Coronavirus Dashboard – Operational Hindcast/Forecast

I wrote a Plotly Reactive Dash app to run the Coronavirus operational hindcast/forecast model written in R code by James D. Annan and Julia C. Hargreaves.

The medRxiv preprint describing their approach is described here:

Model calibration, nowcasting, and operational prediction of the COVID-19 pandemic, James D. Annan and Julia C. Hargreaves (2020)

James and Julia’s hindcast/forecast model is important as it effectively recalibrates the SEIR model as each daily update becomes available.

The hindcast/forecast model simulation is manifested by 5000 runs using Monte Carlo Markov chains for 6 model parameters:

Variable 1Latent period [days]
Variable 2Infectious period [days]
Variable 30.5 x Initial infection rate
Variable 4Death rate
Variable 5Initial reproductive rate, R0
Variable 6Post-lockdown reproductive rate, Rt

whose MCMC traces, kernel density estimates and correlation matrix are displayed in the app which is up and running now on Heroku:

and here is the codebase for issue checking on Github:

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