The growth of case numbers concerning the recent COVID-19 outbreak in provinces of Mainland China can be modeled by a new SIR containment model. This is a complimentary repository that contains the data and the analysis discussed in the paper B. F. Maier and D. Brockmann, "Effective containment explains sub-exponential growth in confirmed cases of recent COVID-19 outbreak in Mainland China", 2020.
The json-file data/all_confirmed_cases_with_population.json contains case number data
of the currently affected provinces in China as well as population size.
The time series count the aggregate number of people whose infection was laboratory-confirmed. It was gathered by the Johns Hopkins University Center for Systems Science and Engineering.
For the data contained in mainland_china, all province data except the one from Hubei
was aggregated by means of interpolation.
Since Feb 12 the case data includes symptomatic cases without lab-confirmation, as well, therefore we only consider data from before Feb 12 6am.
Written and tested for Python 3.7
pip install requirements.txtThese are the requirements:
simplejson==3.16.0
numpy==1.17.2
scipy==1.3.1
bfmplot==0.0.7
lmfit==0.9.12
tabulate==0.8.2
matplotlib==3.0.2
tqdm==4.28.1
Reproduce plots
cd main_results
python model_large_hubei_and_mainland_china.py fit_parameters/hubei_china.p
python model_fit_confirmed_cases_500.py fit_parameters/confirmed_cases_500.p
In case you want new fits, do
python model_large_hubei_and_mainland_china.py
python model_fit_confirmed_cases_500.py
The fit parameters are saved in main/results/fit_parameters/confirmed_cases_500.p
Works similarly for the other analysis scripts.