BackgroundCollege and university students experience substantial morbidity from influenza and influenza-like illness, and they can benefit substantially from vaccination. Public health authorities encourage vaccination not only before the influenza season but also into and even throughout the influenza season. We conducted the present study to assess the impact of various vaccination strategies including delayed (i.e., in-season) vaccination on influenza outbreaks on a college campus.Methods/FindingsWe used a Susceptible → Infected → Recovered (SIR) framework for our mathematical models to simulate influenza epidemics in a closed, college campus. We included both students and faculty/staff in the model and derived values for the model parameters from the published literature. The values for key model parameters were varied to assess the impact on the outbreak of various pre-season and delayed vaccination rates; one-way sensitivity analyses were conducted to test the sensitivity of the model outputs to changes in selected parameter values. In the base case, with a pre-season vaccination rate of 20%, no delayed vaccination, and 1 student index case, the total attack rate (total percent infected, TAR) was 45%. With higher pre-season vaccination rates TARs were lower. Even if vaccinations were given 30 days after outbreak onset, TARs were still lower than the TAR of 69% in the absence of vaccination. Varying the proportions of vaccinations given pre-season versus delayed until after the onset of the outbreak gave intermediate TAR values. Base case outputs were sensitive to changes in infectious contact rates and infectious periods and a holiday/break schedule.ConclusionDelayed vaccination and holidays/breaks can be important adjunctive measures to complement traditional pre-season influenza vaccination for controlling and preventing influenza in a closed college campus.

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A two‐phase Bayesian model is presented for updating risk assessments for locations susceptible to infection by exotic pathogens. Human transportation from previously infected regions to uninfected regions is the main dispersal mechanism. Information embedded in patterns within the transportation flow are exploited in the update process. We explore the sensitivity of the model's outputs to changes in inputs. A sample application of the model to sudden oak death, using fictitious infection data, is performed.

Populations may suffer unexpected loss or distortion of biodiversity as a consequence of strategies employed in artificial propagation programs. The Trinity River Fish Hatchery may have inadvertently experienced this while attempting to preserve diversity in a return time within a Chinook salmon population. We develop a model for this system and prove that the long-term distribution of return types converges and that it is strongly tied to the management strategy. Given estimates of heritabilities for return type and differential survival rates, an estimate of this long-term distribution can be computed easily.

The Resource Modeling Association (RMA) is an international, interdisciplinary scholarly organization dedicated to the design, promulgation, and use of mathematical models for the study and management of natural resources. Founded in the early 1980s, the association's aims are supported by the quarterly publication of a peer‐reviewed academic journal, Natural Resources Modeling, and the sponsorship of an annual scholarly meeting, the World Conference on Natural Resource Modeling, held at locations throughout the world.