Real-time influenza forecasts during the 2012–2013 season

Shaman, Jeffrey; Karspeck, Alicia; Yang, Wan; Tamerius, James; Lipsitch, Marc

doi:10.1038/ncomms3837

Cited by 273 publications

(348 citation statements)

References 20 publications

Supporting

Mentioning

331

Contrasting

Unclassified

Order By: Relevance

“…Shaman et al 4, 5. used an ensemble adjustment Kalman filter (EAKF) and an SIRS infection model to predict seasonal influenza outbreaks in 108 cities across the USA and showed that outbreak peak timing could be predicted 4–6 weeks in advance.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

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

Moss

Zarebski

Dawson

et al. 2016

Influenza Resp Viruses

View full text Add to dashboard Cite

BackgroundAccurate 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.ObjectivesOur aim was to evaluate whether forecasts of similar accuracy could be obtained for metropolitan Melbourne (Australia).MethodsWe 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 ConclusionsOptimal 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.

show abstract

Section: Discussionmentioning

confidence: 99%

“…A variety of recursive Bayesian estimation methods (‘filters’) have been used for such forecasting purposes,1, 2 often in combination with Internet search query surveillance data and mechanistic models of infection 3, 4, 5, 6, 7, 8…”

Section: Introductionmentioning

confidence: 99%

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

Moss

Zarebski

Dawson

et al. 2016

Influenza Resp Viruses

View full text Add to dashboard Cite

show abstract

“…Other studies have used a single data source [1,19,20] or a primary data source that was modulated by a secondary data source ('ILI+') [21,22]; in both cases single datasets were used to generate and evaluate the forecasts.…”

Section: Comparison With Other Studiesmentioning

confidence: 99%

Retrospective forecasting of the 2010–2014 Melbourne influenza seasons using multiple surveillance systems

et al. 2016

View full text Add to dashboard Cite

SUMMARYAccurate forecasting of seasonal influenza epidemics is of great concern to healthcare providers in temperate climates, since 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, and we have previously tailored these methods for metropolitan Melbourne (Australia) and Google Flu Trends data. Here we extend these methods to clinical observation and laboratory-confirmation data for Melbourne, on the grounds that these data sources provide more accurate characterizations of influenza activity. We show that from each of these data sources we can accurately predict the timing of the epidemic peak 4-6 weeks in advance. We also show that making simultaneous use of multiple surveillance systems to improve forecast skill remains a fundamental challenge. Disparate systems provide complementary characterizations of disease activity, which may or may not be comparable, and it is unclear how a 'ground truth' for evaluating forecasts against these multiple characterizations might be defined. These findings are a significant step towards making optimal use of routine surveillance data for outbreak forecasting.

show abstract

“…This time lag is far from optimal for decision-making purposes. To alleviate this information gap, multiple methods combining climate, demographic, and epidemiological data with mathematical models have been proposed for real-time estimation of flu activity (18,(21)(22)(23)(24)(25). In recent years, methods that harness Internet-based information have also been proposed, such as Google (1), Yahoo (2), and Baidu (3) Internet searches, Twitter posts (4), Wikipedia article views (5), clinicians' queries (6), and crowdsourced selfreporting mobile apps such as Influenzanet (Europe) (26), Flutracking (Australia) (27), and Flu Near You (United States) (28).…”

mentioning

confidence: 99%