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Objective: To estimate the number of people in Australia with long COVID by age group, and the associated medium term productivity and economic losses. Study design: Modelling study: a susceptible–exposed–infected–recovered (SEIR) model to estimate the number of people with long COVID over time following single infections, and a labour supply model to estimate productivity losses as a proportion of gross domestic product (GDP). Setting: Australia, 2022–2024. Main outcome measures: Estimated number of people infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during 2022–2023 (based on serosurvey data) who have long COVID, 2022–2024, by age group; estimated GDP loss during 2022 caused by reduced labour supply attributable to long COVID. Results: Our model projected that the number of people with long COVID following a single infection in 2022 would peak in September 2022, when 310 341–1 374 805 people (1.2–5.4% of Australians) would have symptoms of long COVID, declining to 172 530–872 799 people (0.7–3.4%) in December 2024, including 7902–30 002 children aged 0–4 years (0.6–2.2%). The estimated mean labour loss attributable to long COVID in 2022 was projected to be 102.4 million (95% confidence interval [CI], 50.4–162.2 million) worked hours, equivalent to 0.48% (95% CI, 0.24–0.76%) of total worked hours in Australia during the 2020–21 financial year. The estimated mean GDP loss caused by the projected decline in labour supply and reduced use of other production factors was $9.6 billion (95% CI, $4.7–15.2 billion), or 0.5% of GDP. The estimated labour loss was greatest for people aged 30–39 years (27.5 million [95% CI, 16.0–41.0 million] hours; 26.9% of total labour loss) and people aged 40–49 years (24.5 million [95% CI, 12.1–38.7 million] hours; 23.9% of total labour loss). Conclusion: Widespread SARS-CoV-2 infections in Australia mean that even a small proportion of infected people developing long COVID-related illness and disability could

ABSTRACT

We compare the health and economic costs of early (actual), delayed and no suppression of COVID-19 infections in 2020 in Australia. Using a fit-for-purpose compartment model that we fitted from recorded data, a value of a statistical life year (VSLY) and an age-adjusted value of statistical life (A-VSL), we find: (1) the economic costs of no suppression are multiples more than for early suppression; (2) VSLY welfare losses of fatalities equivalent to GDP losses mean that for early suppression to not to be the preferred strategy requires that Australians prefer more than 12,500–30,000 deaths to the economy costs of early suppression, depending on the fatality rate; and (3) early rather than delayed suppression imposes much lower economy and health costs. We conclude that in high-income countries, like Australia, a ‘go early, go hard’ strategy to suppress COVID-19 results in the lowest estimated public health and economy costs.

Most cities in Australia periodically face acute shortages of potable water. In most of them the supply of potable water comes from sequestering supplies from eco-systems away from but close to that in which the urban development occurs. The response usually is to try to husband the water sequestered and stored in dams by managing the demand to 'get' through the dry periods. A combination of factors, including the consequences of climate change and the increase in population of most cities together with increasing per capita consumption is now pressing state governments that have the constitutional responsibility to manage water resources to find ways of responding to demand.

This chapter examines three broad themes of change in governance and regulation which have emerged in recent decades in the urban water supply, treatment, and wastewater sector - collectively described here as the 'urban water sector'. In brief, these themes are the devolution in management and control, increased sophistication in regulation, and the re-emergency of social and environmental concerns.

The general fragility and unique vulnerability of small island countries in the Pacific, has been widely acknowledged for a long time. So too has been the diversity of their geography, geology and sources of available freshwater. Sparse island communities have demonstrated remarkable resilience n the face of climatic extremes and natural hazards over the last 1,000-2,000 years. The well-developed local institutions, resilient social systems, sensitivity to environmental change, and the high value placed on equity in Pacific islands have provided capacities for adapting to threats and change and have allowed low density subsistence populations to survive. Traditional coping mechanisms and customary right and values, however, appear mismatched to the demands, responsibilities and altered social dynamics of high-density urban centres, many of which are in an interactive phase between subsistence and urban living. In this chapter we outline the interactions between technical, climatic, social and cultural factors in urban water supply and water quality in PICs. Our experience has been that to improve urban water supplies, policy makers, donors, and practitioners need to recognise and address these complex interactions in an integrated way. Single-issue, infrastructure-focused solutions that have not engaged local communities have had a poor success rate in the region.

