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Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions

Author

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  • Logan C Brooks
  • David C Farrow
  • Sangwon Hyun
  • Ryan J Tibshirani
  • Roni Rosenfeld

Abstract

Accurate and reliable forecasts of seasonal epidemics of infectious disease can assist in the design of countermeasures and increase public awareness and preparedness. This article describes two main contributions we made recently toward this goal: a novel approach to probabilistic modeling of surveillance time series based on “delta densities”, and an optimization scheme for combining output from multiple forecasting methods into an adaptively weighted ensemble. Delta densities describe the probability distribution of the change between one observation and the next, conditioned on available data; chaining together nonparametric estimates of these distributions yields a model for an entire trajectory. Corresponding distributional forecasts cover more observed events than alternatives that treat the whole season as a unit, and improve upon multiple evaluation metrics when extracting key targets of interest to public health officials. Adaptively weighted ensembles integrate the results of multiple forecasting methods, such as delta density, using weights that can change from situation to situation. We treat selection of optimal weightings across forecasting methods as a separate estimation task, and describe an estimation procedure based on optimizing cross-validation performance. We consider some details of the data generation process, including data revisions and holiday effects, both in the construction of these forecasting methods and when performing retrospective evaluation. The delta density method and an adaptively weighted ensemble of other forecasting methods each improve significantly on the next best ensemble component when applied separately, and achieve even better cross-validated performance when used in conjunction. We submitted real-time forecasts based on these contributions as part of CDC’s 2015/2016 FluSight Collaborative Comparison. Among the fourteen submissions that season, this system was ranked by CDC as the most accurate.Author summary: Seasonal influenza is associated with 250 000 to 500 000 deaths worldwide each year (WHO estimates). In the United States and other temperate regions, seasonal influenza epidemics occur annually, but their timing and intensity varies significantly; accurate and reliable forecasts that quantify their uncertainty can assist policymakers when planning countermeasures such as vaccination campaigns, and increase awareness and preparedness of hospitals and the general public. Starting with the 2013/2014 flu season, CDC has solicited, collected, evaluated, and compared weekly forecasts from external research groups. We developed a new method for forecasting flu surveillance data, which stitches together models of changes that happen each week, and a way of combining its output with other forecasts. The resulting forecasting system produced the most accurate forecasts in CDC’s 2015/2016 FluSight comparison of fourteen forecasting systems. We describe our new forecasting methods, analyze their performance in the 2015/2016 comparison and on data from previous seasons, and describe idiosyncrasies of epidemiological data that should be considered when constructing and evaluating forecasting systems.

Suggested Citation

  • Logan C Brooks & David C Farrow & Sangwon Hyun & Ryan J Tibshirani & Roni Rosenfeld, 2018. "Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions," PLOS Computational Biology, Public Library of Science, vol. 14(6), pages 1-29, June.
  • Handle: RePEc:plo:pcbi00:1006134
    DOI: 10.1371/journal.pcbi.1006134
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    References listed on IDEAS

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    1. Evan L Ray & Nicholas G Reich, 2018. "Prediction of infectious disease epidemics via weighted density ensembles," PLOS Computational Biology, Public Library of Science, vol. 14(2), pages 1-23, February.
    2. Andrea Freyer Dugas & Mehdi Jalalpour & Yulia Gel & Scott Levin & Fred Torcaso & Takeru Igusa & Richard E Rothman, 2013. "Influenza Forecasting with Google Flu Trends," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-7, February.
    3. Logan C Brooks & David C Farrow & Sangwon Hyun & Ryan J Tibshirani & Roni Rosenfeld, 2015. "Flexible Modeling of Epidemics with an Empirical Bayes Framework," PLOS Computational Biology, Public Library of Science, vol. 11(8), pages 1-18, August.
    4. David C Farrow & Logan C Brooks & Sangwon Hyun & Ryan J Tibshirani & Donald S Burke & Roni Rosenfeld, 2017. "A human judgment approach to epidemiological forecasting," PLOS Computational Biology, Public Library of Science, vol. 13(3), pages 1-19, March.
    5. Nicholas Generous & Geoffrey Fairchild & Alina Deshpande & Sara Y Del Valle & Reid Priedhorsky, 2014. "Global Disease Monitoring and Forecasting with Wikipedia," PLOS Computational Biology, Public Library of Science, vol. 10(11), pages 1-16, November.
    6. Radina P Soebiyanto & Farida Adimi & Richard K Kiang, 2010. "Modeling and Predicting Seasonal Influenza Transmission in Warm Regions Using Climatological Parameters," PLOS ONE, Public Library of Science, vol. 5(3), pages 1-10, March.
    7. Steffen Unkel & C. Paddy Farrington & Paul H. Garthwaite & Chris Robertson & Nick Andrews, 2012. "Statistical methods for the prospective detection of infectious disease outbreaks: a review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(1), pages 49-82, January.
    8. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    9. Jean-Paul Chretien & Dylan George & Jeffrey Shaman & Rohit A Chitale & F Ellis McKenzie, 2014. "Influenza Forecasting in Human Populations: A Scoping Review," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-8, April.
    10. David A Broniatowski & Michael J Paul & Mark Dredze, 2013. "National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-1, December.
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    Cited by:

