covsirphy 2.4.1

Package for COVID-19 analysis with SIR-derived ODE models

CovsirPhy is a Python package for COVID-19 (Coronavirus disease 2019) data analysis with SIR-derived ODE models. Please refer to “Method” part of COVID-19 data with SIR model notebook in Kaggle to understand the methods.

Functionalities

• Downloading and cleaning data: refer to “Installation and dataset preparation” section

• Data visualization

• S-R Trend analysis to determine the change points of parameters

• Numerical simulation of ODE models

• Description of ODE models

  • Basic class of ODE models

  • SIR, SIR-D, SIR-F, SIR-FV and SEWIR-F model

• Parameter Estimation of ODE models

• Scenario analysis: Simulate the number of cases with user-defined parameter values

Inspiration

• Monitor the spread of COVID-19

• Keep track parameter values/reproductive number in each country/province

• Find the relationship of reproductive number and measures taken in each country/province

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 and dataset preparation

We have the following options to start analysis with CovsirPhy. Datasets are not included in this package, but we can prepare them with DataLoader class.

	Installation	Dataset preparation
Standard users	pip/pipenv	Automated with DataLoader class
Developers	git-cloning	Automated with DataLoader class
Kagglers (local environment)	git-cloning	Kaggle API and Python script
Kagglers (Kaggle platform)	pip	Kaggle Datasets

Installation and dataset preparation explains how to install and prepare datasets for all users.

Standard users

Covsirphy is available at PyPI (The Python Package Index): covsirphy and supports Python 3.7 or newer versions.

pip install covsirphy

Then, download the datasets with the following codes, when you want to save the data in input directory.

import covsirphy as cs
data_loader = cs.DataLoader("input")
jhu_data = data_loader.jhu()
japan_data = data_loader.japan()
population_data = data_loader.population()
oxcgrt_data = data_loader.oxcgrt()

If input directory has the datasets, DataLoader will load the local files. If the datasets were updated in remote servers, DataLoader will update the local files automatically.

We can get descriptions of the datasets and raw/cleaned datasets easily. As an example, JHU dataset will be used here.

# Description (string)
jhu_data.citation
# Raw data (pandas.DataFrame)
jhu_data.raw
# Cleaned data (pandas.DataFrame)
jhu_data.cleaned()

We can get COVID-19 Data Hub citation list of primary sources as follows.

data_loader.covid19dh_citation

Quick usage for analysis

Example Python codes are in example directory. With Pipenv environment, we can run the Python codes with Bash code example.sh in the top directory of this repository.

Preparation

import covsirphy as cs
cs.__version__

Please load the datasets as explained in the previous section.

(Optional) We can replace a part of JHU data with country-specific datasets. As an example, we will use the records in Japan here because values of JHU dataset sometimes differ from government-announced values as shown in COVID-19: Government/JHU data in Japan.

jhu_data.replace(japan_data)
ncov_df = jhu_data.cleaned()

Scenario analysis

As an example, use dataset in Italy.

Check records

ita_scenario = cs.Scenario(jhu_data, population_data, country="Italy", province=None)

See the records as a figure.

ita_record_df = ita_scenario.records()

S-R trend analysis

Perform S-R trend analysis and set phases to the scenario. The number of change points will be determined automatically (>= 2.4.0).

ita_scenario.trend(set_phases=True)
print(ita_scenario.summary())

Hyperparameter estimation of ODE models

As an example, use SIR-F model.

ita_scenario.estimate(cs.SIRF)
print(ita_scenario.summary())

We can check the accuracy of estimation with a figure.

# Table
ita_scenario.estimate_accuracy(phase="1st")
# Get a value
ita_scenario.get("Rt", phase="4th")
# Show parameter history as a figure
ita_scenario.param_history(targets=["Rt"], divide_by_first=False, box_plot=False)
ita_scenario.param_history(targets=["rho", "sigma"])

Prediction of the number of cases

we can add some future phases.

# if needed, clear the registered future phases
ita_scenario.clear(name="Main")
# Add future phase to main scenario
ita_scenario.add_phase(name="Main", end_date="01Aug2020")
# Get parameter value
sigma_4th = ita_scenario.get("sigma", name="Main", phase="4th")
# Add future phase with changed parameter value to new scenario
sigma_6th = sigma_4th * 2
ita_scenario.add_phase(end_date="31Dec2020", name="Medicine", sigma=sigma_6th)
ita_scenario.add_phase(days=30, name="Medicine")
print(ita_scenario.summary())

Then, we can predict the number of cases and get a figure.

# Prediction and show figure
sim_df = ita_scenario.simulate(name="Main")
# Describe representative values
print(ita_scenario.describe())

Apache License 2.0

Please refer to LICENSE file.

Citation

CovsirPhy Development Team (2020), CovsirPhy, Python package for COVID-19 analysis with SIR-derived ODE models, https://github.com/lisphilar/covid19-sir

Related work

Method of analysis in CovsirPhy:

Lisphilar (2020), Kaggle notebook, COVID-19 data with SIR model, https://www.kaggle.com/lisphilar/covid-19-data-with-sir-model

Reproduction number evolution in each country:

Ilyass Tabiai and Houda Kaddioui (2020), GitHub pages, COVID19 R0 tracker, https://ilylabs.github.io/projects/COVID-trackers/