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COVID-19 remains a significant public health problem in New South Wales, Australia. Although the NSW government is employing various control policies, more specific and compelling interventions are needed to control the spread of COVID-19. This paper presents a modified SEIR-X model based on a nonlinear ordinary differential equations system that considers the transmission routes from asymptomatic (Exposed) and symptomatic (Mild and Critical) individuals. The model is fitted to the corresponding cumulative number of cases in metropolitan and rural health districts of NSW reported by the Health Department and parameterised using the least-squares method. The basic reproduction number (R 0) , which measures the possible spread of COVID-19 in a population, is computed using the next generation operator method. Sensitivity analysis of the model parameters reveals that the transmission rate had an enormous influence on R 0 , which may be an option for controlling this disease. Two time-dependent control strategies, namely preventive (it refers to effort at inhibiting the virus transmission and prevention of case development from Exposed, Mild, Critical, Non-hospitalised and Hospitalised population) and management (it refers to enhance the management of Non-hospitalised and Hospitalised individuals who are infected by COVID-19) measures, are considered to mitigate this disease's dynamics using Pontryagin's maximum principle. The most sensible control strategy is determined through the cost-effectiveness analysis for the metropolitan and rural health districts of NSW. Our findings suggest that of the single intervention strategies, enhanced preventive strategy is more cost-effective than management control strategy, as it promptly reduces COVID-19 cases in NSW. In addition, combining preventive and management interventions simultaneously is found to be the most cost-effective. Alternative policies can be implemented to control COVID-19 depending on the policymakers' decisions. Numerical simulations of the overall system are performed to demonstrate the theoretical outcomes. [ABSTRACT FROM AUTHOR]

The visual modeling method enables flexible interactions with rich graphical depictions of data and supports the exploration of the complexities of epidemiological analysis. However, most epidemiology visualizations do not support the combined analysis of objective factors that might influence the transmission situation, resulting in a lack of quantitative and qualitative evidence. To address this issue, we developed a portrait-based visual modeling method called +msRNAer. This method considers the spatiotemporal features of virus transmission patterns and multidimensional features of objective risk factors in communities, enabling portrait-based exploration and comparison in epidemiological analysis. We applied +msRNAer to aggregate COVID-19-related datasets in New South Wales, Australia, combining COVID-19 case number trends, geo-information, intervention events, and expert-supervised risk factors extracted from local government area-based censuses. We perfected the +msRNAer workflow with collaborative views and evaluated its feasibility, effectiveness, and usefulness through one user study and three subject-driven case studies. Positive feedback from experts indicates that +msRNAer provides a general understanding for analyzing comprehension that not only compares relationships between cases in time-varying and risk factors through portraits but also supports navigation in fundamental geographical, timeline, and other factor comparisons. By adopting interactions, experts discovered functional and practical implications for potential patterns of long-standing community factors regarding the vulnerability faced by the pandemic. Experts confirmed that +msRNAer is expected to deliver visual modeling benefits with spatiotemporal and multidimensional features in other epidemiological analysis scenarios. [ABSTRACT FROM AUTHOR]

This article reports on a study analysing changes in the use of digital technologies and working from home during the COVID-19 crisis and the impact of these changes on the wellbeing of five female university lecturers from Australia and Sweden. Applying collaborative autoethnographical methods, this study employed Weick's sensemaking framework to explore how the academics made sense of these sudden changes. The Positive emotion, Engagement, Relationships, Meaning, and Accomplishment (PERMA) wellbeing framework was also employed to explore the effect of these changes on the academics' wellbeing. Findings from the reflective narratives show that after the initial experiences of stress, each university lecturer was able to adapt and navigate the online teaching environment during the pandemic. However, the time constraints in preparing and adapting to online teaching, and working from home, were experienced by some of the university lecturers as highly stressful and isolating which impacted their sense of wellbeing. Even so, working from home was recognized as a positive experience, providing time for research, hobbies, and time with family. This study addresses a gap in current knowledge by examining the impact of the sudden transition to online teaching and learning had on academic wellbeing as conceptualised through the PERMA framework. In addition, by applying Weick's sensemaking framework, this study provides a unique perspective around how academics made sense of the sudden switch to online teaching and learning during COVID-19. [ABSTRACT FROM AUTHOR]

The visual modeling method enables flexible interactions with rich graphical depictions of data and supports the exploration of the complexities of epidemiological analysis. However, most epidemiology visualizations do not support the combined analysis of objective factors that might influence the transmission situation, resulting in a lack of quantitative and qualitative evidence. To address this issue, we developed a portrait-based visual modeling method called +msRNAer. This method considers the spatiotemporal features of virus transmission patterns and multidimensional features of objective risk factors in communities, enabling portrait-based exploration and comparison in epidemiological analysis. We applied +msRNAer to aggregate COVID-19-related datasets in New South Wales, Australia, combining COVID-19 case number trends, geo-information, intervention events, and expert-supervised risk factors extracted from local government area-based censuses. We perfected the +msRNAer workflow with collaborative views and evaluated its feasibility, effectiveness, and usefulness through one user study and three subject-driven case studies. Positive feedback from experts indicates that +msRNAer provides a general understanding for analyzing comprehension that not only compares relationships between cases in time-varying and risk factors through portraits but also supports navigation in fundamental geographical, timeline, and other factor comparisons. By adopting interactions, experts discovered functional and practical implications for potential patterns of long-standing community factors regarding the vulnerability faced by the pandemic. Experts confirmed that +msRNAer is expected to deliver visual modeling benefits with spatiotemporal and multidimensional features in other epidemiological analysis scenarios. [ABSTRACT FROM AUTHOR]

As children's use of screens increased during the COVID pandemic, their reading of traditional books was affected, a national survey of Australian parents shows. The study was conducted by researchers at the University of Melbourne to compare young people's use of screens and books in the pandemic. Their online survey of 513 primary caregivers of children aged seven to thirteen around Australia showed that tablet use flourished during the pandemic and that COVID lockdowns influenced book buying and library borrowing in consequential ways for publishing and literature. Many parents believed their children's use of screens had come at the expense of book reading. [ABSTRACT FROM AUTHOR]