epydemics 0.7.0

Advanced epidemiological modeling and forecasting using discrete SIRD/SIRDV models with time series analysis

Epydemics: Forecasting COVID-19 using time series and machine learning

Version 0.7.0 - SIRDV Vaccination Model Release

epydemics is a Python library for epidemiological modeling and forecasting. It provides tools for creating, fitting, and evaluating discrete SIRD/SIRDV models with time-dependent parameters. The library is designed to be flexible and extensible, allowing users to easily incorporate their own data and models.

Features

• Discrete SIRD Model: A discrete Susceptible-Infected-Recovered-Deceased (SIRD) model with time-dependent parameters.
• SIRDV Vaccination Model (v0.7.0): Extended model including Vaccinated compartment (V) and vaccination rate (δ).
• Time Series Forecasting: Use of VAR (Vector Autoregression) models to forecast epidemic rates with logit transformation.
• Data Container: A convenient class for loading, preprocessing, and storing epidemiological data.
• Parallel Simulations: Multi-core support for faster Monte Carlo simulations (27 scenarios for SIRD, 81 for SIRDV).
• Result Caching: File-based caching to avoid recomputing identical analyses.
• Model Evaluation: Tools for evaluating model performance with MAE, MSE, RMSE, MAPE, SMAPE metrics.
• Visualization: Professional plotting functions for results and forecasts.

SIRDV Model (New in v0.7.0)

The SIRDV model extends the classical SIRD model by incorporating vaccination:

Compartments:

• S: Susceptible
• I: Infected (active cases)
• R: Recovered
• D: Deaths
• V: Vaccinated (new)

Rates:

• α: Infection rate
• β: Recovery rate
• γ: Mortality rate
• δ: Vaccination rate (new)

Key Features:

• Automatic detection from vaccination data
• 81 simulation scenarios (3⁴ confidence levels)
• Conservation law: N = S + I + R + D + V
• Parallel execution recommended for performance

Getting Started

To get started with epydemics, we recommend following the tutorial in TUTORIAL.md.

Installation

You can install epydemics from PyPI:

pip install epydemics

To install the latest development version, you can clone this repository and install it in editable mode:

git clone https://github.com/julihocc/epydemics.git
cd epydemics
pip install -e .

Documentation

• TUTORIAL.md: A step-by-step guide to using epydemics for COVID-19 forecasting.
• ARCHITECTURE.md: A high-level overview of the project's architecture.
• CONTRIBUTING.md: Instructions for contributing to the project.

Further work

There are many ways to extend and improve epydemics. Some possible directions for future work include:

• More advanced time series models: The current version of epydemics uses a simple time series model for forecasting the SIRD parameters. More advanced models, such as SARIMAX or Prophet, could be used to improve the accuracy of the forecasts.
• Support for other epidemiological models: epydemics could be extended to support other epidemiological models, such as the SEIR model.
• Improved visualization: The visualization tools in epydemics could be improved to provide more insights into the dynamics of the pandemic.
• More comprehensive documentation: The documentation for epydemics could be improved to provide more detailed explanations of the models and the code.

References

Allen u.a. 2008 Allen, L.J.S. ; Brauer, F. ; Driessche, P. van den ; Bauch, C.T. ; Wu, J. ; Castillo-Chavez, C. ; Earn, D. ; Feng, Z. ; Lewis, M.A. ; Li, J. u.a.: Mathematical Epidemiology. Springer Berlin Heidelberg, 2008 (Lecture Notes in Mathematics).– URL https://books. google.com/books?id=gcP5l1a22rQC.– ISBN 9783540789109

Andrade u.a. 2021 Andrade, Marinho G. ; Achcar, Jorge A. ; Conce icc˜ ao, Katiane S. ; Ravishanker, Nalini: Time Series Regression Models for COVID-19 Deaths. In: J. Data Sci 19 (2021), Nr. 2, S. 269–292

Hawas 2020 Hawas, Mohamed: Generated time-series prediction data of COVID-19s daily infections in Brazil by using recurrent neural networks. In: Data in brief 32 (2020), S. 106175

Maleki u.a. 2020 Maleki, Mohsen ; Mahmoudi, Mohammad R. ; Wraith, Darren ; Pho, Kim-Hung: Time series modelling to forecast the confirmed and recovered cases of COVID-19. In: Travel medicine and infectious disease 37 (2020), S. 101742

Martcheva 2015 Martcheva, M.: An Introduction to Mathematical Epi demiology. Springer US, 2015 (Texts in Applied Mathematics).– URL https: //books.google.com/books?id=tt7HCgAAQBAJ.– ISBN 9781489976123

Singh u.a. 2020 Singh, Vijander ; Poonia, Ramesh C. ; Kumar, Sandeep ; Dass, Pranav ; Agarwal, Pankaj ; Bhatnagar, Vaibhav ; Raja, Linesh: Prediction of COVID-19 coronavirus pandemic based on time series data using Support Vector Machine. In: Journal of Discrete Mathematical Sciences and Cryptography 23 (2020), Nr. 8, S. 1583–1597

Wacker und Schluter 2020 Wacker, Benjamin; Schluter, Jan: Time continuous and time-discrete SIR models revisited: theory and applications. In: Advances in Difference Equations 2020 (2020), Nr. 1, S. 1–44.– ISSN 1687-1847.– URL https://doi.org/10.1186/s13662-020-02907-9