https://github.com/benmaier/covid19casenumbermodel
Fit the algebraic growth of case numbers in Mainland China with an SIR-containment model
https://github.com/benmaier/covid19casenumbermodel
Last synced: about 1 month ago
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Fit the algebraic growth of case numbers in Mainland China with an SIR-containment model
- Host: GitHub
- URL: https://github.com/benmaier/covid19casenumbermodel
- Owner: benmaier
- Created: 2020-02-16T23:54:23.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-07-06T21:54:26.000Z (almost 2 years ago)
- Last Synced: 2024-10-11T11:07:02.817Z (8 months ago)
- Language: Python
- Size: 22.5 MB
- Stars: 22
- Watchers: 5
- Forks: 9
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
[](https://zenodo.org/badge/latestdoi/240985561)
# COVID-19 case number growth
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](https://arxiv.org/abs/2002.07572).
## Data
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](https://github.com/CSSEGISandData/COVID-19).
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.
## Prerequisites
Written and tested for Python 3.7
### Requirements
```bash
pip install requirements.txt
```
These 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
```
## Examples
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.