Skip to content

Lars-G/covid19_inference_forecast

 
 

Repository files navigation

Bayesian inference and forecast of COVID-19

Documentation Status License: GPL v3 DOI

Modeling forecast scenarios in Germany (updated figures of the paper)

Our aim is to quantify the effects of intervention policies on the spread of COVID-19. To that end, we built a Bayesian SIR model where we can incorporate our prior knowledge of the time points of governmental policy changes. While the first two change points were not sufficient to switch from growth of novel cases to a decline, the third change point (the strict contact ban initiated around March 23) brought this crucial reversal. - Now, a number of stores have been opened and policies have been loosened on the one hand, which may lead to increased spreading (increased $\lambda^\ast$). On the other hand, masks are now widely used and contact tracing might start to show effect, which both may reduce the spread of the virus (decrease $\lambda^\ast$). We will only start to see the joint effects of the novel govenrmental policies and collective behavior with a delay of 2-3 weeks. Therefore, we show alternative future scenarios here.

Daily updated scenarios

The following scenarios are run daily and should use up to date data from the Robert Koch Institute.

Scenario using weekly changepoints and reporting date data

  • Daily updated cases by reporting date are retrieved from the arcgis dashboard.

Scenario using weekly changepoints and nowcasting data

  • Daily updated nowcasting data is available at the Robert Koch Institute, but is delayed by four days to one week.

Alternative forecast scenarios, projecting the relaxation of restrictions on May 11

  • If the effective growth rate stays on the current (all-time low) value, new cases will further decrease (green). A low number of new daily cases might bring a full control of the spread within reach (see our position paper by the four German research associations; Endorsement; Position paper).

  • If the relaxation of restrictions causes an increase in effective growth rate above zero, the daily new reported cases will increase again (red).

The current scenarios are based on the model that incorporates weekly reporting modulation (less cases reported on weekends).

Scenario focus on three change points

Scenario assuming three change points with a weekly modulation of reported cases

What-if scenarios

What if the growth would have continued with less change points?

We fitted the four scenarios to the number of new cases until respectively March 18th, March 25th, April 1st and April 7th.

This figure was used widely in German media, including TV, to illustrate the magnitude of the different change points.

About

No description, website, or topics provided.

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages

  • Jupyter Notebook 91.7%
  • Python 8.3%