    1. John M Drake & Tobias S Brett & Shiyang Chen & Bogdan I Epureanu & Matthew J Ferrari & Éric Marty & Paige B Miller & Eamon B O’Dea & Suzanne M O’Regan & Andrew W Park & Pejman Rohani, 2019. "The statistics of epidemic transitions," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-14, May.
    2. Sasikiran Kandula & Jeffrey Shaman, 2019. "Reappraising the utility of Google Flu Trends," PLOS Computational Biology, Public Library of Science, vol. 15(8), pages 1-16, August.
    3. Reid Priedhorsky & Ashlynn R Daughton & Martha Barnard & Fiona O’Connell & Dave Osthus, 2019. "Estimating influenza incidence using search query deceptiveness and generalized ridge regression," PLOS Computational Biology, Public Library of Science, vol. 15(10), pages 1-23, October.
    4. Graham Casey Gibson & Kelly R Moran & Nicholas G Reich & Dave Osthus, 2021. "Improving probabilistic infectious disease forecasting through coherence," PLOS Computational Biology, Public Library of Science, vol. 17(1), pages 1-20, January.
    5. Victor Olsavszky & Mihnea Dosius & Cristian Vladescu & Johannes Benecke, 2020. "Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database," IJERPH, MDPI, vol. 17(14), pages 1-17, July.
    6. Prashant Rangarajan & Sandeep K Mody & Madhav Marathe, 2019. "Forecasting dengue and influenza incidences using a sparse representation of Google trends, electronic health records, and time series data," PLOS Computational Biology, Public Library of Science, vol. 15(11), pages 1-24, November.
    7. Ray, Evan L. & Brooks, Logan C. & Bien, Jacob & Biggerstaff, Matthew & Bosse, Nikos I. & Bracher, Johannes & Cramer, Estee Y. & Funk, Sebastian & Gerding, Aaron & Johansson, Michael A. & Rumack, Aaron, 2023. "Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1366-1383.
    8. Junyi Lu & Sebastian Meyer, 2020. "Forecasting Flu Activity in the United States: Benchmarking an Endemic-Epidemic Beta Model," IJERPH, MDPI, vol. 17(4), pages 1-13, February.
    9. Michal Ben-Nun & Pete Riley & James Turtle & David P Bacon & Steven Riley, 2019. "Forecasting national and regional influenza-like illness for the USA," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-20, May.
    10. Dave Osthus & Ashlynn R Daughton & Reid Priedhorsky, 2019. "Even a good influenza forecasting model can benefit from internet-based nowcasts, but those benefits are limited," PLOS Computational Biology, Public Library of Science, vol. 15(2), pages 1-19, February.
    11. Nicholas G Reich & Craig J McGowan & Teresa K Yamana & Abhinav Tushar & Evan L Ray & Dave Osthus & Sasikiran Kandula & Logan C Brooks & Willow Crawford-Crudell & Graham Casey Gibson & Evan Moore & Reb, 2019. "Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S," PLOS Computational Biology, Public Library of Science, vol. 15(11), pages 1-19, November.
    12. Kookjin Lee & Jaideep Ray & Cosmin Safta, 2021. "The predictive skill of convolutional neural networks models for disease forecasting," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-26, July.

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