropriate spatiotemporal models for disease case data, and on understanding and communicating uncertainty in predictions. > > - The basic steps of **modeling and forecasting with time‑series data** : preparing data, identifying patterns such as trends and seasonality, fitting models, and generating predictions for climate‑sensitive diseases. > > - Core ideas from **Bayesian statistics** , emphasizing how Bayesian methods provide full probability distributions for parameters and predictions, and why this matters for operational early warning systems where uncertainty must be quantified and communicated. > > - Different **sources of uncertainty** in predictive modeling: variability in the data, limitations of model structure and assumptions, and external climate or environmental drivers that affect forecast accuracy. > > AI Summary based on the detailed transcript‑derived notes, the webinar walks through the following technical elements: > > - **Linear model review** : Starting from a simple linear regression where disease case counts are modeled as a function of rainfall and temperature, the presenters discuss assumptions such as linearity, conditional independence, and normally distributed errors, and why these are problematic for count data. > > - **Generalized Linear Models (GLMs)**: GLMs are introduced as a way to handle non‑normal outcomes and non‑linear relationships, using appropriate distributions for counts (e.g. Poisson or negative binomial) and link functions (such as the log link) to connect the mean response to a linear predictor and to interpret coefficients on the link scale. > > - **Population offset** : To move from crude counts to rates, the model includes a population offset, allowing comparisons across spatial units and times with different population sizes by effectively modeling incidence rates. > > - **Bayesian inference in practice** : Beyond the conceptual introduction, the session shows how Bayesian inference is applied to these GLMs to obtain posterior distributions, credible intervals, and probabilities that predicted values exceed defined thresholds—key quantities for triggering early warning alerts. > > - **Hierarchical (multilevel) models**: Hierarchical structures are used to incorporate group‑level information (e.g. regions or districts) while retaining unit‑level detail (e.g. municipalities). Partial pooling via varying intercepts and slopes shares information across groups and stabilizes estimates where data are sparse. > > - **Model terms for spatiotemporal structure** : The model is extended with terms to capture interannual variation and seasonality (e.g. annual random effects, monthly seasonality components with suitable priors) and to model spatial dependence (e.g. intrinsic CAR/BYM2‑type structures) so that neighbouring areas can borrow strength from each other. > > - **Non‑linear effects** : To represent non‑linear climate–disease relationships, the webinar illustrates the use of polynomial terms and/or spline functions on climatic covariates, increasing model flexibility while retaining interpretability. > > - **Forecasting for early warning systems** : The full predictive distribution from the Bayesian model is then used to frame early warning in probabilistic terms—for example, computing the probability that incidence will exceed an outbreak threshold, and using this for risk communication and decision rules. > > - **Model evaluation** : Finally, the webinar emphasizes the role of cross‑validation and retrospective evaluation—comparing out‑of‑sample forecasts with observed outcomes over historical periods—to assess and compare model performance before operational use. > [@gsandve](#): > > Climate data tools, GIS, and data harmonization strategies https://www.youtube.com/embed/9q11dNj2A1w?si=RI3obtPN5C6-T4__ > **AI generated summary of the video: Webinar: Using DHIS2 Climate Tools - A flexible toolkit for climate data integration** > > This webinar presents **DHIS2 Climate Tools** , an open‑source Python toolkit for accessing, processing, harmonizing, and loading climate, weather, and environmental data into DHIS2 and the Chap modeling platform. The tools are aimed at data engineers and data scientists who need reproducible workflows for exploring, analyzing, visualizing, and importing both global and local data sources. > > **Introduction to DHIS2 Climate Tools** > The webinar starts with an overview of the toolkit and its website entry point, which documents open‑source libraries and end‑to‑end workflows for integrating climate, weather, and environmental data with DHIS2 and Chap. > Climate Tools is built on Python and leverages the geospatial ecosystem (Xarray, GeoPandas, EarthKit) to make complex spatial operations manageable. > > **Challenges and solutions in climate data integration** > Common challenges described include accessing climate data from different providers and aligning it with health data in space and time. To address this, the toolkit provides **dhis2eo** , a Python library that can download climate and environmental datasets (e.g. from the Copernicus Climate Data Store or the Climate Hazards Center) and convert them towards DHIS2‑ready formats. > > **Data harmonization (temporal and spatial)** > A central part of the webinar is GIS‑based harmonization: > > - _Temporal harmonization_: many global datasets (e.g. ERA5) are available hourly. Climate Tools shows how to aggregate these to daily or other DHIS2‑relevant periods, handling time aggregation as part of the workflow. > > - _Spatial harmonization_: global products are typically gridded (e.g. ~9 km cells). Using Xarray, GeoPandas, and EarthKit, the toolkit aggregates grid cells to DHIS2 organisation units by overlaying the grid with admin polygons and computing statistics (e.g. mean temperature or rainfall per district). > > These steps produce datasets directly aligned with the health regions where DHIS2 data are collected. > > **Automating data flows and integration options** > The webinar emphasizes that Climate Tools is meant for automated, repeatable pipelines: once a workflow is defined, it can be run regularly (e.g. as a scheduled job) so DHIS2 always has up‑to‑date climate and environmental data, which is particularly important for early warning and forecasting use cases. > The outputs can be: > > - Imported into DHIS2 (as aggregate data values), or > > - Sent directly to the **Chap** modeling platform when working primarily on modeling workflows rather than DHIS2 storage. > > **Python client for DHIS2 interaction** > A key component is the **DHIS2 Python Client** , bundled in the Climate Tools ecosystem. It provides a lightweight interface to the DHIS2 Web API from Python, making it easier to pull and push data, work with metadata, and integrate climate workflows into broader data engineering tasks. > > **Contributions and community workflows** > Finally, the webinar invites contributions from the community. The project maintains a growing set of **runnable Jupyter notebooks** that demonstrate end‑to‑end workflows (downloading, harmonizing, and uploading data), and users are encouraged to contribute their own notebooks and use cases to the GitHub repositories, making Climate Tools a collaborative, evolving toolkit. ---
**Author:** ![system](https://dhis2.b-cdn.net/uploads/default/original/1X/9f553c5c5bb105561154f8aa806ed6e572df30be.png) [@system](https://community.dhis2.org/u/system)\ **Post date:** [20 January 2026 04:00 UTC](https://community.dhis2.org/t/advanced-webinar-series-on-spatiotemporal-modeling-of-climate-sensitive-diseases/69034/5 "2026-01-20T04:00:40Z")
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**Author:** ![Henok\_Tadesse](https://dhis2.b-cdn.net/user_avatar/community.dhis2.org/henok_tadesse/32/47386_2.png) [@Henok\_Tadesse](https://community.dhis2.org/u/Henok_Tadesse)\ **Post date:** [23 January 2026 16:33 UTC](https://community.dhis2.org/t/advanced-webinar-series-on-spatiotemporal-modeling-of-climate-sensitive-diseases/69034/6 "2026-01-23T16:33:34Z")
Thank you for the truly wonderful webinar. I am learning a lot and cant wait for the next sessions. I was wondering is there any chance to get the slides(in pdf form) form the latest session on Spatiotemporal modeling of Climate-Sensitive Diseases. Thanks in advance!! ---
**Author:** ![Gassim](https://dhis2.b-cdn.net/user_avatar/community.dhis2.org/gassim/32/35347_2.png) [@Gassim](https://community.dhis2.org/u/Gassim)\ **Post date:** [28 January 2026 07:31 UTC](https://community.dhis2.org/t/advanced-webinar-series-on-spatiotemporal-modeling-of-climate-sensitive-diseases/69034/7 "2026-01-28T07:31:35Z")