Seasonal influenza is a serious public health and societal problem due to its consequences resulting from absenteeism, hospitalizations, and deaths. The overall burden of influenza is captured by the Centers for Disease Control and Prevention’s influenza-like illness network, which provides invaluable information about the current incidence. This information is used to provide decision support regarding prevention and response efforts. Despite the relatively rich surveillance data and the recurrent nature of seasonal influenza, forecasting the timing and intensity of seasonal influenza in the U.S. remains challenging because the form of the disease transmission process is uncertain, the disease dynamics are only partially observed, and the public health observations are noisy. Fitting a probabilistic state-space model motivated by a deterministic mathematical model [a susceptible-infectious-recovered (SIR) model] is a promising approach for forecasting seasonal influenza while simultaneously accounting for multiple sources of uncertainty. A significant finding of this work is the importance of thoughtfully specifying the prior, as results critically depend on its specification. Our conditionally specified prior allows us to exploit known relationships between latent SIR initial conditions and parameters and functions of surveillance data. We demonstrate advantages of our approach relative to alternatives via a forecasting comparison using several forecast accuracy metrics.

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The autologistic model is a Markov random field model for spatial binary data. Because it can account for both statistical dependence among the data and for the effects of potential covariates, the autologistic model is particularly suitable for problems in many fields, including ecology, where binary responses, indicating the presence or absence of a certain plant or animal species, are observed over a two-dimensional lattice. We consider inference and computation for two models: the original autologistic model due to Besag, and the centered autologistic model proposed recently by Caragea and Kaiser. Parameter estimation and inference for these models is a notoriously difficult problem due to the complex form of the likelihood function. We study pseudolikelihood (PL), maximum likelihood (ML), and Bayesian approaches to inference and describe ways to optimize the efficiency of these algorithms and the perfect sampling algorithms upon which they depend, taking advantage of parallel computing when possible. We conduct a simulation study to investigate the effects of spatial dependence and lattice size on parameter inference, and find that inference for regression parameters in the centered model is reliable only for reasonably large lattices (n > 900) and no more than moderate spatial dependence. When the lattice is large enough, and the dependence small enough, to permit reliable inference, the three approaches perform comparably, and so we recommend the PL approach for its easier implementation and much faster execution.

Abstract. Climate change can have a broad range of effects on ecosystems and organisms, and early responses may include shifts in vegetation phenology and productivity that may not coincide with the energetics and forage timing of higher trophic levels. We evaluated phenology, annual height growth, and foliar frost responses of forbs to a factorial experiment of snow removal (SR) and warming in a high-elevation meadow over two years in the Rocky Mountains, United States. Species included arrowleaf balsamroot (Balsamorhiza sagittata, early-season emergence and flowering) and buckwheat (Eriogonum umbellatum, semiwoody and late-season flowering), key forbs for pollinator and nectar-using animal communities that are widely distributed and locally abundant in western North America. Snow removal exerted stronger effects than did warming, and advanced phenology differently for each species. Specifically, SR advanced greenup by a few days for B. sagittata to >2 wk in E. umbellatum, and led to 5-to 11-d advances in flowering of B. sagittata in one year and advances in bud break in 3 of 4 species/yr combinations. Snow removal increased height of E. umbellatum appreciably (~5 cm added to~22.8 cm in control), but led to substantial increases in frost damage to flowers of B. sagittata. Whereas warming had no effects on E. umbellatum, it increased heights of B. sagittata by >6 cm (compared to 30.7 cm in control plots) and moreover led to appreciable reductions in frost damage to flowers. These data suggest that timing of snowmelt, which is highly variable from year to year but is advancing in recent decades, has a greater impact on these critical phenological, growth, and floral survival traits and floral/nectar resources than warming per se, although warming mitigated early effects of SR on frost kill of flowers. Given the short growing season of these species, the shifts could cause uncoupling in nectar availability and timing of foraging.

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