Accommodating Complex Substitution Patterns in a Random Utility Model of Recreational Fishing

We employed a cross-nested logit (CNL) model that permits a rich pattern of substitution among alternatives within a closed form choice model. The specification we employed is ideal for applications with many choice alternatives, such as the 431 fishing sites in this application. The CNL model provided a significant improvement over multinomial and nested logit model specifications at explaining observed recreational fishing site choices by residents of northern Ontario, Canada. Results from two scenarios illustrated the implications of using the CNL model on spatial substitution patterns and welfare measures associated with attribute change scenarios. The CNL model forecasts demonstrated that the relative change in fishing site use was lower at the most affected sites and higher at sites near the affected sites than was predicted by the multinomial logit model. No consistent pattern was found in mean or variance of welfare estimates associated with hypothetical attribute changes.Compensating variation, cross-nested logit, fishing site choice, random utility model, spatial substitution, Demand and Price Analysis, Institutional and Behavioral Economics, Q26,

IMPACT: The Journal of the Center for Interdisciplinary Teaching and Learning. Volume 7, Issue 2, Summer 2018

IMPACT: The Journal of the Center for Interdisciplinary Teaching & Learning is a peer-reviewed, biannual online journal that publishes scholarly and creative non-fiction essays about the theory, practice and assessment of interdisciplinary education. Impact is produced by the Center for Interdisciplinary Teaching & Learning at the College of General Studies, Boston University (www.bu.edu/cgs/citl).In the weeks and months following August 12, 2017, members of the Boston University community struggled — like Americans everywhere — to comprehend the series of troubling, and tragic, events which would come, almost immediately, to be denoted in the national imagination by the metonym “Charlottesville.” This special issue of Impact: The Journal of the Center for Interdisciplinary Teaching & Learning comprises a series of responses to these events and their aftermath, as well as the conditions which enabled them, by faculty members from across the BU campus

Orthogonally Decoupled Variational Gaussian Processes

Gaussian processes (GPs) provide a powerful non-parametric framework for reasoning over functions. Despite appealing theory, its superlinear computational and memory complexities have presented a long-standing challenge. State-of-the-art sparse variational inference methods trade modeling accuracy against complexity. However, the complexities of these methods still scale superlinearly in the number of basis functions, implying that that sparse GP methods are able to learn from large datasets only when a small model is used. Recently, a decoupled approach was proposed that removes the unnecessary coupling between the complexities of modeling the mean and the covariance functions of a GP. It achieves a linear complexity in the number of mean parameters, so an expressive posterior mean function can be modeled. While promising, this approach suffers from optimization difficulties due to ill-conditioning and non-convexity. In this work, we propose an alternative decoupled parametrization. It adopts an orthogonal basis in the mean function to model the residues that cannot be learned by the standard coupled approach. Therefore, our method extends, rather than replaces, the coupled approach to achieve strictly better performance. This construction admits a straightforward natural gradient update rule, so the structure of the information manifold that is lost during decoupling can be leveraged to speed up learning. Empirically, our algorithm demonstrates significantly faster convergence in multiple experiments

Viral antibody dynamics in a chiropteran host

1. Bats host many viruses that are significant for human and domestic animal health, but the dynamics of these infections in their natural reservoir hosts remain poorly elucidated.<p></p> 2. In these, and other, systems, there is evidence that seasonal life-cycle events drive infection dynamics, directly impacting the risk of exposure to spillover hosts. Understanding these dynamics improves our ability to predict zoonotic spillover from the reservoir hosts.<p></p> 3. To this end, we followed henipavirus antibody levels of >100 individual E. helvum in a closed, captive, breeding population over a 30-month period, using a powerful novel antibody quantitation method.<p></p> 4. We demonstrate the presence of maternal antibodies in this system and accurately determine their longevity. We also present evidence of population-level persistence of viral infection and demonstrate periods of increased horizontal virus transmission associated with the pregnancy/lactation period.<p></p> 5.The novel findings of infection persistence and the effect of pregnancy on viral transmission, as well as an accurate quantitation of chiropteran maternal antiviral antibody half-life, provide fundamental baseline data for the continued study of viral infections in these important reservoir hosts

Integrating social behaviour, demography and disease dynamics in network models: applications to disease management in declining wildlife populations

This is the final version. Available on open access from the Royal Society via the DOI in this record.The emergence and spread of infections can contribute to the decline and extinction of populations, particularly in conjunction with anthropogenic environmental change. The importance of heterogeneity in processes of transmission, resistance and tolerance is increasingly well understood in theory, but empirical studies that consider both the demographic and behavioural implications of infection are scarce. Non-random mixing of host individuals can impact the demographic thresholds that determine the amplification or attenuation of disease prevalence. Risk assessment and management of disease in threatened wildlife populations must therefore consider not just host density, but also the social structure of host populations. Here we integrate the most recent developments in epidemiological research from a demographic and social network perspective and synthesise the latest developments in social network modelling for wildlife disease, to explore their applications to disease management in populations in decline and at risk of extinction. We use simulated examples to support our key points and reveal how disease-management strategies can and should exploit both behavioural and demographic information to prevent or control the spread of disease. Our synthesis highlights the importance of considering the combined impacts of demographic and behavioural processes in epidemics to successful disease management in a conservation context.Natural Environment Research Council (NERC)Animal and Plant Health AgencyUniversity of Exete

Social structure contains epidemics and regulates individual roles in disease transmission in a group-living mammal

This is the final version. Available from Wiley via the DOI in this record. Data accessibility: The original weighted adjacency matrix for the high‐density population of European badgers, as well as code used for simulating networks and disease simulations can be found online https://doi.org/10.5061/dryad.49n3878.Population structure is critical to infectious disease transmission. As a result, theoretical and empirical contact network models of infectious disease spread are increasingly providing valuable insights into wildlife epidemiology. Analyzing an exceptionally detailed dataset on contact structure within a high-density population of European badgers Meles meles, we show that a modular contact network produced by spatially structured stable social groups, lead to smaller epidemics, particularly for infections with intermediate transmissibility. The key advance is that we identify considerable variation among individuals in their role in disease spread, with these new insights made possible by the detail in the badger dataset. Furthermore, the important impacts on epidemiology are found even though the modularity of the Badger network is much lower than the threshold that previous work suggested was necessary. These findings reveal the importance of stable social group structure for disease dynamics with important management implications for socially structured populations.Natural Environment Research Council (NERC