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

TL;DR

The paper introduces the R package EpiILMCT for modeling infectious disease spread using continuous time individual-level models, supporting simulation, visualization, and Bayesian inference with data uncertainty handling.

Contribution

It provides a comprehensive tool for simulating and analyzing continuous time ILMs with Bayesian fitting and data augmentation, applicable to various disease frameworks.

Findings

Successful simulation of disease spread using the package

Application to tomato spotted wilt virus data demonstrates utility

Supports partial observation and missing data handling

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 (MCMC) framework. The continuous time ILMs can be implemented within either susceptible-infected-removed (SIR) or susceptible-infected-notified-removed (SINR) 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 (TSWV) disease.