https://github.com/scai-bio/dynamic_ensemble

A Dynamic Ensemble Model for short-term Forecasting in Pandemic Situations
https://github.com/scai-bio/dynamic_ensemble

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A Dynamic Ensemble Model for short-term Forecasting in Pandemic Situations

• Host: GitHub
• URL: https://github.com/scai-bio/dynamic_ensemble
• Owner: SCAI-BIO
• Created: 2024-03-04T12:54:07.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-09-18T10:42:13.000Z (8 months ago)
• Last Synced: 2024-09-18T14:19:40.696Z (8 months ago)
• Language: Python
• Homepage:
• Size: 39.1 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# A Dynamic Ensemble Model for short-term Forecasting in Pandemic Situations - Botz et al 

This repository is part of the publication entitled "a dynamic ensemble model for short-term forecasting in pandemic situations" (https://journals.plos.org/globalpublichealth/article?id=10.1371/journal.pgph.0003058). In the following we will describe how this repository is organized and how to run the scripts.

For questions please contact Jonas Botz ([email protected]).

## Organization:

    .

    ├── environment.yml

    ├── README.md

    └── src                                 -> includes necessary scripts for loading data and running models  

        ├── data                            -> scripts for data loading and processing

        │   ├── datasets.py

        │   ├── __init__.py

        │   └── load_data.py

        ├── __init__.py

        ├── models                          -> scripts for modeling, tuning and evaluation

        │   ├── arima.py                    -> ARIMA base-model

        │   ├── __init__.py

        │   ├── main_ens_model.py           -> meta-model without metadata

        │   ├── main_meta.py                -> meta-model with metadata

        │   ├── main_optuna_ens.py          -> optuna tuning for meta-model without metadata

        │   ├── main_optuna_LSTM.py         -> optuna tuning for LSTM base-model

        │   ├── main_optuna_meta.py         -> optuna tuning for meta-model with metadata

        │   ├── main_optuna_trees.py        -> optuna tuning for XGBoost and Random Forest 

        │   ├── main.py                     -> script for running tuned basemodel and baseline ensemble methods

        │   ├── networks.py                 -> LSTM base-model and meta-model

        │   ├── parser.py                   -> configurations

        │   ├── regression.py               -> Linear Regression 

        │   ├── solver_ens_model.py         -> for running meta-model without metadata

        │   ├── solver_meta_model.py        -> for running meta-model with metadata

        │   ├── solver_optuna.py            -> for running optuna tuning for LSTM base-model

        │   ├── solver.py                   -> for running LSTM base-model

        │   ├── test.py                     -> for testing LSTM and Regression base-models

        │   ├── train_ens_model.py          -> for training meta-model without metadata

        │   ├── train_meta_model.py         -> for training meta-model with metadata

        │   ├── train_optuna.py             -> for training LSTM base-model in optuna tuning

        │   ├── train.py                    -> for training LSTM base-model

        │   ├── tree_models.py              -> XGBoost and Random Forest

        │   ├── utils.py                    -> Definition of Metrics

        │   └── visualizations.py           -> Barplots, Violinplots and Boxplots

        └── README.md

## Data:

We used German COVID-19, Influenza and SARI Surveillance Data provided by the RKI, available here: https://github.com/robert-koch-institut

We used French COVID-19 Surveillance Data provided by SPF, available here: https://www.data.gouv.fr/fr/organizations/sante-publique-france

The metadata was accessed via the Google Trends API. For access you have to apply here: https://docs.google.com/document/d/1Ybu3gHUHtcSXXzgDJ-m7PPto9tw0QG8A5oOBsFP2jao/edit?pli=1#heading=h.qye80d9e325z

Then follow Danqi et al.: https://www.nature.com/articles/s41598-023-48096-3

For smoothing we applied a centered moving average over seven days (for the COVID-19 and metadata).

## Configuration Parameters:

For running the scripts following configuration parameters need to be set (we also mention the parameters that we used):

1. iterations - number of test windows (140 for COVID, 30 for Influenza, 80 for SARI)

2. period - training period (70 for daily data, 52 for weekly data)

3. size_SW - fitting window size (7 for daily data, 5 for weekly data)

4. size_PW - prediction window size (14 for daily data, 2 for weekly data)

5. exp_name - name of the experiment to be correctly stored

6. num_trials - number of optuna trials

7. num_epochs - number of epochs the LSTM or meta-model are trained 

There are more, which are specific to the models, for example the prediction type (stacking or selection) for the meta-model. How to set these should become clear when looking at the corresponding scripts. 

## Model Running:

1. Hyperparameter Tuning Base-Models:

    - main_optuna_LSTM.py

    - main_optuna_trees.py

2. Base-Model and Baseline Ensemble Evaluation:

    - main.py (check that the correct optuna databases are selected)

3. Hyperparameter Tuning for Meta-Model:

    - main_optuna_ens.py

    - main_optuna_meta.py*

4. Meta-Model Evaluation:

    - main_ens_model.py

    - main_meta.py*

*Only if metadata is available

The results for step 2 should be stored and are used for steps 3 and 4. Further the results for step 4 are stored.