Bayesian Modeling of Covid-19 Spread

A Bayesian approach of the modeling COVID19 one and two-wave spread based on the Gompertz equation via Markov Chain Monte Carlo (MCMC) simulations.

It needs:

• JAGS (https://sourceforge.net/projects/mcmc-jags/)
• RStudio ( https://rstudio.com)
• R (https://www.r-project.org)

It uses:

• the R Interface to COVID-19 Data Hub of Guidotti and Ardia to retrieve current COVID19 data worldwide.

  • See : https://cran.r-project.org/web/packages/COVID19/index.html and "COVID-19 Data Hub," Journal of Open Source Software, 5(51), 2376. doi: 10.21105/joss.02376.

• RJAGS package of Martyn Plummer to perform MCMC to obtain posterior estimates

  • See : https://cran.r-project.org/web/packages/rjags/index.html

• Packages for parallel computations (see full list in the "utilities" file):

  • foreach
  • doFuture
  • doRNG

It performs:

• A nonlinear parametric fit of the COVID19 spread (i.e., fatalities, confirmed cases, recovered) by country.

It provides:

• Both daily and cumulative spreading (fatalities, confirmed cases, recovered and active cases) plots.