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Mathis SM, Webber AE, León TM, Murray EL, Sun M, White LA, Brooks LC, Green A, Hu AJ, McDonald DJ, Rosenfeld R, Shemetov D, Tibshirani RJ, Kandula S, Pei S, Shaman J, Yaari R, Yamana TK, Agarwal P, Balusu S, Gururajan G, Kamarthi H, Prakash BA, Raman R, Rodríguez A, Zhao Z, Meiyappan A, Omar S, Baccam P, Gurung HL, Stage SA, Suchoski BT, Ajelli M, Kummer AG, Litvinova M, Ventura PC, Wadsworth S, Niemi J, Carcelen E, Hill AL, Jung SM, Lemaitre JC, Lessler J, Loo SL, McKee CD, Sato K, Smith C, Truelove S, McAndrew T, Ye W, Bosse N, Hlavacek WS, Lin YT, Mallela A, Chen Y, Lamm SM, Lee J, Posner RG, Perofsky AC, Viboud C, Clemente L, Lu F, Meyer AG, Santillana M, Chinazzi M, Davis JT, Mu K, Piontti APY, Vespignani A, Xiong X, Ben-Nun M, Riley P, Turtle J, Hulme-Lowe C, Jessa S, Nagraj VP, Turner SD, Williams D, Basu A, Drake JM, Fox SJ, Gibson GC, Suez E, Thommes EW, Cojocaru MG, Cramer EY, Gerding A, Stark A, Ray EL, Reich NG, Shandross L, Wattanachit N, Wang Y, Zorn MW, Al Aawar M, Srivastava A, Meyers LA, Adiga A, Hurt B, Kaur G, Lewis BL, Marathe M, Venkatramanan S, Butler P, Farabow A, Muralidhar N, Ramakrishnan N, Reed C, Biggerstaff M, Borchering RK. Evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations. medRxiv 2023:2023.12.08.23299726. [PMID: 38168429 PMCID: PMC10760285 DOI: 10.1101/2023.12.08.23299726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. Forecasting teams were asked to provide national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one through four weeks ahead for the 2021-22 and 2022-23 influenza seasons. Across both seasons, 26 teams submitted forecasts, with the submitting teams varying between seasons. Forecast skill was evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperformed the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble was the 2nd most accurate model measured by WIS in 2021-22 and the 5th most accurate in the 2022-23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degraded over longer forecast horizons and during periods of rapid change. Current influenza forecasting efforts help inform situational awareness, but research is needed to address limitations, including decreased performance during periods of changing epidemic dynamics.
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Affiliation(s)
- Sarabeth M Mathis
- Centers for Disease Control and Prevention, Atlanta, Georgia, 30329, USA
| | - Alexander E Webber
- Centers for Disease Control and Prevention, Atlanta, Georgia, 30329, USA
| | - Tomás M León
- California Department of Public Health, Richmond, CA, 95899
| | - Erin L Murray
- California Department of Public Health, Richmond, CA, 95899
| | - Monica Sun
- California Department of Public Health, Richmond, CA, 95899
| | - Lauren A White
- California Department of Public Health, Richmond, CA, 95899
| | - Logan C Brooks
- Carnegie Mellon University, Pittsburgh, PA, 15213
- University of California, Berkeley, Berkeley, CA 94720
| | - Alden Green
- Carnegie Mellon University, Pittsburgh, PA, 15213
| | - Addison J Hu
- Carnegie Mellon University, Pittsburgh, PA, 15213
| | | | | | | | - Ryan J Tibshirani
- Carnegie Mellon University, Pittsburgh, PA, 15213
- University of California, Berkeley, Berkeley, CA 94720
| | | | - Sen Pei
- Columbia University, New York, NY, 10032
| | - Jeffrey Shaman
- Columbia University, New York, NY, 10032
- Columbia University School of Climate, New York, NY 10025
| | - Rami Yaari
- Columbia University, New York, NY, 10032
| | | | | | | | | | | | | | - Rishi Raman
- Georgia Institute of Technology, Atlanta, GA, 30318
| | | | - Zhiyuan Zhao
- Georgia Institute of Technology, Atlanta, GA, 30318
| | | | - Shalina Omar
- Guidehouse Advisory and Consulting Services, McClean VA, 22102
| | | | | | | | | | - Marco Ajelli
- Indiana University School of Public Health, Bloomington, IN, 47405
| | | | - Maria Litvinova
- Indiana University School of Public Health, Bloomington, IN, 47405
| | - Paulo C Ventura
- Indiana University School of Public Health, Bloomington, IN, 47405
| | | | | | | | | | - Sung-Mok Jung
- University of North Carolina at Chapel Hill, Chapel Hill, NC
| | | | - Justin Lessler
- University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Sara L Loo
- Johns Hopkins University, Baltimore, MD, 21205
| | | | - Koji Sato
- Johns Hopkins University, Baltimore, MD, 21205
| | | | | | | | | | - Nikos Bosse
- London School of Health and Tropical Medicine, London, UK, WC1E 7HT
| | | | - Yen Ting Lin
- Los Alamos National Laboratory, Los Alamos, NM, 87545
| | | | - Ye Chen
- Northern Arizona University, Flagstaff, AZ, 86011
| | | | - Jaechoul Lee
- Northern Arizona University, Flagstaff, AZ, 86011
| | | | - Amanda C Perofsky
- Fogarty International Center, National Institutes of Health, Bethesda, MD, 20892
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MD, 20892
| | | | - Fred Lu
- Northeastern University, Boston, MA, 02115
| | | | | | | | | | - Kunpeng Mu
