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Forecasting a Moving Target: Ensemble Models for ILI Case Count Predictions
[chapter]
2014
Proceedings of the 2014 SIAM International Conference on Data Mining
Preserved Fulltext
Finally, we present a novel matrix factorization approach using neighborhood embedding to predict flu case counts. ...
For a realtime prediction system, we posit that one of the key challenges is to effectively handle the uncertainty associated with reports of flu activity. ...
Advanced Research Projects Activity (IARPA) via Department of Interior National Business Center (DoI/NBC) contract number D12PC000337, the US Government is authorized to reproduce and distribute reprints for ...
doi:10.1137/1.9781611973440.30
dblp:conf/sdm/ChakrabortyKLMCBNMBMR14
fatcat:lun3c6gsyzao3fpoorbxygh2ea
Moving beyond the cost-loss ratio: Economic assessment of streamflow forecasts for a risk-averse decision maker
2016
Hydrology and Earth System Sciences Discussions
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It is found that the economic value of a forecast for a risk-averse decision maker is closely linked to the forecast reliability in predicting the upper tail of the streamflow distribution. ...
Another approach is the use of ensemble meteorological forecasts for precipitation and temperature, which are then provided as inputs to one or many hydrological model(s). ...
The authors also thank the ECMWF for the development and maintenance of the TIGGE data portal allowing free access to meteorological ensemble forecasts for research purposes. ...
doi:10.5194/hess-2016-495
fatcat:ismtral55rbnzijaornautyqti
Moving beyond the cost–loss ratio: economic assessment of streamflow forecasts for a risk-averse decision maker
2017
Hydrology and Earth System Sciences
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It is found that the economic value of a forecast for a risk-averse decision maker is closely linked to the forecast reliability in predicting the upper tail of the streamflow distribution. ...
Another approach is the use of ensemble meteorological forecasts for precipitation and temperature, which are then provided as inputs to one or many hydrological model(s). ...
The authors also thank the ECMWF for the development and maintenance of the TIGGE data portal allowing free access to meteorological ensemble forecasts for research purposes. ...
doi:10.5194/hess-21-2967-2017
fatcat:lijv3bwxqnganf42nety6ihxsq
A stacked ensemble method for forecasting influenza-like illness visit volumes at emergency departments
2021
PLoS ONE
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In this paper, we developed a stacked ensemble model that averages the predictions from various competing methodologies in the current frontier for ILI-related forecasts. ...
We also constructed a back-of-the-envelope prediction interval for the stacked ensemble, which provides a conservative characterization of the uncertainty in the stacked ensemble predictions. ...
Hussain Usman, Adrienne Macdonald and Jason Cabaj provided helpful input and feedback during the model development stage. ...
doi:10.1371/journal.pone.0241725
pmid:33750974
fatcat:jx2pidi37nd5lpamjxq6igbcoq
A Stacked ensemble method for forecasting influenza-like illness visit volumes at emergency departments
[article]
2020
bioRxiv
pre-print
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In this paper, we developed a stacked ensemble model that averages the predictions from various competing methodologies in the current frontier for ILI-related forecasts. ...
We also constructed a back-of-the-envelope prediction interval for the stacked ensemble, which provides a conservative characterization of the uncertainty in the stacked ensemble predictions. ...
Hussain Usman and Adrienne Macdonald provided helpful input and feedback 277 during the model development stage. We also thank Sherry Trithart and Shaun Malo 278 for data provision from ARTSSN. ...
doi:10.1101/2020.10.21.348417
fatcat:vpfqt2outvevrnidfnt54gqrq4
Forecasting Influenza Outbreaks in Boroughs and Neighborhoods of New York City
2016
PLoS Computational Biology
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These forecasts are compared to an alternate system that predicts influenza for each borough or neighborhood in isolation. ...
By identifying the optimal granularity for a given disease and host population, and matching surveillance and prediction efforts to this scale, response to emergent and recurrent outbreaks can be improved ...
Acknowledgments We thank Robert Mathes and Ramona Lall of the Syndromic Surveillance Unit of the Bureau of Communicable Disease, New York City Department of Health and Mental Hygiene for preparing the ...
doi:10.1371/journal.pcbi.1005201
pmid:27855155
pmcid:PMC5113861
fatcat:u5okuoaspbhefea4pz6mn6yfhi
Using electronic health records and Internet search information for accurate influenza forecasting
2017
BMC Infectious Diseases
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Our regularized multivariate regression model dynamically selects the most appropriate variables for flu prediction every week. ...
We present a method that combines the information from these two data sources with historical flu activity to produce national flu forecasts for the United States up to 4 weeks ahead of the publication ...
Acknowledgements The authors would like to thank David Harrington and Anna Zink for their helpful comments. ...
doi:10.1186/s12879-017-2424-7
pmid:28482810
pmcid:PMC5423019
fatcat:7yzhdmfgyfg7dp2brdfgyycs4y
Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting
[article]
2020
arXiv
pre-print
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In order to incorporate the effects of multiple factors in COVID-19 spread, we consider multiple sources such as COVID-19 confirmed and death case count data and testing data for better predictions. ...
