CovsirPhy: COVID-19 data with SIR model

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

SIR-F is a customized SIR-derived ODE model. To evaluate the effect of measures, parameter estimation of SIR-F will be applied to subsets of time series data in each country. Parameter change points will be determined by S-R trend analysis.

Functionalities

Downloading and cleaning data
- Epidemic data: the number of confirmed/fatal/recovered cases
- Population data: raw data must include country, (province), values of population
Data visualization with Matplotlib
S-R Trend analysis with Optuna and scipy.optimize.curve_fit
Numerical simulation of ODE models with scipy.integrate.solve_ivp
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 with Optuna and numerical simulation
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

Trying now

The author is trying to add the following functionalities.

Speed-up S-R trend analysis and hyperparameter estimation of ODE models
Keep track parameter values/reproductive number of all countries with a simple code
Find relationship of reproductive number and measures automatically

If you have ideas or need new functionalities, please join this project. Any suggestions with Github Issues are always welcomed.

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

We will use the following datasets.

	Description	URL
The number of cases (JHU)	COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University.	https://github.com/CSSEGISandData/COVID-19
The number of cases in Japan	Lisphilar (2020), COVID-19 dataset in Japan.	https://github.com/lisphilar/covid19-sir/tree/master/data
Population in each country	The World Bank Group (2020), THE WORLD BANK, Population, total.	https://data.worldbank.org/indicator/SP.POP.TOTL
Government Response Tracker (OxCGRT)	Thomas Hale, Sam Webster, Anna Petherick, Toby Phillips, and Beatriz Kira. (2020). Oxford COVID-19 Government Response Tracker. Blavatnik School of Government.	https://github.com/OxCGRT/covid-policy-tracker

If you want to use a new dataset for your analysis, please kindly inform us via GitHub Issues with "Request new method of DataLoader class" template.

1. 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()

2. Developers

Developers will clone this repository with git clone command and install dependencies with pipenv.

git clone https://github.com/lisphilar/covid19-sir.git
cd covid19-sir
pip install wheel; pip install --upgrade pip; pip install pipenv
export PIPENV_VENV_IN_PROJECT=true
export PIPENV_TIMEOUT=7200
pipenv install --dev

Developers can perform tests with pipenv run pytest -v --durations=0 --profile-svg and call graph will be saved as SVG file (prof/combined.svg).

Windows user need to install Graphviz for Windows in advance.
Debian/Ubuntu user need to install Graphviz with sudo apt install graphviz in advance.

If you can run make command,


`make install`	Install pipenv and the dependencies of CovsirPhy
`make test`	Run tests using Pytest
`make docs`	Update sphinx document
`make example`	Run example codes
`make clean`	Clean-up output files and pipenv environment

We can prepare the dataset with the same codes as that was explained in "1.Preferred" subsection.

3. Kagglers (local environment)

As explained in "2. Developers" subsection, we need to git-clone this repository and install the dependencies when you want to uses this package with Kaggle API in your local environment.

Then, please move to account page and download "kaggle.json" by selecting "API > Create New API Token" button. Copy the json file to the top directory of the local repository. Please refer to How to Use Kaggle: Public API and stackoverflow: documentation for Kaggle API within python?

We can download datasets with pipenv run ./input.py command. Modification of environment variables is un-necessary. Files will be saved in input directory of your local repository.

Note:
Except for OxCGRT dataset, the datasets downloaded with input.py scripts are different from that explained in the previous subsections. URLs are shown in the next table.

	Description	URL
The number of cases (JHU)	Novel Corona Virus 2019 Dataset by SRK	https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset
The number of cases in Japan	COVID-19 dataset in Japan by Lisphilar	https://www.kaggle.com/lisphilar/covid19-dataset-in-japan
Population in each country	covid19 global forecasting: locations population by Dmitry A. Grechka	https://www.kaggle.com/dgrechka/covid19-global-forecasting-locations-population
Government Response Tracker (OxCGRT)	Thomas Hale, Sam Webster, Anna Petherick, Toby Phillips, and Beatriz Kira. (2020). Oxford COVID-19 Government Response Tracker. Blavatnik School of Government.	https://github.com/OxCGRT/covid-policy-tracker

Usage of DataLoader class is as follows. Please specify local_file argument in the methods.

import covsirphy as cs
data_loader = cs.DataLoader("input")
jhu_data = data_loader.jhu(local_file="covid_19_data.csv")
japan_data = data_loader.japan(local_file="covid_jpn_total.csv")
population_data = data_loader.population(local_file="locations_population.csv")
oxcgrt_data = data_loader.oxcgrt(local_file="OxCGRT_latest.csv")

4. Kagglers (Kaggle platform)

When you want to use this package in Kaggle notebook, please turn on Internet option in notebook setting and download the datasets explained in the previous section.

Then, install this package with pip command.

!pip install covsirphy

Then, please load the datasets with the following codes, specifying the filenames.

import covsirphy as cs
# The number of cases (JHU)
jhu_data = cs.JHUData("/kaggle/input/novel-corona-virus-2019-dataset/covid_19_data.csv")
# (Optional) The number of cases in Japan
jpn_data = cs.CountryData("/kaggle/input/covid19-dataset-in-japan/covid_jpn_total.csv", country="Japan")
jpn_data.set_variables(
    date="Date", confirmed="Positive", fatal="Fatal", recovered="Discharged", province=None
)
# Population in each country
pop_data = cs.Population(
    "/kaggle/input/covid19-global-forecasting-locations-population/locations_population.csv"
)

Note:
Currently, OxCGRT dataset is not supported.

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 the records in Japan, 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

Show S-R trend to determine the number of change points.

ita_scenario.trend()

As an example, set the number of change points as 4.

ita_scenario.trend(n_points=4, set_phases=True)

Start/end date of the four phase were automatically determined. Let's see.

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

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

CovsirPhy development team, 2020, GitHub repository, CovsirPhy, Python package for COVID-19 data with SIR model, https://github.com/lisphilar/covid19-sir

SM-ins/covid19-sir