Assessing Seasonal Variation in Multisource Surveillance Data: Annual Harmonic Regression

  • Eric Lofgren
  • Nina Fefferman
  • Meena Doshi
  • Elena N. Naumova
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4506)

Abstract

A significant proportion of human diseases, spanning the gamut from viral respiratory disease to arthropod-borne macroparasitic infections of the blood, exhibit distinct and stable seasonal patterns of incidence. Traditional statistical methods for the evaluation of seasonal time-series data emphasize the removal of these seasonal variations to be able to examine non-periodic, and therefore unexpected, or ‘excess’, incidence. Here, the authors present an alternate methodology emphasizing the retention and quantification of exactly these seasonal fluctuations, explicitly examining the changes in severity and timing of the expected seasonal outbreaks over several years. Using a PCRconfirmed Influenza time series as a case study, the authors provide an example of this type of analysis and discuss the potential uses of this method, including the comparison of differing sources of surveillance data. The requirements for statistical and practical validity, and considerations of data collection, reporting and analysis involved in the appropriate applications of the methods proposed are also discussed in detail.

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References

  1. 1.
    Dowell, S.F., Ho, M.S.: Seasonality of Infectious Diseases and Severe Acute Respiratory Syndrome – What We Don’t Know Can Hurt Us. Lancet Infect Dis. 4, 704–708 (2004)CrossRefGoogle Scholar
  2. 2.
    Lofgren, E., et al.: Influenza Seasonalty: Underlying Causes and Modeling Theories. J. Virol. (in press) (2007)Google Scholar
  3. 3.
    Graham, N.M.H., Nelson, K.E., Steinhoff, M.C.: The Epidemiology of Acute Respiratory Infections. In: Nelson, K.E., Williams, C.F.M. (eds.) Infectious Disease Epidemiology: Theory and Practice, 2nd edn., pp. 699–755. Jones and Bartlett, Boston (2007)Google Scholar
  4. 4.
    Naumova, E.N., et al.: Seasonality in Six Enterically Transmitted Diseases and Ambient Temperatures. Epidemiol. Infect. (in press) (2007)Google Scholar
  5. 5.
    Wonham, M.J., de Camino-Beck, T., Lewis, M.A.: An Epidemiological Model for West Nile Virus: Invasion Analysis and Control Applications. Proc. Royal Soc. B 271, 501–507 (2004)CrossRefGoogle Scholar
  6. 6.
    Serfling, R.E.: Methods for Current Statistical Analysis of Excess Pneumonia-Influenza Deaths. Public Health Rep. 78, 494–506 (1963)Google Scholar
  7. 7.
    Thompson, W.W., Comanor, L., Shay, D.K.: Epidemiology of Seasonal Influenza: Use of Surveillance Data and Statistical Models to Estimate the Burden of Disease. J. Infec. Dis. 194, 582–591 (2006)CrossRefGoogle Scholar
  8. 8.
    Thompson, W.W., et al.: Mortality Associated with Influenza and Respiratory Syncytial Virus in the United States. JAMA 289, 179–186 (2003)CrossRefGoogle Scholar
  9. 9.
    Simonsen, L., et al.: Impact of Influenza Vaccination on Seasonal Mortality in the US Elderly Population. Arch. Intern. Med. 165, 265–272 (2005)CrossRefGoogle Scholar
  10. 10.
    Naumova, E.N., MacNeill, I.B.: Seasonality assessment for biosurveillance systems. In: Balakrishnan, N., et al. (eds.) Advances in Statistical Methods for the Health Sciences: Applications to Cancer and AIDS Studies, Genome Sequence Analysis, and Survival Analysis, pp. 437–450. Birkhäuser, Boston (2006)Google Scholar
  11. 11.
    Farrington, C.P., et al.: A Statistical Algorithm for the Early Detection of Outbreaks of Infectious Disease. J. R. Statist. Soc. A 159, 547–563 (1996)MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Eric Lofgren
    • 1
  • Nina Fefferman
    • 1
  • Meena Doshi
    • 1
  • Elena N. Naumova
    • 1
  1. 1.Department of Public Health and Family Medicine, Tufts University School of Medicine, 136 Harrison Ave., 204 Stearns 

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