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

arXiv: https://arxiv.org/abs/1411.0416 · HTML: https://mlsanity.com/paper/1411.0416/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2014.

## Built on by (most cited first, 2 of 2)

- [Planetary Prediction Engine: Autonomous Geospatial Prediction via Intelligent Data Selection and Foundation Model Embeddings](https://mlsanity.com/paper/2608.26088/) (2026)
- [When Context Compensates for Sparse Event History: AlphaEarth for Spatio-Temporal Point-Process Forecasting](https://mlsanity.com/paper/2607.01082/) (2026)

## Abstract (arXiv)

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.