Sirum

Repository to experiment with SIR-like models. The purpose is not to develop new SIR-models but rather to create a thin wrapper around existing SIR-implementations to allow for easy experimentation.

To start with:

We take the minimal SIR-model and combine it with a blackbox-optimiser like Optuna or Hyperopt
Expand the complexity of the SIR-model (SEIR, SEIRSPLUS, SIRF, SIS, SIRD, etc.)
Try different model paradigms?

Research questions:

what is the required immunity
how to include/extract the required IC-capacity in/from the model?

I.e. we have the following basic components: {{intial conditions}-> {SIR-model}} <-> {Optimisation/fitting}

Problems:

the population is either assumed perfectly mixed or the parameters are identical over all stratifications -> this will lead to a gross over-estimation of the number of death/critically ill if the mildly affected patients are separated from the weak patients: SEIRSplus handles this.
it does not have spatially separated population hubs that interact
it does not have spatially centrally connected points where people interact

Suggestions for improvement:

the mild, moderate and clinical patients should be physically separated in 3 models that interact
those 3 models should operate in individual population hubs
those population hubs should have central connection points

online calculators and dashboards:

https://alhill.shinyapps.io/COVID19seir/ (SEIR)
https://gabgoh.github.io/COVID/index.html (SEIR)
https://github.com/ryansmcgee/seirsplus ****(SEIR)
https://covid19dashboards.com/covid-overview/

Crowd initiatives:

https://crowdfightcovid19.org/

Kaggle competitions: https://www.kaggle.com/covid19

Good notebooks:

Literature sources:

Data sources:

Specific papers:

Explanations:

UPOD-datascience / Sirum

readme

Sirum