pandemics

Full dynamic system to describe and forecast the spread and the severity of a developing pandemic, based on available data. These data are number of infections, hospitalizations, deaths and recoveries notified each day. The system consists of three transitions, infection-infection, infection-hospital and hospital-death/recovery. The intensities of these transitions are dynamic and estimated using non-parametric local linear estimators. The package can be used to provide forecasts and survival indicators such as the median time spent in hospital and the probability that a patient who has been in hospital for a number of days can leave it alive. Methods are described in Gámiz, Mammen, Martínez-Miranda, and Nielsen (2024) doi:10.48550/arXiv.2308.09918 and doi:10.48550/arXiv.2308.09919.

Last 30 days

This package has been downloaded 209 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 7 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 1,572 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Sep 11, 2024 with 35 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.