Deep learning-based time series models for forecasting have recently gained popularity and have been successfully used for epidemic forecasting. ...
suggestions related to epidemic modeling and response support. ...
arXiv:2010.14491v2
fatcat:axkpnt2yq5grbppj7cojk4fbqe
Forecasting national and regional influenza-like illness for the USA
2019
PLoS Computational Biology
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Also, we tested a simple ensemble model for the 2016-17 season and found that it underperformed our subjective choice for all forecast targets. ...
Here, we describe our participation in a weekly prospective ILI forecasting challenge for the United States for the 2016-17 season and subsequent evaluation of our performance. ...
Acknowledgments We thank the organizers and participants of the CDC influenza challenge for helpful discussions.
Author Contributions ...
doi:10.1371/journal.pcbi.1007013
pmid:31120881
pmcid:PMC6557527
fatcat:m36kbloav5eo5ddkyr5bywid5a
A systematic review of studies on forecasting the dynamics of influenza outbreaks
2013
Influenza and Other Respiratory Viruses
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"A systematic review of studies on forecasting the dynamics of influenza outbreaks." Influenza and Other Respiratory Viruses 8 (3): 309-316. ...
Research Projects Activity (IARPA) via Department of Interior National Business Center (DoI/NBC) contract number D12PC000337, and the US Government is authorized to reproduce and distribute reprints for ...
Ong
et al. 4
2010
ILI
Weekly
2009
Singapore
SEIR model with
particle filtering
Weekly case counts,
peak timing, and
duration
Error
Chao
et al. 2
2010
CDC influenza case
estimates ...
doi:10.1111/irv.12226
pmid:24373466
pmcid:PMC4181479
fatcat:dmqavzp6kbcd7b6pewoxcl6vmq
Chimeric forecasting: combining probabilistic predictions from computational models and human judgment
[article]
2022
arXiv
pre-print
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We find a chimeric ensemble compared to an ensemble including only computational models improves predictions of incident cases and shows similar performance for predictions of incident deaths. ...
A traditional approach to forecasting fits a computational model to structured data and generates a predictive distribution. ...
If a model missed a forecast in the past they are still included. If a model missed a forecast for the present survey for either cases or deaths than they are removed from the ensemble. ...
arXiv:2202.09820v1
fatcat:ohprj4cgtjfyzoo2l7ydfdlxse
Addressing delayed case reporting in infectious disease forecast modeling
[article]
2021
arXiv
pre-print
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Due to delays in case reporting, however, forecasting models may often underestimate the current and future disease burden. ...
This work provides intuition and guidance for handling delay in disease case reporting and may serve as a useful resource to inform practical infectious disease forecasting efforts. ...
This work is approved for distribution under LA-UR-21-30640. ...
arXiv:2110.14533v1
fatcat:y6byurt53zey7oynswqguvikxa
COVID-19 and Influenza Joint Forecasts Using Internet Search Information in the United States
[article]
2022
arXiv
pre-print
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Inspired by the inner-connection between influenza and COVID-19 activities, we propose ARGOX-Joint-Ensemble which allows us to combine historical influenza and COVID-19 disease forecasting models to a ...
Moreover, our experiments demonstrate that our approach is successful in adapting past influenza forecasting models to the current pandemic, while improving upon previous COVID-19 forecasting models, by ...
Ref [35] uses incidence patterns from past influenza seasons, COVID-19 time series information, and demographic covariates in a Generalized Linear Model to forecast next week's country-level case counts ...
arXiv:2202.02621v1
fatcat:ywxd3mebpzbvdk6n6qmeekkjdy
Forecasting influenza activity using self-adaptive AI model and multi-source data in Chongqing, China
2019
EBioMedicine
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an innovative Self-adaptive AI Model (SAAIM), which was constructed by integrating Seasonal Autoregressive Integrated Moving Average model and XGBoost model using a self-adaptive weight adjustment mechanism ...
SAAIM was applied to ILI% forecast in Chongqing from 2017 to 2018, of which the performance was compared with three previously available models on forecasting. ...
allow for the recent historical performance of the ensemble model. ...
doi:10.1016/j.ebiom.2019.08.024
pmid:31477561
pmcid:PMC6796527
fatcat:mnmeo6biqvbm3k62lt7uu7qsqy
Improved forecasts of influenza-associated hospitalization rates with Google Search Trends
2019
Journal of the Royal Society Interface
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In this paper, we describe a method to forecast hospitalization rates using a population level transmission model in combination with a data assimilation technique. ...
These results suggest that the model-inference framework can provide reasonably accurate real-time forecasts of influenza hospitalizations; backcasts and nowcasts offer a way to improve system tolerance ...
In effect, the lowest possible score for a forecast is 26.9.
Endnote ...
doi:10.1098/rsif.2019.0080
pmid:31185818
pmcid:PMC6597779
fatcat:rlytrvtmtzgtpiq2fk43dwffiq
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