# Forecasting Based on Surveillance Data

arXiv: https://arxiv.org/abs/1809.03735 · HTML: https://mlsanity.com/paper/1809.03735/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2018.

## Built on by (most cited first, 1 of 1)

- [How (Not) to Hybridize Neural and Mechanistic Models for Epidemiological Forecasting](https://mlsanity.com/paper/2602.06323/) (2026)

## Abstract (arXiv)

Forecasting the future course of epidemics has always been one of the main goals of epidemic modelling. This chapter reviews statistical methods to quantify the accuracy of epidemic forecasts. We distinguish point and probabilistic forecasts and describe different methods to evaluate and compare the predictive performance across models. Two case studies demonstrate how to apply the different techniques to uni- and multivariate forecasts. We focus on forecasting count time series from routine public health surveillance: weekly counts of influenza-like illness in Switzerland, and age-stratified counts of norovirus gastroenteritis in Berlin, Germany. Data and code for all analyses are available in a supplementary R package.