Package for COVID-19 analysis with phase-dependent SIR-derived ODE models
Project description
CovsirPhy introduction
Documentation | Installation | Quickest usage | API reference | Qiita (Japanese)
CovsirPhy is a Python package for COVID-19 (Coronavirus disease 2019) data analysis with phase-dependent SIR-derived ODE models. We can download datasets and analyse it easily. This will be a helpful tool for data-informed decision making. Please refer to “Method” part of Kaggle Notebook: COVID-19 data with SIR model to understand the methods.
Functionalities
Data preparation and data visualization
Numerical simulation of ODE models
Stable: SIR, SIR-D and SIR-F model
Development: SIR-FV and SEWIR-F model
Phase-dependent parameter estimation of ODE models
Scenario analysis: Simulate the number of cases with user-defined parameter values
(In development): Find the relationship of government response and parameter values
Inspiration
Monitor the spread of COVID-19
Keep track parameter values/reproduction number in each country/province
Find the relationship of reproductive number and measures taken by each country
If you have ideas or need new functionalities, please join this project. Any suggestions with Github Issues are always welcomed. Please read Guideline of contribution in advance.
Installation
The latest stable version of CovsirPhy is available at PyPI (The Python Package Index): covsirphy and supports Python 3.6 or newer versions.
pip install --upgrade covsirphy
Development versions are in GitHub repository: CovsirPhy.
pip install --upgrade "git+https://github.com/lisphilar/covid19-sir.git#egg=covsirphy"
Usage
Quickest tour of CovsirPhy is here. The following codes analyze the records in Japan, but we can change the country name when creating Scenario class instance for your own analysis.
import covsirphy as cs
# Download and update datasets
data_loader = cs.DataLoader("input")
jhu_data = data_loader.jhu()
population_data = data_loader.population()
# Check records
snl = cs.Scenario(jhu_data, population_data, country="Japan")
snl.records()
# S-R trend analysis
snl.trend().summary()
# Parameter estimation of SIR-F model
snl.estimate(cs.SIRF)
# History of reproduction number
_ = snl.history(target="Rt")
# History of parameters
_ = snl.history_rate()
_ = snl.history(target="rho")
# Simulation for 30 days
snl.add(days=30)
_ = snl.simulate()
Further information:
Support
License: Apache License 2.0
Please refer to LICENSE file.
Citation
We have no original papers the author and contributors wrote, but please cite this package as follows.
CovsirPhy Development Team (2020), CovsirPhy, Python package for COVID-19 analysis with SIR-derived ODE models, https://github.com/lisphilar/covid19-sir
If you want to use SIR-F/SIR-FV/SEWIR-F model, S-R trend analysis, phase-dependent approach to SIR-derived models, and other scientific method performed with CovsirPhy, please cite the next Kaggle notebook.
Lisphilar (2020), Kaggle notebook, COVID-19 data with SIR model, https://www.kaggle.com/lisphilar/covid-19-data-with-sir-model
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