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The COVID-19 pandemic has brought forth the importance of epidemic forecasting for decision makers in multiple domains, ranging from public health to the economy as a whole.
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Planning for the control of pandemic influenza A (H1N1) in Los Angeles County and the United States
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Separating fact from fear: Tracking flu infections on twitter. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . 789–795
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Real-time disease surveillance using twitter data: demonstration on flu and cancer. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining . 1474–1477
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A simulation optimization approach to epidemic forecasting
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Reassessing Google Flu Trends data for detection of seasonal and pandemic influenza: a comparative epidemiological study at three geographic scales
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The impact of biases in mobile phone ownership on estimates of human mobility
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Environmental surveillance for polioviruses in the Global Polio Eradication Initiative
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Estimates of the reproduction number for seasonal, pandemic, and zoonotic influenza: a systematic review of the literature
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Satellite imagery analysis: What can hospital parking lots tell us about a disease outbreak?
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Influenza forecasting in human populations: a scoping review
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Epidemiologic data and pathogen genome sequences: a powerful synergy for public health
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Wikipedia usage estimates prevalence of influenza-like illness in the United States in near real-time
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Influenzanet: citizens among 10 countries collaborating to monitor influenza in Europe
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Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems . 6000–6010
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When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes
Zhaozhi Qian, Ahmed M Alaa, and Mihaela van der Schaar. 2020 · 2020
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Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study
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Deepcovidnet: An interpretable deep learning model for predictive surveillance of covid-19 using heterogeneous features and their interactions
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reichlab/covid19-Forecast-Hub: Pre-Publication Snapshot
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Forecasting influenza-like illness dynamics for military populations using neural networks and social media
Svitlana Volkova, Ellyn Ayton, Katherine Porterfield, and Courtney D Corley. 2017 · 2017
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Individual versus superensemble forecasts of seasonal influenza outbreaks in the United States
Teresa K Yamana, Sasikiran Kandula, and Jeffrey Shaman. 2017 · 2017
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Advances in using Internet searches to track dengue
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Muhammad Aurangzeb Ahmad, Carly Eckert, and Ankur Teredesai. 2018 · 2018
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Comparison of crowd-sourced, electronic health records based, and traditional health-care based influenza-tracking systems at multiple spatial resolutions in the United States of America
Kristin Baltrusaitis, John S Brownstein, Samuel V Scarpino, Eric Bakota, Adam W Crawley, Giuseppe Conidi, Julia Gunn, Josh Gray, Anna Zink, and Mauricio Santillana. 2018 · 2018
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Results from the second year of a collaborative effort to forecast influenza seasons in the United States
Matthew Biggerstaff, Michael Johansson, David Alper, Logan C Brooks, Prithwish Chakraborty, David C Farrow, Sangwon Hyun, Sasikiran Kandula, Craig McGowan, Naren Ramakrishnan, et al · 2018
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Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions
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Incorporating Expert Guidance in Epidemic Forecasting. In ACM SIGKDD 2020 Epidemiology Meets Data Mining and Knowledge Discovery (epiDAMIK)
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The Twitter pandemic: The critical role of Twitter in the dissemination of medical information and misinformation during the COVID-19 pandemic
Hans Rosenberg, Shahbaz Syed, and Salim Rezaie. 2020 · 2020
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A Syndromic COVID-19 Indicator Based on Insurance Claims of Outpatient Visits
Aaron Rumack and Roni Rosenfeld. 2021 · 2020
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Harnessing multiple models for outbreak management
Katriona Shea, Michael C. Runge, David Pannell, William J. M. Probert, Shou-Li Li, Michael Tildesley, and Matthew Ferrari. 2020 · 2020
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