This is a framework for fitting a model to coronavirus data. It uses differential evolution to optimse a model fit.

This uses python v3.

Install numpy,matplotlib,scypi,pandas using

pip install

pymc3 likely to come soon.

We are currently using data from European Centre for Disease Prevention and Control. To get the latest version use the following command...

wget https://opendata.ecdc.europa.eu/covid19/casedistribution/csv -O cases.csv

All dates in the model are calculated as days since the origin (1 Dec 2019).

It expects a computer with at least 2 cores, but you can adjust that by changing the 'workers' setting.

The models I have added are some simple extensions to the Oxford model. (See https://www.medrxiv.org/content/10.1101/2020.03.24.20042291v1). The two model either have a lockdown phase with a lower beta value, or a proportion of the population which are excluded from getting infected after the lockdown. An interesting note about the Oxford model is that they set a proportion of infected+recovered individuals to die. In this case, the death rate will never go down.

I have also added the ability to fit the same model to multiple countries, adapting some of the parameters (like transmission rate) to each country.

I was thinking about adding MCMC to this, so it will do forecasting. However, I think finding a good model that fits the data first is probably a better plan.