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Influenza poses a significant health threat to children, and schools may play a critical role in community outbreaks. Mathematical outbreak models require assumptions about contact rates and patterns among students, but the level of temporal granularity required to produce reliable results is unclear. We collected objective contact data from students aged 5-14 at an elementary school and middle school in the state of Utah, USA, and paired those data with a novel, data-based model of influenza transmission in schools. Our simulations produced within-school transmission averages consistent with published estimates. We compared simulated outbreaks over the full resolution dynamic network with simulations on networks with averaged representations of contact timing and duration. For both schools, averaging the timing of contacts over one or two school days caused average outbreak sizes to increase by 1-8%. Averaging both contact timing and pairwise contact durations caused average outbreak sizes to increase by 10% at the middle school and 72% at the elementary school. Averaging contact durations separately across within-class and between-class contacts reduced the increase for the elementary school to 5%. Thus, the effect of ignoring details about contact timing and duration in school contact networks on outbreak size modelling can vary across different schools.

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Two regional studies conducted during dry weather demonstrated that the Southern California Bight (SCB) shoreline has good water quality, except near areas that drain land-based runoff. Here, we repeat those regional studies 36 h after a rainstorm to assess the influence of runoff under high flow conditions. Two hundred and fifty-four shoreline sites between Santa Barbara, California and Ensenada, Mexico were sampled using a stratified-random sampling design with four strata: sandy beaches, rocky shoreline, shoreline adjacent to urban runoff outlets that flow intermittently, and shoreline adjacent to outlets that flow year-round. Each site was sampled for total coliforms, fecal coliforms (or E. coli), and enterococci. Sixty percent of the shoreline failed water quality standards after the storm compared to only 6% during dry weather. Failure of water quality standards increased to more than 90% for shoreline areas adjacent to urban runoff outlets. During dry weather, most water quality failures occurred for only one of the three bacterial indicators and concentrations were barely above State of California standards; following the storm, most failures were for multiple indicators and exceeded State of California standards by a large margin. The condition of the shoreline in Mexico and the United States was similar following rainfall, which was not the case during dry weather.

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BackgroundSeasonal 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.MethodsThe 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.ResultsThe 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.ConclusionsThe 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.