Forecasting Aggregate Period Specific Birth Rates: The Time Series Properties of a Microdynamic Neoclassical Model of Fertility

This article demonstrates the value of microdata for understanding the effect of wages on life cycle fertility dynamics. Conventional estimates of neoclassical economic fertility models obtained from linear aggregate time series regressions are widely criticized for being nonrobust when adjusted for serial correlation. Moreover, the forecasting power of these aggregative neoclassical models has been shown to be inferior when compared with conventional time series models that assign no role to wages. This article demonstrates, that when neoclassical models of fertility are estimated on microdata using methods that incorporate key demographic restrictions and when they are properly aggregated, they have considerable forecasting power.

Inference of epidemiological parameters from household stratified data

We consider a continuous-time Markov chain model of SIR disease dynamics with two levels of mixing. For this so-called stochastic households model, we provide two methods for inferring the model parameters---governing within-household transmission, recovery, and between-household transmission---from data of the day upon which each individual became infectious and the household in which each infection occurred, as would be available from first few hundred studies. Each method is a form of Bayesian Markov Chain Monte Carlo that allows us to calculate a joint posterior distribution for all parameters and hence the household reproduction number and the early growth rate of the epidemic. The first method performs exact Bayesian inference using a standard data-augmentation approach; the second performs approximate Bayesian inference based on a likelihood approximation derived from branching processes. These methods are compared for computational efficiency and posteriors from each are compared. The branching process is shown to be an excellent approximation and remains computationally efficient as the amount of data is increased

A systematic review of health economic models of opioid agonist therapies in maintenance treatment of non-prescription opioid dependence

Background: Opioid dependence is a chronic condition with substantial health, economic and social costs. The study objective was to conduct a systematic review of published health-economic models of opioid agonist therapy for non-prescription opioid dependence, to review the different modelling approaches identified, and to inform future modelling studies. Methods: Literature searches were conducted in March 2015 in eight electronic databases, supplemented by hand-searching reference lists and searches on six National Health Technology Assessment Agency websites. Studies were included if they: investigated populations that were dependent on non-prescription opioids and were receiving opioid agonist or maintenance therapy; compared any pharmacological maintenance intervention with any other maintenance regimen (including placebo or no treatment); and were health-economic models of any type. Results: A total of 18 unique models were included. These used a range of modelling approaches, including Markov models (n = 4), decision tree with Monte Carlo simulations (n = 3), decision analysis (n = 3), dynamic transmission models (n = 3), decision tree (n = 1), cohort simulation (n = 1), Bayesian (n = 1), and Monte Carlo simulations (n = 2). Time horizons ranged from 6 months to lifetime. The most common evaluation was cost-utility analysis reporting cost per quality-adjusted life-year (n = 11), followed by cost-effectiveness analysis (n = 4), budget-impact analysis/cost comparison (n = 2) and cost-benefit analysis (n = 1). Most studies took the healthcare provider’s perspective. Only a few models included some wider societal costs, such as productivity loss or costs of drug-related crime, disorder and antisocial behaviour. Costs to individuals and impacts on family and social networks were not included in any model. Conclusion: A relatively small number of studies of varying quality were found. Strengths and weaknesses relating to model structure, inputs and approach were identified across all the studies. There was no indication of a single standard emerging as a preferred approach. Most studies omitted societal costs, an important issue since the implications of drug abuse extend widely beyond healthcare services. Nevertheless, elements from previous models could together form a framework for future economic evaluations in opioid agonist therapy including all relevant costs and outcomes. This could more adequately support decision-making and policy development for treatment of non-prescription opioid dependence

Matching Contributions and the Voluntary Provision of a Pure Public Good: Experimental Evidence

Laboratory experiments are used to study the voluntary provision of a pure public good in the presence of an anonymous external donor. The external funds are used in two different settings, lump-sum matching and one-to-one matching, to examine how allocations to the public good are affected. The experimental results reveal that allocations to the public good under lumpsum matching are significantly higher, and have significantly lower within-group dispersion, relative to one-to-one matching and a baseline setting without external matching funds.public goods, free riding, laboratory experiments

Emerging viral diseases of fish and shrimp

The rise of aquaculture has been one of the most profound changes in global food production of the past 100 years. Driven by population growth, rising demand for seafood and a levelling of production from capture fisheries, the practice of farming aquatic animals has expanded rapidly to become a major global industry. Aquaculture is now integral to the economies of many countries. It has provided employment and been a major driver of socio-economic development in poor rural and coastal communities, particularly in Asia, and has relieved pressure on the sustainability of the natural harvest from our rivers, lakes and oceans. However, the rapid growth of aquaculture has also been the source of anthropogenic change on a massive scale. Aquatic animals have been displaced from their natural environment, cultured in high density, exposed to environmental stress, provided artificial or unnatural feeds, and a prolific global trade has developed in both live aquatic animals and their products. At the same time, over-exploitation of fisheries and anthropogenic stress on aquatic ecosystems has placed pressure on wild fish populations. Not surprisingly, the consequence has been the emergence and spread of an increasing array of new diseases. This review examines the rise and characteristics of aquaculture, the major viral pathogens of fish and shrimp and their impacts, and the particular characteristics of disease emergence in an aquatic, rather than terrestrial, context. It also considers the potential for future disease emergence in aquatic animals as aquaculture continues to expand and faces the challenges presented by climate change

Hierarchical strategies for efficient fault recovery on the reconfigurable PAnDA device

A novel hierarchical fault-tolerance methodology for reconfigurable devices is presented. A bespoke multi-reconfigurable FPGA architecture, the programmable analogue and digital array (PAnDA), is introduced allowing fine-grained reconfiguration beyond any other FPGA architecture currently in existence. Fault blind circuit repair strategies, which require no specific information of the nature or location of faults, are developed, exploiting architectural features of PAnDA. Two fault recovery techniques, stochastic and deterministic strategies, are proposed and results of each, as well as a comparison of the two, are presented. Both approaches are based on creating algorithms performing fine-grained hierarchical partial reconfiguration on faulty circuits in order to repair them. While the stochastic approach provides insights into feasibility of the method, the deterministic approach aims to generate optimal repair strategies for generic faults induced into a specific circuit. It is shown that both techniques successfully repair the benchmark circuits used after random faults are induced in random circuit locations, and the deterministic strategies are shown to operate efficiently and effectively after optimisation for a specific use case. The methods are shown to be generally applicable to any circuit on PAnDA, and to be straightforwardly customisable for any FPGA fabric providing some regularity and symmetry in its structure

Evaluation of a Brazilian fuel alcohol yeast strain for Scotch whisky fermentations

Traditionally, distilling companies in Scotland have employed a very limited number of yeast strains in the production of alcohol for Scotch whiskies. Recent changes such as the decline in availability of brewers’ yeast as a secondary yeast strain and the availability of yeast in different formats (e.g., dried and cream yeast as alternatives to compressed yeast) have promoted interest in alternative Scotch whisky distilling yeasts. In previous work, we investigated different strains of yeasts, specifically Brazilian yeasts which had been isolated from and used in fuel alcohol distilleries. One of the Brazilian yeasts (CAT 1) showed a comparable fermentation performance and superior stress tolerance compared with a standard commercial Scotch whisky distilling yeast (M Type). The Brazilian CAT 1 yeast isolate was further assessed in laboratory scale fermentations and subsequent new make spirit was subjected to sensory analyses. The spirits produced using the Brazilian strain had acceptable flavour profiles and exhibited no sensory characteristics that were atypical of Scotch whisky new make spirit. This study highlights the potential of exploiting yeast biodiversity in traditional Scotch whisky distillery fermentation processes