Open AccessForecasting influenza outbreak dynamics in Melbourne from Internet search query surveillance data
Author(s) -
Moss Robert,
Zarebski Alexander,
Dawson Peter,
McCaw James M.
Publication year - 2016
Publication title -
influenza and other respiratory viruses
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.743
H-Index - 57
eISSN - 1750-2659
pISSN - 1750-2640
DOI - 10.1111/irv.12376
Subject(s) - metropolitan area , estimation , computer science , the internet , robustness (evolution) , bayesian probability , population , seasonal influenza , outbreak , covid-19 , data mining , geography , medicine , infectious disease (medical specialty) , environmental health , virology , artificial intelligence , engineering , biochemistry , chemistry , archaeology , systems engineering , disease , pathology , world wide web , gene
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). Methods We used the bootstrap particle filter and a mechanistic infection model to generate epidemic forecasts for metropolitan Melbourne (Australia) from weekly Internet search query surveillance data reported by Google Flu Trends for 2006–14. Results and Conclusions Optimal observation models were selected from hundreds of candidates using a novel approach that treats forecasts akin to receiver operating characteristic ( ROC ) curves. We show that the timing of the epidemic peak can be accurately predicted 4–6 weeks in advance, but that the magnitude of the epidemic peak and the overall burden are much harder to predict. We then discuss how the infection and observation models and the filtering process may be refined to improve forecast robustness, thereby improving the utility of these methods for healthcare decision support.