- Northeastern University, Boston, MA, 02115
| | | | | | | | | | - Pete Riley
- Predictive Science Inc, San Diego, CA 92121
| | | | | | | | - V P Nagraj
- Signature Science, LLC, Charlottesville, VA, 22911
| | | | | | | | | | | | | | - Ehsan Suez
- University of Georgia, Athens, GA, 30609
| | - Edward W Thommes
- University of Guelph, Guelph, ON N1G 2W1, Canada
- Sanofi, Toronto, ON, M2R 3T4
| | | | | | - Aaron Gerding
- University of Massachusetts Amherst, Amherst, MA, 01003
| | - Ariane Stark
- University of Massachusetts Amherst, Amherst, MA, 01003
| | - Evan L Ray
- University of Massachusetts Amherst, Amherst, MA, 01003
| | | | - Li Shandross
- University of Massachusetts Amherst, Amherst, MA, 01003
| | | | - Yijin Wang
- University of Massachusetts Amherst, Amherst, MA, 01003
| | - Martha W Zorn
- University of Massachusetts Amherst, Amherst, MA, 01003
| | - Majd Al Aawar
- University of Southern California, Los Angeles, CA, 90089
| | | | | | | | | | | | | | | | | | | | | | | | | | - Carrie Reed
- Centers for Disease Control and Prevention, Atlanta, Georgia, 30329, USA
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Fox SJ, Javan E, Pasco R, Gibson GC, Betke B, Herrera-Diestra JL, Woody S, Pierce K, Johnson KE, Johnson-León M, Lachmann M, Meyers LA. Disproportionate impacts of COVID-19 in a large US city. PLoS Comput Biol 2023; 19:e1011149. [PMID: 37262052 DOI: 10.1371/journal.pcbi.1011149] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 05/02/2023] [Indexed: 06/03/2023] Open
Abstract
COVID-19 has disproportionately impacted individuals depending on where they live and work, and based on their race, ethnicity, and socioeconomic status. Studies have documented catastrophic disparities at critical points throughout the pandemic, but have not yet systematically tracked their severity through time. Using anonymized hospitalization data from March 11, 2020 to June 1, 2021 and fine-grain infection hospitalization rates, we estimate the time-varying burden of COVID-19 by age group and ZIP code in Austin, Texas. During this 15-month period, we estimate an overall 23.7% (95% CrI: 22.5-24.8%) infection rate and 29.4% (95% CrI: 28.0-31.0%) case reporting rate. Individuals over 65 were less likely to be infected than younger age groups (11.2% [95% CrI: 10.3-12.0%] vs 25.1% [95% CrI: 23.7-26.4%]), but more likely to be hospitalized (1,965 per 100,000 vs 376 per 100,000) and have their infections reported (53% [95% CrI: 49-57%] vs 28% [95% CrI: 27-30%]). We used a mixed effect poisson regression model to estimate disparities in infection and reporting rates as a function of social vulnerability. We compared ZIP codes ranking in the 75th percentile of vulnerability to those in the 25th percentile, and found that the more vulnerable communities had 2.5 (95% CrI: 2.0-3.0) times the infection rate and only 70% (95% CrI: 60%-82%) the reporting rate compared to the less vulnerable communities. Inequality persisted but declined significantly over the 15-month study period. Our results suggest that further public health efforts are needed to mitigate local COVID-19 disparities and that the CDC's social vulnerability index may serve as a reliable predictor of risk on a local scale when surveillance data are limited.
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Affiliation(s)
- Spencer J Fox
- Department of Epidemiology & Biostatistics, University of Georgia, Athens, Georgia, United States of America
- Institute of Bioinformatics, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Emily Javan
- Department of Integrative Biology, The University of Texas at Austin, Austin, Texas, United States of America
| | - Remy Pasco
- Department of Industrial Engineering, The University of Texas at Austin, Austin, Texas, United States of America
| | - Graham C Gibson
- Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Briana Betke
- Department of Integrative Biology, The University of Texas at Austin, Austin, Texas, United States of America
| | - José L Herrera-Diestra
- Department of Integrative Biology, The University of Texas at Austin, Austin, Texas, United States of America
| | - Spencer Woody
- Department of Integrative Biology, The University of Texas at Austin, Austin, Texas, United States of America
| | - Kelly Pierce
- The Texas Advanced Computing Center, The University of Texas at Austin, Austin, Texas, United States of America
| | - Kaitlyn E Johnson
- The Rockefeller Foundation, New York, New York, United States of America
| | - Maureen Johnson-León
- Department of Integrative Biology, The University of Texas at Austin, Austin, Texas, United States of America
| | - Michael Lachmann
- The Santa Fe Institute, Santa Fe, New Mexico, United States of America
| | - Lauren Ancel Meyers
- Department of Integrative Biology, The University of Texas at Austin, Austin, Texas, United States of America
- The Santa Fe Institute, Santa Fe, New Mexico, United States of America
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