ABSTRACT
Forecasting the future course of epidemics has always been one of the main goals of epidemic modeling. This chapter reviews statistical methods to quantify the accuracy of epidemic forecasts. Point and probabilistic forecasts are distinguished and different methods are described to evaluate and compare the predictive performance across models. Two case studies demonstrate how to apply the different techniques to uni- and multivariate forecasts. The focus is 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.
