Model-Based Evaluation of Highly and Low Pathogenic Avian Influenza Dynamics in Wild Birds

There is growing interest in avian influenza (AI) epidemiology to predict disease risk in wild and domestic birds, and prevent transmission to humans. However, understanding the epidemic dynamics of highly pathogenic (HPAI) viruses remains challenging because they have rarely been detected in wild birds. We used modeling to integrate available scientific information from laboratory and field studies, evaluate AI dynamics in individual hosts and waterfowl populations, and identify key areas for future research. We developed a Susceptible-Exposed-Infectious-Recovered (SEIR) model and used published laboratory challenge studies to estimate epidemiological parameters (rate of infection, latency period, recovery and mortality rates), considering the importance of age classes, and virus pathogenicity. Infectious contact leads to infection and virus shedding within 1–2 days, followed by relatively slower period for recovery or mortality. We found a shorter infectious period for HPAI than low pathogenic (LP) AI, which may explain that HPAI has been much harder to detect than LPAI during surveillance programs. Our model predicted a rapid LPAI epidemic curve, with a median duration of infection of 50–60 days and no fatalities. In contrast, HPAI dynamics had lower prevalence and higher mortality, especially in young birds. Based on field data from LPAI studies, our model suggests to increase surveillance for HPAI in post-breeding areas, because the presence of immunologically naïve young birds is predicted to cause higher HPAI prevalence and bird losses during this season. Our results indicate a better understanding of the transmission, infection, and immunity-related processes is required to refine predictions of AI risk and spread, improve surveillance for HPAI in wild birds, and develop disease control strategies to reduce potential transmission to domestic birds and/or humans

Tracking large carnivore dispersal using isotopic clues in claws: An application to cougars across the Great Plains

1. Cougar (Puma concolor) populations, like other large carnivores, have increased during recent decades and may be recolonizing their former ranges in Midwestern North America. The dispersal routes taken by these animals from established populations are unknown and insight into these movements would facilitate their conservation and management. 2. We inferred the origin and migration route of four dispersing cougars using stable hydrogen (δD) and carbon (δ13C) isotope values along one of their claws. We compared isotopic variations within claws to regional and large-scale isoscapes of δD and δ13C values in prey species. Using a likelihood-based assignment approach, we predicted the most likely dispersal route of each cougar (among several least-cost dispersal paths to potential source populations) in a chronological sequence dating back from its final location. 3. Our model predicted the origin of a radio-collared short-distance disperser and inferences about the most likely dispersal corridors for two long-distance dispersers matched reported information from re-sighting events and genetic investigations. 4. Insights about the most likely migration corridors may help identify critical areas and guide future conservation efforts of cougars and other large carnivores. We encourage managers to extend regional isoscapes based on sedentary prey species as they prove to be valuable tools in isotopic tracking of long-distance migration. 5. Our isotopic approach may be extended to other metabolically inert tissues that grow continuously, to investigate dispersal paths of species of interest, providing that individuals disperse across known isotopically structured landscapes

A new strategy for diagnostic model assessment in capture-recapture

Common to both diagnostic tests used in capture–recapture and score tests is the idea that starting from a simple base model it is possible to interrogate data to determine whether more complex parameter structures will be supported. Current recommendations advise that diagnostic tests are performed as a precursor to a model selection step. We show that certain well-known diagnostic tests for examining the fit of capture–recapture models to data are in fact score tests. Because of this direct relationship we investigate a new strategy for model assessment which combines the diagnosis of departure from basic model assumptions with a step-up model selection, all based on score tests. We investigate the power of such an approach to detect common reasons for lack of model fit and compare the performance of this new strategy with the existing recommendations by using simulation. We present motivating examples with real data for which the extra flexibility of score tests results in an improved performance compared with diagnostic tests

Fracture of glassy materials as detected by real-time Atomic Force Microscopy (AFM) experiments

We have studied the low speed fracture regime for different glassy materials with variable but controlled length scales of heterogeneity in a carefully mastered surrounding atmosphere. By using optical and atomic force (AFM) microscopy techniques we tracked in real-time the crack tip propagation at the nanometer scale on a wide velocity range (1 mm/s and 0.1 nm/s and below). The influence of the heterogeneities on this velocity is presented and discussed. Our experiments revealed also -for the first time- that the crack advance proceeds through nucleation, growth and coalescence of nanometric damage cavities inside the amorphous phase, which generate large velocity fluctuations. The implications of the existence of such a nano-ductile fracture mode in glass are discussed.Comment: 6 pages, 5 figures, submitted to Applied surface Scienc

Impact of Imperfect Disease Detection on the Identification of Risk Factors in Veterinary Epidemiology

