SIR Compartmental Model

applied to data available for the spread of COVID-19 Coronavirus


COVID-19: SIR epidemic model fitting

(for a more complex and detailed model check this preprint)

Image's license CC BY-NC-SA 4.0

You can try using fitteia to fit any SIR-based compartment model to the statistical data of the people infected with coronavirus. As an example, we have used the SIRS model to fit the data collected (here) for USA, China, South Korea, Portugal, Italy, Spain, Germany, and Austria as well as France, Czechia, and Switzerland for previous days.

PLEASE NOTE THAT THIS IS NOT AN OFFICIAL MODEL AND IT IS PRESENTED HERE FOR EDUCATIONAL PURPOSES ONLY.

Please follow these instructions step by step:

  1. Choose and download to your computer the latest fitteia file(s): US, CN, IT, SP, DE, PT, AT, or KR; or from previous days.

  2. Login into your fitteia account (if your are a new user, please register first) and select your working folder by clicking on the respective link.

  3. Browse the file you have downloaded and click fitter->Upload.

  4. fitter->Upload->Return to the working folder.

  5. Select fitter-> Expert mode by clicking on the respective link.

  6. Select COVID-19_XX-SIRS, where XX represents {US, CN, IT, SP, DE, FR, PT, AT, CH, CZ, KR}, and click Recover.

  7. Play with the fits and be aware that you are fitting the solutions of a set of ordinary differential equations to a set of actual data.

Output examples

South Korea model data fit used as reference for the remaing data fits with exception of data from China data and Austria.

COVID-19 @ US

COVID-19 @ IT

COVID-19 @ SP

COVID-19 @ CN

COVID-19 @ DE

COVID-19 @ PT

COVID-19 @ AT

COVID-19 @ KR

SIRS model epidemic peaks

For the SIRS model depending one can refer the peak corresponding to moment when the number of active infected cases is maximum , or the moment when the rate of change of the number of infected people is maximum, or to the moment when maximum number of daily cases, or even the total nuber of cases.

Number of active infected cases normalized to the maximum value

Rate of change of active infected cases normalised to the maximum

Daily new cases

Total cases

Comparison

Simulation