Responding to pressure on the natural and built environments from population growth, the need for safe and secure water to support resilient and liveable communities, and the need to adapt to increased climate risk and vulnerability, many cities around the world have sought new ways to develop, manage, and sustain their urban environments. One such concept is 'water sensitive urban design' (WSUD). WSUD is the integrated design of the urban water cycle, incorporating water supply, wastewater, stormwater and groundwater management, urban design and environmental protection. The focus of this chapter is on the challenges of implementing WSUD, specifically with respect to the stormwater management aspects of WSUD.

Marine protected areas (MPAs) are a primary policy instrument for managing and protecting coral reefs. Successful MPAs ultimately depend on knowledge-based decision making, where scientific research is integrated into management actions. Fourteen coral reef MPA managers and sixteen academics from eleven research, state and federal government institutions each outlined at least five pertinent research needs for improving the management of MPAs situated in Australian coral reefs. From this list of 173 key questions, we asked members of each group to rank questions in order of urgency, redundancy and importance, which allowed us to explore the extent of perceptional mismatch and overlap among the two groups. Our results suggest the mismatch among MPA managers and academics is small, with no significant difference among the groups in terms of their respective research interests, or the type of questions they pose. However, managers prioritised spatial management and monitoring as research themes, whilst academics identified climate change, resilience, spatial management, fishing and connectivity as the most important topics. Ranking of the posed questions by the two groups was also similar, although managers were less confident about the achievability of the posed research questions and whether questions represented a knowledge gap. We conclude that improved collaboration and knowledge transfer among management and academic groups can be used to achieve similar objectives and enhance the knowledge-based management of MPAs. © 2012 Elsevier Ltd.

the classic study on diffusion of Tetracycline by Coleman, Katz and Menzel (1966). Medical Innovation articulates how different patterns of interpersonal communications can influence the diffusion process at different stages of adoption. In their pioneering study, individual network (discussion, friendship or advice) was perceived as a set of disjointed pairs, and the extent of influences were therefore, evaluated for pairs of individuals. Given the existence of overlapping networks and consequent influences on doctors’ adoption decisions, the complexity of actual events was not captured by pair analysis. Subsequent reanalyses (Burt 1987, Strang and Tuma 1993, Valente 1995, Van den Bulte and Lilien 2001) failed to capture the complexity involved in the diffusion process and had a static exposure of the network structure. In this paper, for the first time, we address these limitations by combining Agent-Based Modeling (ABM) and network analysis. Based on the findings of Coleman et. al. (1966) study, we develop a diffusion model, Gammanym. Using SMALLTALK programming language, Gammanym is developed with CORMAS platform under Visual Works environment. The medical community is portrayed in an 8 X 8 spatial grid. The unit cell captures three different locations for professional interactions: practices, hospitals, and conference centers, randomly located over the spatial grid. Two social agents- Doctor and Laboratory are depicted in the model. Doctors are the principal agents in the diffusion process and are initially located at their respective practices. A doctor’s adoption decision is influenced by a random friendship network, and a professional network created through discussions with office colleagues, or hospital visits or conference attendance. A communicating agent, Laboratory, on the other hand, influences doctors’ adoption decisions by sending information through multiple channels: medical representatives or detailman visiting practices, journals sent to doctors

the classic study on diffusion of Tetracycline by Coleman, Katz and Menzel (1966). Medical Innovation articulates how different patterns of interpersonal communications can influence the diffusion process at different stages of adoption. In their pioneering study, individual network (discussion, friendship or advice) was perceived as a set of disjointed pairs, and the extent of influences were therefore, evaluated for pairs of individuals. Given the existence of overlapping networks and consequent influences on doctors’ adoption decisions, the complexity of actual events was not captured by pair analysis. Subsequent reanalyses (Burt 1987, Strang and Tuma 1993, Valente 1995, Van den Bulte and Lilien 2001) failed to capture the complexity involved in the diffusion process and had a static exposure of the network structure. In this paper, for the first time, we address these limitations by combining Agent-Based Modeling (ABM) and network analysis.