GDILM.SEIRS

Geographically Dependent Individual Level Models (GDILMs) within the Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) framework are applied to model infectious disease transmission, incorporating reinfection dynamics. This package employs a likelihood based Monte Carlo Expectation Conditional Maximization (MCECM) algorithm for estimating model parameters. It also provides tools for GDILM fitting, parameter estimation, AIC calculation on real pandemic data, and simulation studies customized to user-defined model settings.

Last 30 days

This package has been downloaded 161 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 3 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 908 times in the last 365 days. This could be a paper that people cite without reading. Reaching the medium popularity echelon is no small feat! The day with the most downloads was Dec 08, 2024 with 63 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.