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Past and present Missouri River management practices have resulted in native fishes being identified as in jeopardy. In 1995, the Missouri River Benthic Fishes Study was initiated to provide improved information on Missouri River fish populations and how alterations might affect them. The study produced a baseline against which to evaluate future changes in Missouri River operating criteria. The objective was to evaluate population structure and habitat use of benthic fishes along the entire mainstem Missouri River, exclusive of reservoirs. Here we use the data from this study to provide a recent‐past baseline for on‐going Missouri River fish population monitoring programmes along with a more powerful method for analysing data containing large percentages of zero values. This is carried out by describing the distribution and habitat use of 21 species of Missouri River benthic fishes based on catch‐per‐unit area data from multiple gears. We employ a Bayesian zero‐inflated Poisson model expanded to include continuous measures of habitat quality (i.e. substrate composition, depth, velocity, temperature, turbidity and conductivity). Along with presenting the method, we provide a relatively complete picture of the Missouri River benthic fish community and the relationship between their relative population numbers and habitat conditions. We demonstrate that our single model provides all the information that is often obtained by a myriad of analytical techniques. An important advantage of the present approach is reliable inference for patterns of relative abundance using multiple gears without using gear efficiencies. Copyright © 2011 John Wiley & Sons, Ltd.

Epidemiological data often include excess zeros. This is particularly the case for data on rare conditions, diseases that are not common in specific areas or specific time periods, and conditions and diseases that are hard to detect or on the rise. In this paper, we provide a review of methods for modeling data with excess zeros with focus on count data, namely hurdle and zero-inflated models, and discuss extensions of these models to data with spatial and spatio-temporal dependence structures. We consider a Bayesian hierarchical framework to implement spatial and spatio-temporal models for data with excess zeros. We further review current implementation methods and computational tools. Finally, we provide a case study on five-year counts of confirmed cases of Lyme disease in Illinois at the county level.

The surveillance of influenza activity is critical to early detection of epidemics and pandemics and the design of disease control strategies. Case reporting through a voluntary network of sentinel physicians is a commonly used method of passive surveillance for monitoring rates of influenza-like illness (ILI) worldwide. Despite its ubiquity, little attention has been given to the processes underlying the observation, collection, and spatial aggregation of sentinel surveillance data, and its subsequent effects on epidemiological understanding. We harnessed the high specificity of diagnosis codes in medical claims from a database that represented 2.5 billion visits from upwards of 120,000 United States healthcare providers each year. Among influenza seasons from 2002-2009 and the 2009 pandemic, we simulated limitations of sentinel surveillance systems such as low coverage and coarse spatial resolution, and performed Bayesian inference to probe the robustness of ecological inference and spatial prediction of disease burden. Our models suggest that a number of socio-environmental factors, in addition to local population interactions, state-specific health policies, as well as sampling effort may be responsible for the spatial patterns in U.S. sentinel ILI surveillance. In addition, we find that biases related to spatial aggregation were accentuated among areas with more heterogeneous disease risk, and sentinel systems designed with fixed reporting locations across seasons provided robust inference and prediction. With the growing availability of health-associated big data worldwide, our results suggest mechanisms for optimizing digital data streams to complement traditional surveillance in developed settings and enhance surveillance opportunities in developing countries.