Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions
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DOI: 10.1371/journal.pcbi.1006134
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- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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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