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This study explores the contrasting impacts of the COVID-19 pandemic on various industries in Australia. Considering all daily announced information, we analyzed the diverse impacts of COVID-19 on the sectoral stock returns from 26 January to 20 July 2020. Sixteen out of twenty examined stock indices negatively react to the daily rise in COVID-19 confirmed cases. Several actions from the Australian government to control the pandemic are relatively ineffective in boosting the overall financial market; however, some positive interactions are captured in five sectors of industrials, health care, metals and mining, materials, and resources. The result shows that all industries that benefited from government financial assistance are either shielded or less severely affected by the pandemic. While sectors that did not directly receive financial remedies relatively showed no enhancement in their overall performance. Having achieved short-term success in helping the economy, the government recorded an all-time high deficit since 2004 that might eventually lead to adverse effects on the overall economy. The Australian equity market is found to be rationally distinct to the crude oil price risk, while positive correlations between AUD/USD rate and real estate-related sectors are reported.

We investigate joint modelling of longevity trends using the spatial statistical framework of Gaussian process (GP) regression. Our analysis is motivated by the Human Mortality Database (HMD) that provides unified raw mortality tables for nearly 40 countries. Yet few stochastic models exist for handling more than two populations at a time. To bridge this gap, we leverage a spatial covariance framework from machine learning that treats populations as distinct levels of a factor covariate, explicitly capturing the cross-population dependence. The proposed multi-output GP models straightforwardly scale up to a dozen populations and moreover intrinsically generate coherent joint longevity scenarios. In our numerous case studies, we investigate predictive gains from aggregating mortality experience across nations and genders, including by borrowing the most recently available “foreign” data. We show that in our approach, information fusion leads to more precise (and statistically more credible) forecasts. We implement our models in R, as well as a Bayesian version in Stan that provides further uncertainty quantification regarding the estimated mortality covariance structure. All examples utilise public HMD datasets.

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Patients frequently have comorbidities that when combined with their primary diagnosis qualifies the patient for hospice. Consequently, patients are at risk for polypharmacy due to the number of medications prescribed to treat both the underlying conditions and the related symptoms. Polypharmacy is associated with negative consequences, including increased risk for adverse drug events, drug–drug and drug–disease interactions, reduced functional status and falls, multiple geriatric syndromes, medication nonadherence, and increased mortality. Polypharmacy also increases the complexity of medication management for caregivers and contributes to the cost of prescription drugs for hospices and patients. Deprescribing or removing nonbeneficial or ineffective medications can reduce polypharmacy in hospice. We study medication possession ratios and rates of deprescribing of commonly prescribed but potentially nonbeneficial classes of medication using a large hospice pharmacy database. Prevalence of some classes of potentially inappropriate medications is high. We report possession ratios for 10 frequently prescribed classes, and, because death and prescription termination are competing events, we calculate prescription termination rates using Cumulative Incidence Functions. Median duration of antifungal and antiviral medications is brief (5 and 7 days, respectively), while statins and diabetes medications have slow discontinuance rates (median termination durations of 93 and 197 days). Almost all patients with a proton pump inhibitor prescription have the drug for their entire hospice stay. Data from this study identify those drug classes that are commonly deprescribed slowly, suggesting drug classes and diagnoses that hospices may wish to focus on more closely, as they act to limit polypharmacy and reduce prescription costs.