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Comparing Observed with Predicted Weekly Influenza-Like Illness Rates during the Winter Holiday Break, United States, 2004-2013

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  • Hongjiang Gao
  • Karen K Wong
  • Yenlik Zheteyeva
  • Jianrong Shi
  • Amra Uzicanin
  • Jeanette J Rainey

Abstract

In the United States, influenza season typically begins in October or November, peaks in February, and tapers off in April. During the winter holiday break, from the end of December to the beginning of January, changes in social mixing patterns, healthcare-seeking behaviors, and surveillance reporting could affect influenza-like illness (ILI) rates. We compared predicted with observed weekly ILI to examine trends around the winter break period. We examined weekly rates of ILI by region in the United States from influenza season 2003–2004 to 2012–2013. We compared observed and predicted ILI rates from week 44 to week 8 of each influenza season using the auto-regressive integrated moving average (ARIMA) method. Of 1,530 region, week, and year combinations, 64 observed ILI rates were significantly higher than predicted by the model. Of these, 21 occurred during the typical winter holiday break period (weeks 51–52); 12 occurred during influenza season 2012–2013. There were 46 observed ILI rates that were significantly lower than predicted. Of these, 16 occurred after the typical holiday break during week 1, eight of which occurred during season 2012–2013. Of 90 (10 HHS regions x 9 seasons) predictions during the peak week, 78 predicted ILI rates were lower than observed. Out of 73 predictions for the post-peak week, 62 ILI rates were higher than observed. There were 53 out of 73 models that had lower peak and higher post-peak predicted ILI rates than were actually observed. While most regions had ILI rates higher than predicted during winter holiday break and lower than predicted after the break during the 2012–2013 season, overall there was not a consistent relationship between observed and predicted ILI around the winter holiday break during the other influenza seasons.

Suggested Citation

  • Hongjiang Gao & Karen K Wong & Yenlik Zheteyeva & Jianrong Shi & Amra Uzicanin & Jeanette J Rainey, 2015. "Comparing Observed with Predicted Weekly Influenza-Like Illness Rates during the Winter Holiday Break, United States, 2004-2013," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-11, December.
    • Handle: RePEc:plo:pone00:0143791
    DOI: 10.1371/journal.pone.0143791
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    References listed on IDEAS

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    1. Radina P Soebiyanto & Farida Adimi & Richard K Kiang, 2010. "Modeling and Predicting Seasonal Influenza Transmission in Warm Regions Using Climatological Parameters," PLOS ONE, Public Library of Science, vol. 5(3), pages 1-10, March.
    2. Simon Cauchemez & Alain-Jacques Valleron & Pierre-Yves Boëlle & Antoine Flahault & Neil M. Ferguson, 2008. "Estimating the impact of school closure on influenza transmission from Sentinel data," Nature, Nature, vol. 452(7188), pages 750-754, April.
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    1. Mohammed A. A. Al-qaness & Ahmed A. Ewees & Hong Fan & Mohamed Abd Elaziz, 2020. "Optimized Forecasting Method for Weekly Influenza Confirmed Cases," IJERPH, MDPI, vol. 17(10), pages 1-12, May.
    2. Tatiana Petukhova & Davor Ojkic & Beverly McEwen & Rob Deardon & Zvonimir Poljak, 2018. "Assessment of autoregressive integrated moving average (ARIMA), generalized linear autoregressive moving average (GLARMA), and random forest (RF) time series regression models for predicting influenza," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-17, June.
    3. Carina Aguilar Martín & Mª Rosa Dalmau Llorca & Elisabet Castro Blanco & Noèlia Carrasco-Querol & Zojaina Hernández Rojas & Emma Forcadell Drago & Dolores Rodríguez Cumplido & Alessandra Queiroga Gonç, 2022. "Concordance between the Clinical Diagnosis of Influenza in Primary Care and Epidemiological Surveillance Systems (PREVIGrip Study)," IJERPH, MDPI, vol. 19(3), pages 1-12, January.

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