Understanding links between water-quality variables and nitrate concentration in freshwater streams using high frequency sensor data.

Real-time monitoring using in-situ sensors is becoming a common approach for measuring water-quality within watersheds. High-frequency measurements produce big datasets that present opportunities to conduct new analyses for improved understanding of water-quality dynamics and more effective management of rivers and streams. Of primary importance is enhancing knowledge of the relationships between nitrate, one of the most reactive forms of inorganic nitrogen in the aquatic environment, and other water-quality variables. We analysed high-frequency water-quality data from in-situ sensors deployed in three sites from different watersheds and climate zones within the National Ecological Observatory Network, USA. We used generalised additive mixed models to explain the nonlinear relationships at each site between nitrate concentration and conductivity, turbidity, dissolved oxygen, water temperature, and elevation. Temporal auto-correlation was modelled with an auto-regressive-moving-average (ARIMA) model and we examined the relative importance of the explanatory variables. Total deviance explained by the models was high for all sites (99%). Although variable importance and the smooth regression parameters differed among sites, the models explaining the most variation in nitrate contained the same explanatory variables. This study demonstrates that building a model for nitrate using the same set of explanatory water-quality variables is achievable, even for sites with vastly different environmental and climatic characteristics. Applying such models will assist managers to select cost-effective water-quality variables to monitor when the goals are to gain a spatial and temporal in-depth understanding of nitrate dynamics and adapt management plans accordingly.

Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020.

As of January 2021, Australia had effectively controlled local transmission of COVID-19 despite a steady influx of imported cases and several local, but contained, outbreaks in 2020. Throughout 2020, state and territory public health responses were informed by weekly situational reports that included an ensemble forecast of daily COVID-19 cases for each jurisdiction. We present here an analysis of one forecasting model included in this ensemble across the variety of scenarios experienced by each jurisdiction from May to October 2020. We examine how successfully the forecasts characterised future case incidence, subject to variations in data timeliness and completeness, showcase how we adapted these forecasts to support decisions of public health priority in rapidly-evolving situations, evaluate the impact of key model features on forecast skill, and demonstrate how to assess forecast skill in real-time before the ground truth is known. Conditioning the model on the most recent, but incomplete, data improved the forecast skill, emphasising the importance of developing strong quantitative models of surveillance system characteristics, such as ascertainment delay distributions. Forecast skill was highest when there were at least 10 reported cases per day, the circumstances in which authorities were most in need of forecasts to aid in planning and response.

Probabilistic Forecasts Using Expert Judgment: The Road to Recovery From COVID-19.

The COVID-19 pandemic has had a devastating effect on many industries around the world including tourism and policy makers are interested in mapping out what the recovery path will look like. We propose a novel statistical methodology for generating scenario-based probabilistic forecasts based on a large survey of 443 tourism experts and stakeholders. The scenarios map out pessimistic, most-likely and optimistic paths to recovery. Taking advantage of the natural aggregation structure of tourism data due to geographic locations and purposes of travel, we propose combining forecast reconciliation and forecast combinations implemented to historical data to generate robust COVID-free counterfactual forecasts, to contrast against. Our empirical application focuses on Australia, analyzing international arrivals and domestic flows. Both sectors have been severely affected by travel restrictions in the form of international and interstate border closures and regional lockdowns. The two sets of forecasts, allow policy makers to map out the road to recovery and also estimate the expected effect of the pandemic.

Reconstructing Missing and Anomalous Data Collected from High-Frequency In-Situ Sensors in Fresh Waters.

In situ sensors that collect high-frequency data are used increasingly to monitor aquatic environments. These sensors are prone to technical errors, resulting in unrecorded observations and/or anomalous values that are subsequently removed and create gaps in time series data. We present a framework based on generalized additive and auto-regressive models to recover these missing data. To mimic sporadically missing (i) single observations and (ii) periods of contiguous observations, we randomly removed (i) point data and (ii) day- and week-long sequences of data from a two-year time series of nitrate concentration data collected from Arikaree River, USA, where synoptically collected water temperature, turbidity, conductance, elevation, and dissolved oxygen data were available. In 72% of cases with missing point data, predicted values were within the sensor precision interval of the original value, although predictive ability declined when sequences of missing data occurred. Precision also depended on the availability of other water quality covariates. When covariates were available, even a sudden, event-based peak in nitrate concentration was reconstructed well. By providing a promising method for accurate prediction of missing data, the utility and confidence in summary statistics and statistical trends will increase, thereby assisting the effective monitoring and management of fresh waters and other at-risk ecosystems.

Early classification of spatio-temporal events using partial information.

This paper investigates event extraction and early event classification in contiguous spatio-temporal data streams, where events need to be classified using partial information, i.e. while the event is ongoing. The framework incorporates an event extraction algorithm and an early event classification algorithm. We apply this framework to synthetic and real problems and demonstrate its reliability and broad applicability. The algorithms and data are available in the R package eventstream, and other code in the supplementary material.

Hospital characteristics, rather than surgical volume, predict length of stay following colorectal cancer surgery.

OBJECTIVE: Length of hospital stay (LOS) is considered a vital component for successful colorectal surgery treatment. Evidence of an association between hospital surgery volume and LOS has been mixed. Data modelling techniques may give inconsistent results that adversely impact conclusions. This study applied techniques to overcome possible modelling drawbacks. METHOD: An additive quantile regression model formulated to isolate hospital contextual effects was applied to every colorectal surgery for cancer conducted in Victoria, Australia, between 2005 and 2015, involving 28,343 admissions in 90 Victorian hospitals. The model compared hospitals' operational efficiencies regarding LOS. RESULTS: Hospital LOS operational efficiencies for colorectal cancer surgery varied markedly between the 90 hospitals and were independent of volume. This result was adjusted for pertinent patient and hospital characteristics. CONCLUSION: No evidence was found that higher annual surgery volume was associated with lower LOS for patients undergoing colorectal cancer surgery. Our model showed strong evidence that differences in LOS efficiency between hospitals was driven by hospital contextual effects that were not predicted by provider volume. Further study is required to elucidate these inherent differences between hospitals. Implications for public health: Our model indicated improved efficiency would benefit the patient and medical system by lowering LOS and reducing expenditure by more than $3 million per year.

Predicting sediment and nutrient concentrations from high-frequency water-quality data.

Water-quality monitoring in rivers often focuses on the concentrations of sediments and nutrients, constituents that can smother biota and cause eutrophication. However, the physical and economic constraints of manual sampling prohibit data collection at the frequency required to adequately capture the variation in concentrations through time. Here, we developed models to predict total suspended solids (TSS) and oxidized nitrogen (NOx) concentrations based on high-frequency time series of turbidity, conductivity and river level data from in situ sensors in rivers flowing into the Great Barrier Reef lagoon. We fit generalized-linear mixed-effects models with continuous first-order autoregressive correlation structures to water-quality data collected by manual sampling at two freshwater sites and one estuarine site and used the fitted models to predict TSS and NOx from the in situ sensor data. These models described the temporal autocorrelation in the data and handled observations collected at irregular frequencies, characteristics typical of water-quality monitoring data. Turbidity proved a useful and generalizable surrogate of TSS, with high predictive ability in the estuarine and fresh water sites. Turbidity, conductivity and river level served as combined surrogates of NOx. However, the relationship between NOx and the covariates was more complex than that between TSS and turbidity, and consequently the ability to predict NOx was lower and less generalizable across sites than for TSS. Furthermore, prediction intervals tended to increase during events, for both TSS and NOx models, highlighting the need to include measures of uncertainty routinely in water-quality reporting. Our study also highlights that surrogate-based models used to predict sediments and nutrients need to better incorporate temporal components if variance estimates are to be unbiased and model inference meaningful. The transferability of models across sites, and potentially regions, will become increasingly important as organizations move to automated sensing for water-quality monitoring throughout catchments.

A framework for automated anomaly detection in high frequency water-quality data from in situ sensors.

Monitoring the water quality of rivers is increasingly conducted using automated in situ sensors, enabling timelier identification of unexpected values or trends. However, the data are confounded by anomalies caused by technical issues, for which the volume and velocity of data preclude manual detection. We present a framework for automated anomaly detection in high-frequency water-quality data from in situ sensors, using turbidity, conductivity and river level data collected from rivers flowing into the Great Barrier Reef. After identifying end-user needs and defining anomalies, we ranked anomaly importance and selected suitable detection methods. High priority anomalies included sudden isolated spikes and level shifts, most of which were classified correctly by regression-based methods such as autoregressive integrated moving average models. However, incorporation of multiple water-quality variables as covariates reduced performance due to complex relationships among variables. Classifications of drift and periods of anomalously low or high variability were more often correct when we applied mitigation, which replaces anomalous measurements with forecasts for further forecasting, but this inflated false positive rates. Feature-based methods also performed well on high priority anomalies and were similarly less proficient at detecting lower priority anomalies, resulting in high false negative rates. Unlike regression-based methods, however, all feature-based methods produced low false positive rates and have the benefit of not requiring training or optimization. Rule-based methods successfully detected a subset of lower priority anomalies, specifically impossible values and missing observations. We therefore suggest that a combination of methods will provide optimal performance in terms of correct anomaly detection, whilst minimizing false detection rates. Furthermore, our framework emphasizes the importance of communication between end-users and anomaly detection developers for optimal outcomes with respect to both detection performance and end-user application. To this end, our framework has high transferability to other types of high frequency time-series data and anomaly detection applications.

Handgun Acquisitions in California After Two Mass Shootings.

BACKGROUND: Mass shootings are common in the United States. They are the most visible form of firearm violence. Their effect on personal decisions to purchase firearms is not well-understood. OBJECTIVE: To determine changes in handgun acquisition patterns after the mass shootings in Newtown, Connecticut, in 2012 and San Bernardino, California, in 2015. DESIGN: Time-series analysis using seasonal autoregressive integrated moving-average (SARIMA) models. SETTING: California. POPULATION: Adults who acquired handguns between 2007 and 2016. MEASUREMENTS: Excess handgun acquisitions (defined as the difference between actual and expected acquisitions) in the 6-week and 12-week periods after each shooting, overall and within subgroups of acquirers. RESULTS: In the 6 weeks after the Newtown and San Bernardino shootings, there were 25 705 (95% prediction interval, 17 411 to 32 788) and 27 413 (prediction interval, 15 188 to 37 734) excess acquisitions, respectively, representing increases of 53% (95% CI, 30% to 80%) and 41% (CI, 19% to 68%) over expected volume. Large increases in acquisitions occurred among white and Hispanic persons, but not among black persons, and among persons with no record of having previously acquired a handgun. After the San Bernardino shootings, acquisition rates increased by 85% among residents of that city and adjacent neighborhoods, compared with 35% elsewhere in California. LIMITATIONS: The data relate to handguns in 1 state. The statistical analysis cannot establish causality. CONCLUSION: Large increases in handgun acquisitions occurred after these 2 mass shootings. The spikes were short-lived and accounted for less than 10% of annual handgun acquisitions statewide. Further research should examine whether repeated shocks of this kind lead to substantial increases in the prevalence of firearm ownership. PRIMARY FUNDING SOURCE: None.

Associations between outdoor fungal spores and childhood and adolescent asthma hospitalizations.

BACKGROUND: Childhood asthma is a significant public health problem and severe exacerbations can result in diminished quality of life and hospitalization. OBJECTIVE: We sought to examine the contribution of outdoor fungi to childhood and adolescent asthma hospitalizations. METHODS: The Melbourne Air Pollen Children and Adolescent study is a case-crossover study of 644 children and adolescents (aged 2-17 years) hospitalized for asthma. The Melbourne Air Pollen Children and Adolescent study collected individual data on human rhinovirus infection and sensitization to Alternaria and Cladosporium and daily counts of ambient concentrations of fungal spores, pollen, and air pollutants. Conditional logistic regression models were used to assess associations with increases in spore counts while controlling for potential confounding and testing interactions. RESULTS: Exposure to Alternaria (adjusted odds ratio [aOR], 1.07; 95% CI, 1.03-1.11), Leptosphaeria (aOR, 1.05; 95% CI, 1.02-1.07), Coprinus (aOR, 1.04; 95% CI, 1.01-1.07), Drechslera (aOR, 1.03; 95% CI, 1.00-1.05), and total spores (aOR, 1.05; 95% CI, 1.01-1.09) was significantly associated with child asthma hospitalizations independent of human rhinovirus infection. There were significant lagged effects up to 3 days with Alternaria, Leptosphaeria, Cladosporium, Sporormiella, Coprinus, and Drechslera. Some of these associations were significantly greater in participants with Cladosporium sensitization. CONCLUSIONS: Exposures to several outdoor fungal spore taxa, including some not reported in previous research, are associated with the risk of child and adolescent asthma hospitalization, particularly in individuals sensitized to Cladosporium. We need further studies to examine cross-reactivity causing asthma exacerbations. Identifying sensitization to multiple fungal allergens in children with asthma could support the design and implementation of more effective strategies to prevent asthma exacerbations.

Exploring the influence of short-term temperature patterns on temperature-related mortality: a case-study of Melbourne, Australia.

BACKGROUND: Several studies have identified the association between ambient temperature and mortality; however, several features of temperature behavior and their impacts on health remain unresolved. We obtain daily counts of nonaccidental all-cause mortality data in the elderly (65 + years) and corresponding meteorological data for Melbourne, Australia during 1999 to 2006. We then characterize the temporal behavior of ambient temperature development by quantifying the rates of temperature change during periods designated by pre-specified windows ranging from 1 to 30 days. Finally, we evaluate if the association between same day temperature and mortality in the framework of a Poisson regression and include our temperature trajectory variables in order to assess if associations were modified by the nature of how the given daily temperature had evolved. RESULTS: We found a positive significant association between short-term mortality risk and daily average temperature as mortality risk increased 6 % on days when temperatures were above the 90th percentile as compared to days in the referent 25-75th. In addition, we found that mortality risk associated with daily temperature varied by the nature of the temperature trajectory over the preceding twelve days and that peaks in mortality occurred during periods of high temperatures and stable trajectories and during periods of increasing higher temperatures and increasing trajectories. CONCLUSION: Our method presents a promising tool for improving understanding of complex temperature health associations. These findings suggest that the nature of sub-monthly temperature variability plays a role in the acute impacts of temperature on mortality; however, further studies are suggested.

Coherent mortality forecasting: the product-ratio method with functional time series models.

When independence is assumed, forecasts of mortality for subpopulations are almost always divergent in the long term. We propose a method for coherent forecasting of mortality rates for two or more subpopulations, based on functional principal components models of simple and interpretable functions of rates. The product-ratio functional forecasting method models and forecasts the geometric mean of subpopulation rates and the ratio of subpopulation rates to product rates. Coherence is imposed by constraining the forecast ratio function through stationary time series models. The method is applied to sex-specific data for Sweden and state-specific data for Australia. Based on out-of-sample forecasts, the coherent forecasts are at least as accurate in overall terms as comparable independent forecasts, and forecast accuracy is homogenized across subpopulations.

Forecasts of COPD mortality in Australia: 2006-2025.

BACKGROUND: Chronic Obstructive Pulmonary Disease (COPD) is currently the fifth leading cause of death in Australia, and there are marked differences in mortality trends between men and women. In this study, we have sought to model and forecast age related changes in COPD mortality over time for men and women separately over the period 2006-2025. METHODS: Annual COPD death rates in Australia from 1922 to 2005 for age groups (50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80-84, 85+) were used. Functional time series models of age-specific COPD mortality rates for men and women were used, and forecasts of mortality rates were modelled separately for men and women. RESULTS: Functional time series models with four basis functions were fitted to each population separately. Twenty-year forecasts were computed, and indicated an overall decline. This decline may be slower for women than for men. By age, we expect similar rates of decline in men over time. In contrast, for women, forecasts for the age group 75-79 years suggest less of a decline over time compared to younger age groups. CONCLUSIONS: By using a new method to predict age-specific trends in COPD mortality over time, this study provides important insights into at-risk age groups for men and women separately, which has implications for policy and program development.

Method for optimizing coating properties based on an evolutionary algorithm approach.

In industry as well as many areas of scientific research, data collected often contain a number of responses of interest for a chosen set of exploratory variables. Optimization of such multivariable multiresponse systems is a challenge well suited to genetic algorithms as global optimization tools. One such example is the optimization of coating surfaces with the required absolute and relative sensitivity for detecting analytes using devices such as sensor arrays. High-throughput synthesis and screening methods can be used to accelerate materials discovery and optimization; however, an important practical consideration for successful optimization of materials for arrays and other applications is the ability to generate adequate information from a minimum number of experiments. Here we present a case study to evaluate the efficiency of a novel evolutionary model-based multiresponse approach (EMMA) that enables the optimization of a coating while minimizing the number of experiments. EMMA plans the experiments and simultaneously models the material properties. We illustrate this novel procedure for materials optimization by testing the algorithm on a sol-gel synthetic route for production and optimization of a well studied amino-methyl-silane coating. The response variables of the coating have been optimized based on application criteria for micro- and macro-array surfaces. Spotting performance has been monitored using a fluorescent dye molecule for demonstration purposes and measured using a laser scanner. Optimization is achieved by exploring less than 2% of the possible experiments, resulting in identification of the most influential compositional variables. Use of EMMA to optimize control factors of a product or process is illustrated, and the proposed approach is shown to be a promising tool for simultaneously optimizing and modeling multivariable multiresponse systems.

Forecasting age-related changes in breast cancer mortality among white and black US women: a functional data approach.

BACKGROUND: The disparity in breast cancer mortality rates among white and black US women is widening, with higher mortality rates among black women. We apply functional time series models on age-specific breast cancer mortality rates for each group of women, and forecast their mortality curves using exponential smoothing state-space models with damping. MATERIALS AND METHODS: The data were obtained from the Surveillance, Epidemiology and End Results (SEER) program of the US [1]. Mortality data were obtained from the National Centre for Health Statistics (NCHS) available on the SEER*Stat database. We use annual unadjusted breast cancer mortality rates from 1969 to 2004 in 5-year age groups (45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80-84). Age-specific mortality curves were obtained using nonparametric smoothing methods. The curves are then decomposed using functional principal components and we fit functional time series models with four basis functions for each population separately. The curves from each population are forecast and prediction intervals are calculated. RESULTS: Twenty-year forecasts indicate an overall decline in future breast cancer mortality rates for both groups of women. This decline appears to be steeper among white women aged 55-73 and black women aged 60-84. For black women under 55 years of age, the forecast rates are relatively stable indicating there is no significant change in future breast cancer mortality rates among young black women in the next 20 years. CONCLUSION: White women have smooth and consistent patterns in breast cancer mortality rates for all age-groups whereas the mortality rates for black women are much more variable. The projections suggest, for some age groups, black American women may not benefit equally from the overall decline in breast cancer mortality in the United States.

Measurement of changes in antihypertensive drug utilisation following primary care educational interventions.

PURPOSE: To measure changes in drug utilisation following a national general practice education program aimed at improving prescribing for hypertension. METHODS: A series of nationally implemented, multifaceted educational interventions using social marketing principles focusing on prescribing for hypertension, was commenced in October 1999, and repeated in September 2001 and August 2003. The target group was all primary care prescribers in Australia and interventions were both active (voluntary) and passive. Newsletter and prescribing feedback was mailed in October 1999, September 2001 (newsletter only) and August 2003. Approximately a third of general practitioners (GPs) in Australia undertook at least one active educational activity (clinical audit, educational visit or case study) during the period October 1999-April 2004. National dispensing data from 1996 to 2004 were analysed using time series methodology with a decay term for intervention effect, to assess trends in prescribing of various classes of antihypertensives. In particular, the program aimed to increase the prescribing of thiazide diuretics and beta blockers. RESULTS: Consistent with key intervention messages, the program achieved an increase in low-dose thiazide and beta blocker prescribing. The rate of prescribing of low-dose thiazides doubled from 1.1 per 1000 consultations in October 1999 to 2.4 per 1000 in October 2003. Beta-blocker utilisation showed a more modest but significant increase over the time of the study, with the change in observed versus expected rate of prescribing increasing by 8% by April 2004. Therapeutic options for treating hypertension changed markedly in the time of the study with the advent of ACE inhibitor/Angiotensin II receptor antagonists and thiazide combination products. It is important, therefore, to interpret the results in light of these changes. CONCLUSION: A national education program aimed at GPs was successful in improving prescribing for hypertension. Lessons learned will be applied in evaluation of future NPS programs and are also applicable to analysis of other interventions aimed at influencing prescribing behaviour.

Forecasting age-specific breast cancer mortality using functional data models.

Accurate estimates of future age-specific incidence and mortality are critical for allocation of resources to breast cancer control programmes and evaluation of screening programmes. The purpose of this study is to apply functional data analysis techniques to model age-specific breast cancer mortality time trends, and forecast entire age-specific mortality functions using a state-space approach. We use annual unadjusted breast cancer mortality rates in Australia, from 1921 to 2001 in 5 year age groups (45 to 85+). We use functional data analysis techniques where mortality and incidence are modelled as curves with age as a functional covariate varying by time. Data are smoothed using non-parametric smoothing methods then decomposed (using principal components analysis) to estimate basis functions that represent the functional curve. Period effects from the fitted coefficients are forecast then multiplied by the basis functions, resulting in a forecast mortality curve with prediction intervals. To forecast, we adopt a state-space approach and an automatic modelling framework for selecting among exponential smoothing methods.Overall, breast cancer mortality rates in Australia remained relatively stable from 1960 to the late 1990s, but have declined over the last few years. A set of four basis functions minimized the mean integrated squared forecasting error and account for 99.3 per cent of variation around the mean mortality curve. Twenty year forecasts suggest a continuing decline, but at a slower rate, and stabilizing beyond 2010. Forecasts show a decline in all age groups with the greatest decline in older women. The proposed methods have the potential to incorporate important covariates such as hormone replacement therapy and interventions to represent mammographic screening. This would be particularly useful for evaluating the impact of screening on mortality and incidence from breast cancer.

Measuring change in prescription drug utilization in Australia.

PURPOSE: The National Prescribing Service Ltd (NPS) aims to improve prescribing and use of medicines consistent with evidence-based best practice. This report compares two statistical methods used to determine whether multiple educational interventions influenced antibiotic prescription in Australia. METHODS: Monthly data (July 1996 to June 2003) were obtained from a national claims database. The outcome measures were the median number of antibiotic prescriptions per 1000 consultations for each general practitioner (GP) each month, and the mean proportion (across GPs) of each subgroup of antibiotics (e.g. roxithromycin) out of nine antibiotics having primary use for upper respiratory tract infection. Two approaches were used to investigate shifts in prescribing: augmented regression, which included seasonality, autocorrelation and one intervention; and seasonally adjusted piecewise linear dynamic regression, which removed seasonality prior to modelling, included several interventions, GP participation and autocorrelated errors. Both methods are variations of piecewise linear regression modelling. RESULTS: Both approaches described a similar decrease in rates, with a non-significant change after the first intervention. The inclusion of more interventions and GP participation made no difference. Using roxithromycin as an example of the analyses of proportions, both approaches implied that after the first intervention the proportion decreased significantly. The statistical significance of this intervention disappears when other interventions are included. CONCLUSIONS: The two analyses provide results which agree regarding the possible impact of the NPS interventions, but raise questions about what is the best way to model drug utilization, particularly regarding whether to include all intervention terms when they belong to an extended roll-out of related interventions.

Sensitivity of the estimated air pollution-respiratory admissions relationship to statistical model choice.

The objective of this study was to demonstrate the methodological shortcomings of currently available analytical methods for single-city time series data. We analyzed daily Chronic Obstructive Pulmonary Disease (COPD) and daily asthma hospital admissions in Melbourne, Australia from July 1989 to December 1992. Air pollution data comprised nitrogen dioxide, ozone and sulphur dioxide and air particles index consistent with particulates between 0.1 and 1 microm in aerodynamic diameter. Statistical analyses were performed using generalized linear models, generalized additive models, Poisson autoregressive models and transitional regression models. The estimated effect of nitrogen dioxide on COPD hospital admissions was similar across the different statistical models, RR = 1.06 (95% CI 1.01-1.11). Similarly the estimated effect of nitrogen dioxide on asthma hospital admissions was also consistent, RR = 1.05 (95% CI 1.01-1.09). However, the effects of ozone, air particles index and sulphur dioxide were highly sensitive to model specification for both COPD and asthma hospital admissions. In single-city studies of air pollution and respiratory disease, very different conclusions can be drawn from competing models. Furthermore, real time series data have greater complexity than any of the commonly-used existing models allow. Consequently, single-city studies should use several statistical models to demonstrate the stability of estimated effects.