Your first forecast in the Modeling App

A walk from a fresh deployment to an evaluated model and a three-month dengue forecast, by clicking in the Modeling App. It takes about fifteen minutes, most of it waiting for the model. It starts where Chap with a local DHIS2 ends:

chap-devops init mychap --models chapkit_simple_multistep_model --with dhis2
cd mychap
chap-devops up
chap-devops status          # until the dhis2 line says up
chap-devops dhis2 connect
chap-devops open dhis2

chap-devops dhis2 connect points the DHIS2 route chap at chap-core, installs the Modeling App and the DHIS2 Climate App, and runs the DHIS2 analytics tables. It also makes the configured models that the Modeling App lists. It took about 40 seconds. Its last line is the Modeling App can reach Chap; open DHIS2 with `chap-devops open dhis2`.

Tested on 2026-10-19 with chap-devops v0.110.0, Modeling App 7.3.0, DHIS2 2.42.6 with the Laos demo database, chap-core v2.5.0 and Simple Multistep (chapkit) 0.1.2. Other versions of the Modeling App may name a button differently; the steps stay the same. chap-devops dhis2 show says which Modeling App version a deployment has.

What the demo data holds

The DHIS2 that chap-devops starts is seeded with a demo database from Laos:

for Lao PDR, its 18 provinces and their districts. The Modeling App adds the weekly cases up to months when an evaluation is monthly.

1. Open the Modeling App

Log in to DHIS2 with admin / district, open the app menu (the grid of squares at the top right), type Modeling in its search box and pick Modeling: the menu shows only a few top apps until you search. The dashboard is empty until the first evaluation.

2. Start an evaluation

An evaluation asks: had this model been used in the past, how close would its forecasts have come? It trains the model on the earlier data, forecasts the months after, and compares the forecast with what really happened, several times over.

In Evaluate, Overview, choose New evaluation. The page opens on the tab Use saved dataset, which needs a dataset saved before. Choose the tab Import from DHIS2 and fill in:

The list cuts long names short, for example Simple Multistep Model (chapkit) [Mont…. The panel on the right shows the full name of the entry you clicked, and Use this model picks that entry.

Leave the Backtest parameters as they are: 3 forecast periods, 7 splits, a step of 1, 1 training run, and the future-weather provider Seasonal climatology.

Simple Multistep Model (chapkit) is the model chap-devops started with --models chapkit_simple_multistep_model. It has two entries, one for each configuration in its marketplace entry: Monthly climate uses rainfall, temperature and relative humidity, and Monthly selfhistory uses only the case history. The list shows one entry for each configured model, as <model name> [<variant name>]. Each model enabled with chap-devops models enable adds its own entries. chap-core 2.4 and later does not make these entries itself: chap-devops up --wait, chap-devops dhis2 connect and chap-devops models configs sync make them. If a model is not in the list, run chap-devops status. If it shows the model as registered, not configured, run chap-devops models configs sync. To add an entry of your own with other option values, run chap-devops models configs add. See Adding. chap-devops models test --all makes each model train and predict. See Configured models.

Under each name, the list shows the author of the model. The list also holds models that chap-core ships configured on its own and runs inside its worker, such as CHAP-EWARS Model and Monthly CHAP-EWARS model. Some of them have the status Deprecated. From chap-core v2.5.0, it also has the three built-in baselines, Persistence, Global median and Seasonal median. chap-core runs them beside each evaluation, so you can compare a model with them. None of these are models that chap-devops started. The two (chapkit) entries have the status Limited.

3. Map the data

Configure sources asks which DHIS2 data each input of the model comes from. Search by name in each box:

A search for the dengue cases also finds the items CHAP Dengue Cases (Any) - Weekly Quantile ... and NCLE: 7. Dengue cases (any). The CHAP items hold forecasts, not cases. Pick Dengue Cases (Any) - Weekly exactly.

Save, then Start dry run. It checks the data without starting anything, and should say All 18 locations can be successfully imported.

Close it and choose Start import. The app moves to Jobs, where the evaluation runs. With Simple Multistep it took about 24 seconds on an Apple Silicon Mac (M2 Max), where the model runs under emulation. chap-devops jobs in the terminal shows the same job; its type is create_backtest_from_data.

4. Read the result

Back in Evaluate, Overview, open the evaluation. The chart shows the real cases (orange) and, for the period picked under Split period, the model’s forecast with its 50% and 80% prediction intervals. The list on the left switches between all of Laos and one province.

Evaluation metrics, lower on the right, scores the forecasts over every split and province: lower is better for CRPS (log1p), MAE and RMSE, and Coverage 10-90 should be close to its target of 0.8 (80% of the real values inside the 80% interval). Show all 13 metrics lists the others.

To compare two models, run a second evaluation on the same data:

1. On the first evaluation, choose Create new based on…. It asks which settings to copy; leave them all ticked and choose Create.
2. The copy has the name, provinces, model, data mapping and periods of the first, and its name ends in (Copy).
3. On the copy, Select model picks another model, for example Simple Multistep Model (chapkit) [Monthly selfhistory]. Then choose Start import.
4. In Evaluate, Compare, pick the first evaluation in the left box and the copy in the box beside it.

The evaluation of Monthly selfhistory took about 20 seconds. The comparison shows the two side by side, province by province; the box Location(s) starts with ten provinces selected. More marketplace models come with chap-devops models enable ID and chap-devops up (chap-devops models list shows the ids).

5. Make a forecast

On the evaluation, Create prediction setup keeps the model, the data mapping and the provinces under a name: type one in Setup name, leave Alert policy at No alert policy and Set default import mapping off, and Save. The app then opens the setup. Once the evaluation has a setup, the same button says Predict.

On the setup, choose Run prediction. The page fills in a name for the run. In Training period, set the last training period to the last month with case data: 2024 December for the demo data. Then choose Run prediction again. The app goes back to the setup, where the run shows as Running. The forecast covers the three months after that month.

The job took about 7 seconds in chap-devops jobs. The row in the app said Success within a minute, without a reload. Go to last run shows one chart per province: the real cases up to December 2024 and the forecast for January to March 2025.

Import, on the right of the forecast, opens a page that writes the forecast back into DHIS2 as data, where dashboards and maps can use it. It needs five data elements for the forecast’s quantiles. Each box is empty at first; open it, and it lists the elements of the demo database under Suggested. Pick CHAP Dengue Cases (Any) - Weekly Quantile High to ... Quantile Low.

Use alert outputs is on by default, and then the page also needs an Outbreak indicator: Clear and import does nothing until it has one, and the message is under the box, below the first screen. Pick CHAP Dengue Cases (Any) - Weekly Outbreak Alert; the box does not suggest it, so type Outbreak to find it. Or turn Use alert outputs off. Clear and import asks once more before it writes, then the app goes back to the setup. DHIS2 2.42.6 accepted the monthly values into those elements, but their data set is weekly. For a monthly forecast, create monthly data elements in DHIS2’s Maintenance app and pick those.

When something goes wrong

More on connecting DHIS2 and Chap: DHIS2. The Modeling App’s own guide is at chap.dhis2.org.