Continuous Time Individual-Level Models of Infectious Disease: Package EpiILMCT

Abstract

This paper describes the R package EpiILMCT, which allows users to study the spread of infectious disease using continuous time individual level models (ILMs). The package provides tools for simulation from continuous time ILMs that are based on either spatial demographic, contact network, or a combination of both of them, and for the graphical summarization of epidemics. Model fitting is carried out within a Bayesian Markov Chain Monte Carlo framework. The continuous time ILMs can be implemented within either susceptible-infected-removed (SIR) or susceptible-infected-notified-removed (SIN R) compartmental frameworks. As infectious disease data is often partially observed, data uncertainties in the form of missing infection times -and in some situations missing removal times -are accounted for using data augmentation techniques. The package is illustrated using both simulated and an experimental data set on the spread of the tomato spotted wilt virus disease.

SARS-CoV-2 Transmission in University Classes

“…While this assumption is clearly not true for real-world disease recovery, the computational savings provided by the memoryless property are seen to outweigh this drawback. In fact, the geometric distribution's continuous memoryless analogue, the exponential distribution, is popularly used to model waiting times in continuous problems because of how memorylessness eases computation (see, e.g., Almutiry et al, 2020;Kiss et al, 2017). Next, we observe that the geometric distribution models the number of independent and identically distributed Bernoulli trials before the first success.…”