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Modeling the variations in pediatric respiratory syncytial virus seasonal epidemics.

Leecaster M, Gesteland P, Greene T, Walton N, Gundlapalli A, Rolfs R, Byington C, Samore M - BMC Infect. Dis. (2011)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, Utah, USA. molly.leecaster@hsc.utah.edu

ABSTRACT

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.

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Related in: MedlinePlus

Observed RSV cases and model predicted epidemic curves. Observed RSV cases (grey dots) collected by Primary Children's Medical Center in Salt Lake City from July 2001 through June 2008, plotted for each season along with fitted SEIDR models.
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Figure 4: Observed RSV cases and model predicted epidemic curves. Observed RSV cases (grey dots) collected by Primary Children's Medical Center in Salt Lake City from July 2001 through June 2008, plotted for each season along with fitted SEIDR models.

Mentions: The second main conclusion of this work was that variation of the transmission parameter and the start of the epidemic (offset) over epidemic years could explain the variation in seasonal epidemic size. The three model parameters allowed to vary by epidemic year (detection fraction, transmission parameter, and offset) provided possible rationale for the variation in seasonal epidemic size. The model with detection fraction held constant across epidemic year fits the observed data well with the fewest parameters. The parameter estimates from this model also match the expected biennial pattern of the epidemic years. From the models considered in this study, this one performs best overall (Figure 4).


Modeling the variations in pediatric respiratory syncytial virus seasonal epidemics.

Leecaster M, Gesteland P, Greene T, Walton N, Gundlapalli A, Rolfs R, Byington C, Samore M - BMC Infect. Dis. (2011)

Observed RSV cases and model predicted epidemic curves. Observed RSV cases (grey dots) collected by Primary Children's Medical Center in Salt Lake City from July 2001 through June 2008, plotted for each season along with fitted SEIDR models.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3094225&req=5

Figure 4: Observed RSV cases and model predicted epidemic curves. Observed RSV cases (grey dots) collected by Primary Children's Medical Center in Salt Lake City from July 2001 through June 2008, plotted for each season along with fitted SEIDR models.
Mentions: The second main conclusion of this work was that variation of the transmission parameter and the start of the epidemic (offset) over epidemic years could explain the variation in seasonal epidemic size. The three model parameters allowed to vary by epidemic year (detection fraction, transmission parameter, and offset) provided possible rationale for the variation in seasonal epidemic size. The model with detection fraction held constant across epidemic year fits the observed data well with the fewest parameters. The parameter estimates from this model also match the expected biennial pattern of the epidemic years. From the models considered in this study, this one performs best overall (Figure 4).

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, Utah, USA. molly.leecaster@hsc.utah.edu

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus