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Abstract: Deterministic dynamic compartmental transmission models (DDCTMs) of human papillomavirus (HPV) transmission have been used in a number of studies to estimate the potential impact of HPV vaccination programs. In most cases, the models were built under the assumption that an individual who cleared HPV infection develops (life-long) natural immunity against re-infection with the same HPV type (this is known as SIR scenario). This assumption was also made by two Australian modelling studies evaluating the impact of the National HPV Vaccination Program to assist in the health-economic assessment of male vaccination. An alternative view denying natural immunity after clearance (SIS scenario) was only presented in one study, although neither scenario has been supported by strong evidence. Some recent findings, however, provide arguments in favour of SIS. We developed HPV transmission models implementing life-time (SIR), limited, and non-existent (SIS) natural immunity. For each model we estimated the herd immunity effect of the ongoing Australian HPV vaccination program and its extension to cover males. Given the Australian setting, we aimed to clarify the extent to which the choice of model structure would influence estimation of this effect. A statistically robust and efficient calibration methodology was applied to ensure credibility of our results. We observed that for non-SIR models the herd immunity effect measured in relative reductions in HPV prevalence in the unvaccinated population was much more pronounced than for the SIR model. For example, with vaccine efficacy of 95% for females and 90% for males, the reductions for HPV-16 were 3% in females and 28% in males for the SIR model, and at least 30% (females) and 60% (males) for non-SIR models. The magnitude of these differences implies that evaluations of the impact of vaccination programs using DDCTMs should incorporate several model structures until our understanding of natural immunity is improved. [Copyright &y& Elsevier]

Abstract: Many ecological indices convert raw estimates of a variable into a score, and combine scores for multiple variables into a total score. Biodiversity valuation indices, for example, convert raw estimates of variables thought to be surrogates for habitat attributes into a quantitative expression of biodiversity value for use in market-based conservation policies. This study evaluated two potential sources of inaccuracy in total scores: quantitative or model uncertainty; and input data uncertainty or measurement error. Simulated scenarios were run to explore the effects of measurement error and bias on hypothetical sites in a grassy woodland, using two indices of biodiversity value commonly used in Australia. Parameterisation of measurement error was informed by published information on empirical measurement error, as well as results of a field trial using 10 independent observers. As expected, larger observer errors led to less precision in valuations, although coefficients of variation in total scores were often smaller than those of the input variables due to compensatory errors. Unexpectedly, unbiased observer errors in the input variables generated biased valuations of biodiversity. This bias was almost always in one direction – the indices underestimated the true biodiversity value of most sites, high-value sites in particular. Underestimation primarily occurred where raw true values were within benchmark scoring categories but observer error caused a majority of observer estimates to fall into lower scoring categories. The size of benchmark scoring intervals and parameter weightings were the two factors that most influenced the degree of bias. Depending on the index used to calculate biodiversity value, the underestimation effect was more significant for sites with intact woody or herbaceous features, due to scoring intervals and weightings of these features. For market-based biodiversity policies, significant implications include increasing the risk of worse than expected outcomes. This research highlights the imperative to minimise observer error hitherto poorly dealt with, to design and construct quantitative ecological indices in ways that do not exacerbate uncertainty, and to explicitly address uncertainty in decision making. [Copyright &y& Elsevier]

Summary: Most of the plantation impact studies reported in literature normally use either one of the sensitivity-based approach or a hydrological model with few actually comparing the impact results from these different approaches. This paper investigates the impacts of increase or decrease in plantations and climate variability on streamflow using two approaches: the sensitivity-based approach (including a non-parametric model and six Budyko framework based models) and the hydrological modelling approach (using Xinanjiang and SIMHYD models) for three medium sized catchments in Australia. The results from the nine methods show that both plantation expansion/reduction and climatic differences can have major effects on catchment streamflow. There is a small variability in the reduction or increase in streamflow estimated by the nine methods. The results from this study show that the estimates of plantation impacts from the dynamic hydrological models are similar to those from the commonly used sensitivity-based approaches. The sensitivity-based approaches are only applicable where long term datasets are available and they only provide results at a mean annual time scale. The hydrological models simulate plantation impacted streamflow time series and so they can be used to estimate the relative contributions of land cover changes and climate change/variability at a daily, monthly or annual time-step. The outputs from the hydrological models can also be used to investigate the impacts of plantation expansion or reduction and climate change/variability on different runoff characteristics. [ABSTRACT FROM AUTHOR]

Abstract: Recent acknowledgement of the sensitivity of air quality to changes in climate has initiated a closer examination of the relationships between meteorology and air quality. This paper presents the estimated response of daily air pollutant concentrations in Melbourne, Australia to local-scale meteorology. The meteorological-pollutant relationships have been assessed after controlling for long-term trends, seasonality, weekly emissions, spatial variation, and temporal persistence using the framework of generalized additive models (GAMs). Overall, results found that the aggregate impact of meteorological variables in the models explained 26.3% of the variance in O3, 21.1% in PM10, and 26.7% in NO2. This indicates that meteorology – at the local-scale, is a relatively strong driver of air quality in Melbourne. Analysis of partial residuals plots found that changes in temperature, particularly when above 35 °C, resulted in the strongest positive response for O3 (150%), PM10 (150%) and NO2 (120%). Other variables (boundary layer height, winds, water vapor pressure, radiation, precipitation, and mean sea-level pressure) displayed some importance for one or more of the pollutants, but their impact in the models was less pronounced. In sum, we provide results that form a solid foundation for understanding the importance of local-scale meteorology as a driver of regional air pollution. Additionally, these results can be used to provide a window into how changes in climate may affect air quality. [Copyright &y& Elsevier]

Summary: This paper presents results from a modelling study carried out to investigate whether the calibrated parameter values for rainfall–runoff models based on historical observed data can be used to reliably predict runoff responses to changes in future climate inputs. The modelling experiments are carried out for four rainfall–runoff models using long records of historical daily climate and streamflow data from 61 catchments in southeast Australia. The results indicate that the models, when calibrated using more than 20years of data, can generally be used for climate impact studies where the future mean annual rainfall is not more than 15% drier or 20% wetter than the mean annual rainfall in the model calibration period. It is generally more difficult for a model calibrated over a wet period to predict runoff over a dry period compared to a model calibrated over a dry period to predict runoff over a wet period. For southeast Australia, there is a good reason to use the recent records to calibrate rainfall–runoff models to represent the current prolonged drought over the region and for climate change impact studies where the large majority of climate models predict a drier future across this region. [ABSTRACT FROM AUTHOR]

Small-area estimation techniques have typically relied on plug-in estimation based on models containing random area effects. More recently, regression M-quantiles have been suggested for this purpose, thus avoiding conventional Gaussian assumptions, as well as problems associated with the specification of random effects. However, the plug-in M-quantile estimator for the small-area mean can be shown to be the expected value of this mean with respect to a generally biased estimator of the small-area cumulative distribution function of the characteristic of interest. To correct this problem, we propose a general framework for robust small-area estimation, based on representing a small-area estimator as a functional of a predictor of this small-area cumulative distribution function. Key advantages of this framework are that it naturally leads to integrated estimation of small-area means and quantiles and is not restricted to M-quantile models. We also discuss mean squared error estimation for the resulting estimators, and demonstrate the advantages of our approach through model-based and design-based simulations, with the latter using economic data collected in an Australian farm survey. [ABSTRACT FROM AUTHOR]

The effect of information flows on the return volatility of Australian 3-year Treasury bond futures is examined using linear and non-linear GARCH models. Results show significant asymmetric information effects, where bad news has a greater impact on volatility than good news and a non-linear Threshold ARCH(1,1) in mean model provides the most accurate estimation of return volatility. Diagnostic tests confirm this finding and out of sample forecasting error statistics verify that the Threshold ARCH(1,1) in mean model yields the lowest forecasting error. The Threshold ARCH(1,1)-M model is best at capturing the asymmetric information impact on the Australian three-year T-Bond futures return volatility. [ABSTRACT FROM AUTHOR]