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Modeling the variations in pediatric respiratory syncytial virus seasonal epidemics
Filetype[PDF - 514.57 KB]


Details:
  • Pubmed ID:
    21510889
  • Pubmed Central ID:
    PMC3094225
  • Funding:
    1 PO1 CD000284/CD/ODCDC CDC HHS/United States
    K24- HD047249/HD/NICHD NIH HHS/United States
    U01 AI074419/AI/NIAID NIH HHS/United States
    U01-A1061611/PHS HHS/United States
    UL1-RR025764/RR/NCRR NIH HHS/United States
  • Document Type:
  • Collection(s):
  • Description:
    Background

    Seasonal respiratory syncytial virus (RSV) epidemics occur annually in temperate climates and result in significant pediatric morbidity and increased health care costs. Although RSV epidemics generally occur between October and April, the size and timing vary across epidemic seasons and are difficult to predict accurately. Prediction of epidemic characteristics would support management of resources and treatment.

    Methods

    The goals of this research were to examine the empirical relationships among early exponential growth rate, total epidemic size, and timing, and the utility of specific parameters in compartmental models of transmission in accounting for variation among seasonal RSV epidemic curves. RSV testing data from Primary Children's Medical Center were collected on children under two years of age (July 2001-June 2008). Simple linear regression was used explore the relationship between three epidemic characteristics (final epidemic size, days to peak, and epidemic length) and exponential growth calculated from four weeks of daily case data. A compartmental model of transmission was fit to the data and parameter estimated used to help describe the variation among seasonal RSV epidemic curves.

    Results

    The regression results indicated that exponential growth was correlated to epidemic characteristics. The transmission modeling results indicated that start time for the epidemic and the transmission parameter co-varied with the epidemic season.

    Conclusions

    The conclusions were that exponential growth was somewhat empirically related to seasonal epidemic characteristics and that variation in epidemic start date as well as the transmission parameter over epidemic years could explain variation in seasonal epidemic size. These relationships are useful for public health, health care providers, and infectious disease researchers.