Risk factors are key epidemiological concepts that are used to explain disease distributions. Identifying disease risk factors is generally done by comparing the characteristics of diseased and non-diseased populations. However, imperfect disease detectability generates disease observations that do not necessarily represent accurately the true disease situation. In this study, we conducted an extensive simulation exercise to emphasize the impact of imperfect disease detection on the outcomes of logistic models when case reports are aggregated at a larger scale (e.g., diseased animals aggregated at farm level). We used a probabilistic framework to simulate both the disease distribution in herds and imperfect detectability of the infected animals in these herds. These simulations show that, under logistic models, true herd-level risk factors are generally correctly identified but their associated odds ratio are heavily underestimated as soon as the sensitivity of the detection is less than one. If the detectability of infected animals is not only imperfect but also heterogeneous between herds, the variables associated with the detection heterogeneity are likely to be incorrectly identified as risk factors. This probability of type I error increases with increasing heterogeneity of the detectability, and with decreasing sensitivity. Finally, the simulations highlighted that, when count data is available (e.g., number of infected animals in herds), they should not be reduced to a presence/absence dataset at the herd level (e.g., presence or not of at least one infected animal) but rather modeled directly using zero-inflated count models which are shown to be much less sensitive to imperfect detectability issues. In light of these simulations, we revisited the analysis of the French bovine abortion surveillance data, which has already been shown to be characterized by imperfect and heterogeneous abortion detectability. As expected, we found substantial differences between the quantitative outputs of the logistic model and those of the zero-inflated Poisson model. We conclude by strongly recommending that efforts should be made to account for, or at the very least discuss, imperfect disease detectability when assessing associations between putative risk factors and observed disease distributions, and advocate the use of zero-inflated count models if count data is available

Presence of Avian Influenza Viruses in Waterfowl and Wetlands during Summer 2010 in California: Are Resident Birds a Potential Reservoir?

Although wild waterfowl are the main reservoir for low pathogenic avian influenza viruses (LPAIv), the environment plays a critical role for the circulation and persistence of AIv. LPAIv may persist for extended periods in cold environments, suggesting that waterfowl breeding areas in the northern hemisphere may be an important reservoir for AIv in contrast to the warmer southern wintering areas. We evaluated whether southern wetlands, with relatively small populations (thousands) of resident waterfowl, maintain AIv in the summer, prior to the arrival of millions of migratory birds. We collected water and fecal samples at ten wetlands in two regions (Yolo Bypass and Sacramento Valley) of the California Central Valley during three bi-weekly intervals beginning in late July, 2010. We detected AIv in 29/367 fecal samples (7.9%) and 12/597 water samples (2.0%) by matrix real time Reverse Transcription Polymerase Chain Reaction (rRT-PCR). We isolated two H3N8, two H2N3, and one H4N8 among rRT-PCR positive fecal samples but no live virus from water samples. Detection of AIv RNA in fecal samples was higher from wetlands in the Sacramento Valley (11.9%) than in the Yolo Bypass (0.0%), but no difference was found for water samples (2.7 vs. 1.7%, respectively). Our study showed that low densities of hosts and unfavorable environmental conditions did not prevent LPAIv circulation during summer in California wetlands. Our findings justify further investigations to understand AIv dynamics in resident waterfowl populations, compare AIv subtypes between migratory and resident waterfowl, and assess the importance of local AIv as a source of infection for migratory birds

Effects of infection-induced migration delays on the epidemiology of avian influenza in wild mallard populations

Wild waterfowl populations form a natural reservoir of Avian Influenza (AI) virus, and fears exist that these birds may contribute to an AI pandemic by spreading the virus along their migratory flyways. Observational studies suggest that individuals infected with AI virus may delay departure from migratory staging sites. Here, we explore the epidemiological dynamics of avian influenza virus in a migrating mallard (Anas platyrhynchos) population with a specific view to understanding the role of infection-induced migration delays on the spread of virus strains of differing transmissibility. We develop a host-pathogen model that combines the transmission dynamics of influenza with the migration, reproduction and mortality of the host bird species. Our modeling predicts that delayed migration of individuals influences both the timing and size of outbreaks of AI virus. We find that (1) delayed migration leads to a lower total number of cases of infection each year than in the absence of migration delay, (2) when the transmission rate of a strain is high, the outbreak starts at the staging sites at which birds arrive in the early part of the fall migration, (3) when the transmission rate is low, infection predominantly occurs later in the season, which is further delayed when there is a migration delay. As such, the rise of more virulent AI strains in waterfowl could lead to a higher prevalence of infection later in the year, which could change the exposure risk for farmed poultry. A sensitivity analysis shows the importance of generation time and loss of immunity for the effect of migration delays. Thus, we demonstrate, in contrast to many current transmission risk models solely using empirical information on bird movements to assess the potential for transmission, that a consideration of infection-induced delays is critical to understanding the dynamics of AI infection along the entire flyway.<br /