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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
Lofgren, E., et al.: Influenza Seasonalty: Underlying Causes and Modeling Theories. J. Virol. (in press) (2007)
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)
Naumova, E.N., et al.: Seasonality in Six Enterically Transmitted Diseases and Ambient Temperatures. Epidemiol. Infect. (in press) (2007)
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)
Serfling, R.E.: Methods for Current Statistical Analysis of Excess Pneumonia-Influenza Deaths. Public Health Rep. 78, 494–506 (1963)
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)
Thompson, W.W., et al.: Mortality Associated with Influenza and Respiratory Syncytial Virus in the United States. JAMA 289, 179–186 (2003)
Simonsen, L., et al.: Impact of Influenza Vaccination on Seasonal Mortality in the US Elderly Population. Arch. Intern. Med. 165, 265–272 (2005)
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)
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)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Lofgren, E., Fefferman, N., Doshi, M., Naumova, E.N. (2007). Assessing Seasonal Variation in Multisource Surveillance Data: Annual Harmonic Regression. In: Zeng, D., et al. Intelligence and Security Informatics: Biosurveillance. BioSurveillance 2007. Lecture Notes in Computer Science, vol 4506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72608-1_11
Download citation
DOI: https://doi.org/10.1007/978-3-540-72608-1_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72607-4
Online ISBN: 978-3-540-72608-1
eBook Packages: Computer ScienceComputer Science (R0)