Forecasting influenza outbreak dynamics in Melbourne from Internet search query surveillance data

Abstract

Background: Accurate forecasting of seasonal influenza epidemics is of great concern to healthcare providers in temperate climates, as these epidemics vary substantially in their size, timing and duration from year to year, making it a challenge to deliver timely and proportionate responses. Previous studies have shown that Bayesian estimation techniques can accurately predict when an influenza epidemic will peak many weeks in advance, using existing surveillance data, but these methods must be tailored both to the target population and to the surveillance system. Objectives: Our aim was to evaluate whether forecasts of similar accuracy could be obtained for metropolitan Melbourne (Australia..