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Seasonal Synchronization of Influenza in the United States Older Adult Population

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  • Julia B Wenger
  • Elena N Naumova

Abstract

Background: In temperate regions, influenza epidemics occur annually with the highest activity occurring during the winter months. While seasonal dynamics of the influenza virus, such as time of onset and circulating strains, are well documented by the Centers for Disease Control and Prevention Influenza Surveillance System, an accurate prediction of timing, magnitude, and composition of circulating strains of seasonal influenza remains elusive. To facilitate public health preparedness for seasonal influenza and to obtain better insights into the spatiotemporal behavior of emerging strains, it is important to develop measurable characteristics of seasonal oscillation and to quantify the relationships between those parameters on a spatial scale. The objectives of our research were to examine the seasonality of influenza on a national and state level as well as the relationship between peak timing and intensity of influenza in the United States older adult population. Methodology/Principal Findings: A total of 248,889 hospitalization records were extracted from the Centers for Medicare and Medicaid Services for the influenza seasons 1991–2004. Harmonic regression models were used to quantify the peak timing and absolute intensity for each of the 48 contiguous states and Washington, DC. We found that individual influenza seasons showed spatial synchrony with consistent late or early timing occurring across all 48 states during each influenza season in comparison to the overall average. On a national level, seasons that had an earlier peak also had higher rates of influenza (rs = −0.5). We demonstrated a spatial trend in peak timing of influenza; western states such as Nevada, Utah, and California peaked earlier and New England States such as Rhode Island, Maine, and New Hampshire peaked later. Conclusions/Significance: Our findings suggest that a systematic description of influenza seasonal patterns is a valuable tool for disease surveillance and can facilitate strategies for prevention of severe disease in the vulnerable, older adult population.

Suggested Citation

  • Julia B Wenger & Elena N Naumova, 2010. "Seasonal Synchronization of Influenza in the United States Older Adult Population," PLOS ONE, Public Library of Science, vol. 5(4), pages 1-11, April.
  • Handle: RePEc:plo:pone00:0010187
    DOI: 10.1371/journal.pone.0010187
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    References listed on IDEAS

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    1. Lewi Stone & Ronen Olinky & Amit Huppert, 2007. "Seasonal dynamics of recurrent epidemics," Nature, Nature, vol. 446(7135), pages 533-536, March.
    2. Simonsen, L. & Clarke, M.J. & Williamson, G.D. & Stroup, D.F. & Arden, N.H. & Schonberger, L.B., 1997. "The impact of influenza epidemics on mortality: Introducing a severity index," American Journal of Public Health, American Public Health Association, vol. 87(12), pages 1944-1950.
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    Cited by:

    1. Xiaoli Wang & Shuangsheng Wu & C Raina MacIntyre & Hongbin Zhang & Weixian Shi & Xiaomin Peng & Wei Duan & Peng Yang & Yi Zhang & Quanyi Wang, 2015. "Using an Adjusted Serfling Regression Model to Improve the Early Warning at the Arrival of Peak Timing of Influenza in Beijing," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-14, March.
    2. Supachai Nakapan & Nitin Kumar Tripathi & Taravudh Tipdecho & Marc Souris, 2012. "Spatial Diffusion of Influenza Outbreak-Related Climate Factors in Chiang Mai Province, Thailand," IJERPH, MDPI, vol. 9(11), pages 1-19, October.
    3. Jonathon D. Gass & Nichola J. Hill & Lambodhar Damodaran & Elena N. Naumova & Felicia B. Nutter & Jonathan A. Runstadler, 2023. "Ecogeographic Drivers of the Spatial Spread of Highly Pathogenic Avian Influenza Outbreaks in Europe and the United States, 2016–Early 2022," IJERPH, MDPI, vol. 20(11), pages 1-17, June.
    4. Kavitha Ramanathan & Mani Thenmozhi & Sebastian George & Shalini Anandan & Balaji Veeraraghavan & Elena N. Naumova & Lakshmanan Jeyaseelan, 2020. "Assessing Seasonality Variation with Harmonic Regression: Accommodations for Sharp Peaks," IJERPH, MDPI, vol. 17(4), pages 1-14, February.
    5. Coleman, Stephen, 2018. "Geographical Distributions and Equilibrium in Social Norm-Related Behavior in the United States," MPRA Paper 96207, University Library of Munich, Germany.
    6. Yutong Zhang & Ryan B. Simpson & Lauren E. Sallade & Emily Sanchez & Kyle M. Monahan & Elena N. Naumova, 2022. "Evaluating Completeness of Foodborne Outbreak Reporting in the United States, 1998–2019," IJERPH, MDPI, vol. 19(5), pages 1-19, March.
    7. Olga K. Alsova & Valery B. Loktev & Elena N. Naumova, 2019. "Rotavirus Seasonality: An Application of Singular Spectrum Analysis and Polyharmonic Modeling," IJERPH, MDPI, vol. 16(22), pages 1-20, November.
    8. Ryan B. Simpson & Sofia Babool & Maia C. Tarnas & Paulina M. Kaminski & Meghan A. Hartwick & Elena N. Naumova, 2021. "Signatures of Cholera Outbreak during the Yemeni Civil War, 2016–2019," IJERPH, MDPI, vol. 19(1), pages 1-29, December.
    9. Ayaz Hyder & David L Buckeridge & Brian Leung, 2013. "Predictive Validation of an Influenza Spread Model," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-20, June.
    10. Elena N. Naumova & Ryan B. Simpson & Bingjie Zhou & Meghan A. Hartwick, 2022. "Global seasonal and pandemic patterns in influenza: An application of longitudinal study designs," International Statistical Review, International Statistical Institute, vol. 90(S1), pages 82-95, December.

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