Fast and accurate influenza forecasting in the United States with Inferno
Abstract
Infectious disease forecasting is an emerging field and has the potential to improve public health through anticipatory resource allocation, situational awareness, and mitigation planning. By way of exploring and operationalizing disease forecasting, the U.S. Centers for Disease Control and Prevention (CDC) has hosted FluSight since the 2013/14 flu season, an annual flu forecasting challenge. Since FluSight’s onset, forecasters have developed and improved forecasting models in an effort to provide more timely, reliable, and accurate information about the likely progression of the outbreak. While improving the predictive performance of these forecasting models is often the primary objective, it is also important for a forecasting model to run quickly, facilitating further model development and improvement while providing flexibility when deployed in a real-time setting. In this vein I introduce Inferno, a fast and accurate flu forecasting model inspired by Dante, the top performing model in the 2018/19 FluSight challenge. When pseudoprospectively compared to all models that participated in FluSight 2018/19, Inferno would have placed 2nd in the national and regional challenge as well as the state challenge, behind only Dante. Inferno, however, runs in minutes and is trivially parallelizable, while Dante takes hours to run, representing a significant operational improvement with minimalmore »
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE Laboratory Directed Research and Development (LDRD) Program
- OSTI Identifier:
- 1844455
- Alternate Identifier(s):
- OSTI ID: 1842890; OSTI ID: 1868274
- Report Number(s):
- LA-UR-20-30384
Journal ID: ISSN 1553-7358; 10.1371/journal.pcbi.1008651
- Grant/Contract Number:
- 20190546ECR; 89233218CNA000001
- Resource Type:
- Published Article
- Journal Name:
- PLoS Computational Biology (Online)
- Additional Journal Information:
- Journal Name: PLoS Computational Biology (Online) Journal Volume: 18 Journal Issue: 1; Journal ID: ISSN 1553-7358
- Publisher:
- Public Library of Science (PLoS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; 29 ENERGY PLANNING, POLICY, AND ECONOMY
Citation Formats
Osthus, Dave, and Funk, ed., Sebastian. Fast and accurate influenza forecasting in the United States with Inferno. United States: N. p., 2022.
Web. doi:10.1371/journal.pcbi.1008651.
Osthus, Dave, & Funk, ed., Sebastian. Fast and accurate influenza forecasting in the United States with Inferno. United States. https://doi.org/10.1371/journal.pcbi.1008651
Osthus, Dave, and Funk, ed., Sebastian. Mon .
"Fast and accurate influenza forecasting in the United States with Inferno". United States. https://doi.org/10.1371/journal.pcbi.1008651.
@article{osti_1844455,
title = {Fast and accurate influenza forecasting in the United States with Inferno},
author = {Osthus, Dave and Funk, ed., Sebastian},
abstractNote = {Infectious disease forecasting is an emerging field and has the potential to improve public health through anticipatory resource allocation, situational awareness, and mitigation planning. By way of exploring and operationalizing disease forecasting, the U.S. Centers for Disease Control and Prevention (CDC) has hosted FluSight since the 2013/14 flu season, an annual flu forecasting challenge. Since FluSight’s onset, forecasters have developed and improved forecasting models in an effort to provide more timely, reliable, and accurate information about the likely progression of the outbreak. While improving the predictive performance of these forecasting models is often the primary objective, it is also important for a forecasting model to run quickly, facilitating further model development and improvement while providing flexibility when deployed in a real-time setting. In this vein I introduce Inferno, a fast and accurate flu forecasting model inspired by Dante, the top performing model in the 2018/19 FluSight challenge. When pseudoprospectively compared to all models that participated in FluSight 2018/19, Inferno would have placed 2nd in the national and regional challenge as well as the state challenge, behind only Dante. Inferno, however, runs in minutes and is trivially parallelizable, while Dante takes hours to run, representing a significant operational improvement with minimal impact to performance. Forecasting challenges like FluSight should continue to monitor and evaluate how they can be modified and expanded to incentivize the development of forecasting models that benefit public health.},
doi = {10.1371/journal.pcbi.1008651},
journal = {PLoS Computational Biology (Online)},
number = 1,
volume = 18,
place = {United States},
year = {Mon Jan 31 00:00:00 EST 2022},
month = {Mon Jan 31 00:00:00 EST 2022}
}
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