Relationship between weather and influenza incidents

Project Link

Zenodo badge

Table of Contents

• About the Project
  • Built With
• Getting Started
  • JupyterLab
  • Jupyter Notebook
• Usage
• Contributing
• Contact

About The Project

The goal of this experiment was to model the relationship between weather observations and the prevalence of new influenza infections. It included reading, preparing and transforming data. Subsequently, these data were visualized and used for building a prediction model - we tried to predict influenza infections based on weather conditions.

Built With

This section should list any major frameworks that you built your project using. Leave any add-ons/plugins for the acknowledgements section. Here are a few examples.

• Jupyter

Getting Started

JupyterLab

Installation

JupyterLab can be installed using conda or pip. For more detailed instructions, consult the installation guide.

conda

If you use conda, you can install it with:

conda install -c conda-forge jupyterlab

pip

If you use pip, you can install it with:

pip install jupyterlab

If installing using pip install --user, you must add the user-level bin directory to your PATH environment variable in order to launch jupyter lab.

Jupyter Notebook

Prerequisite: Python While Jupyter runs code in many programming languages, Python is a requirement (Python 3.3 or greater, or Python 2.7) for installing the JupyterLab or the classic Jupyter Notebook.

Installation

conda

We recommend installing Python and Jupyter using the conda package manager. The miniconda distribution includes a minimal Python and conda installation.

Then you can install the notebook with:

conda install -c conda-forge notebook

pip

If you use pip, you can install it with:

pip install notebook

To run the notebook, run the following command at the Terminal (Mac/Linux) or Command Prompt (Windows):

jupyter notebook

See Running the Notebook for more details.

Usage

Code snippet

This is a python snippet of SVR prediction:

target_column = ['weekly_infections']
predictors = list(set(list(data_merged.columns))-set(target_column))
data_merged[predictors] = stats.zscore(data_merged[predictors])
#data_merged.describe()

X = data_merged[predictors].values
y = data_merged[target_column].values

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=20)
#print(X_train.shape); print(X_test.shape)

####Support vector regression
#
svr = SVR(kernel = 'rbf', C=10000, gamma='auto', epsilon = 100)
svr.fit(X_train, y_train.ravel()) 
pred_train_svr= svr.predict(X_train)

pred_test_svr= svr.predict(X_test)
print("RMSE of testing data for SVR:\t",np.sqrt(mean_squared_error(y_test,pred_test_svr))) 
print("R2 of testing data  for SVR:\t",r2_score(y_test, pred_test_svr))

pred_final_data_svr = svr.predict(data_merged_predict[predictors])
print("Train:\n", pred_train_svr)
print("\nTest:\n", pred_test_svr)
print("\nFinal prediction:\n", pred_final_data_svr)

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch

git checkout -b feature/AmazingFeature

3. Commit your Changes ``console git commit -m 'Add some AmazingFeature'

4. Push to the Branch
``console
git push origin feature/AmazingFeature

5. Open a Pull Request

Contact

Ondrej Hudcovic - Regarding any questions, please contact me at [email protected]

ORCID