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:
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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.
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RJAGS package of Martyn Plummer to perform MCMC to obtain posterior estimates
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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.