Spatio-Temporal Analysis of Epidemic Phenomena Using the R Package surveillance

TL;DR

This paper introduces the R package surveillance, which provides tools for analyzing and modeling spatio-temporal epidemic data at various aggregation levels, aiding public health surveillance and research.

Contribution

It offers a comprehensive guide to using the surveillance package for spatio-temporal epidemic modeling with practical examples on measles and meningococcal disease.

Findings

Effective visualization and inference tools for epidemic data

Simulation capabilities for epidemic modeling

Application to real-world disease surveillance data

Abstract

The availability of geocoded health data and the inherent temporal structure of communicable diseases have led to an increased interest in statistical models and software for spatio-temporal data with epidemic features. The open source R package surveillance can handle various levels of aggregation at which infective events have been recorded: individual-level time-stamped geo-referenced data (case reports) in either continuous space or discrete space, as well as counts aggregated by period and region. For each of these data types, the surveillance package implements tools for visualization, likelihoood inference and simulation from recently developed statistical regression frameworks capturing endemic and epidemic dynamics. Altogether, this paper is a guide to the spatio-temporal modeling of epidemic phenomena, exemplified by analyses of public health surveillance data on measles and invasive meningococcal disease.