RESUMO
The novel coronavirus COVID-19 arrived on Australian shores around 25 January 2020. This paper presents a novel method of dynamically modeling and forecasting the COVID-19 pandemic in Australia with a high degree of accuracy and in a timely manner using limited data; a valuable resource that can be used to guide government decision-making on societal restrictions on a daily and/or weekly basis. The "partially-observable stochastic process" used in this study predicts not only the future actual values with extremely low error, but also the percentage of unobserved COVID-19 cases in the population. The model can further assist policy makers to assess the effectiveness of several possible alternative scenarios in their decision-making processes.
Assuntos
Infecções por Coronavirus/epidemiologia , Modelos Estatísticos , Pneumonia Viral/epidemiologia , Austrália/epidemiologia , Betacoronavirus , COVID-19 , Humanos , Pandemias , SARS-CoV-2RESUMO
A circular economy involves maintaining manufactured products in circulation, distributing resource and environmental costs over time and with repeated use. In a linear supply chain, manufactured products are used once and discarded. In high-income nations, health care systems increasingly rely on linear supply chains composed of single-use disposable medical devices. This has resulted in increased health care expenditures and health care-generated waste and pollution, with associated public health damage. It has also caused the supply chain to be vulnerable to disruption and demand fluctuations. Transformation of the medical device industry to a more circular economy would advance the goal of providing increasingly complex care in a low-emissions future. Barriers to circularity include perceptions regarding infection prevention, behaviors of device consumers and manufacturers, and regulatory structures that encourage the proliferation of disposable medical devices. Complementary policy- and market-driven solutions are needed to encourage systemic transformation.