Welcome to Epitools's documentation!

Python toolkit for analysis, modelling and forecasting Epidemic.

Authors:

Souvik Manik, Sabyasachi Pal, Manoj Mandal.

Version:

1.0.0

Key features

• Epidemic outbreak modeling and short-term forecasting using predefined fitted growth functions.

• Estimation of time-dependent transmission coefficients and efective reproduction number.

• Epidemic dynamics modeling and estimation of transmission epidemiological parameters using the Runge-Kutta initial value problem solver and iterative (limited memory) BFGS optimizer.

• Epidemic decay dynamics modeling with time dependent contact rate using different decay functions (exp, power, tanh).

• Estimation of effective reproduction number using Kalman filtering techniques directly from the real-time infection data.

Documentation

• Installation
• Eptools description
  • Flowchart of Epitools usage
  • Modelling and forecasting epidemic
    • Simulations
    • Example dataset
• Tutorial for epidemic outbreak modeling
  • 1. Initial outbreak modelling using Exponential function
  • 2. Outbreak modelling using modified logistic growth model
• Tutorial for epidemic dynamics modeling
  • Available epidemiological models in Epitools
    • SIR model
    • SIRD model
    • SEIR model
    • SEIRD model
  • Two-step aproach for epidemiological modeling using Epitools
    • 1. Estimation of real-time transmission coefficients and reproduction numbers from epidemic data using estimate_tc class
    • 2. Fitting and prediction of Epidemic scenarios using EpiDynamicsModel class of Epitools

Our publications

1. Manik, S., Pal, S., Mandal, M. et al. “Effect of 2021 assembly election in India on COVID-19 transmission.” Nonlinear Dyn 107, 1343–1356 (2022). https://doi.org/10.1007/s11071-021-07041-7.

2. Manik, Souvik, et al. “Impact of climate on COVID-19 transmission: A case study with Indian states.” medRxiv (2020): 2020-07.

3. Manik, Souvik, et al. “Impact of Environmental Factors on COVID-19 Transmission: an Overview. ” (In press).

4. Manik, Souvik, et al. “Epitools: A Python Package for Modelling and Forcasting Epidemic.” medRxiv (2020): (In press).