Résumé
Smart homecare utilises advanced technologies to support, improve and promote remote healthcare in homes and communities through collecting and analysing health data and sharing this knowledge with carers and clinicians. With the continuous growth in the world’s older population, smart homecare becomes increasingly crucial in providing in-home care for older adults, allowing the vital healthcare dollars to go further into other critical care needs. In addition, with the rise in the development and utilisation of innovative technologies in healthcare settings, it is vital to ensure that these technologies are guided and approved by the corresponding regulatory bodies such as FDA (Foods and Drug Administration) in the USA and TGA (Therapeutic Good Administration) in Australia. With this premise, this paper identifies four dimensions for researchers to consider when developing smart homecare solutions for in-home remote care: Technology, Data, People, and Operational Environment. The essential interplays amongst these four dimensions are discussed to identify the various enablers and barriers in the successful delivery of smart homecare solutions. As the primary output of this paper, it proposes a conceptual framework to achieve practical in-home care for the older population living independently with the support of technology, while addressing the challenges such as security and privacy of patient data. Secondly, a comprehensive and practical guide featuring seven phases is presented to support and direct researchers in implementing smart homecare solutions for remote care. The proposed framework and the guide aim to make smart homecare research practical and truly translational into broader practice. Author
Résumé
Laboratory experiments have revealed the meteorological sensitivity of the coronavirus disease 2019 (COVID-19) virus. However, no consensus has been reached about how outdoor meteorological conditions modulate the virus transmission as it is also constrained by non-meteorological conditions. Here, we identify the outbreak's evolution stage, constrained least by non-meteorological conditions, by searching the maximum correlation coefficient between the ultraviolet flux and the growth rate of cumulative confirmed cases at the country level. At this least-constrained stage, the cumulative cases count around 1300-3200, and the count's daily growth rate correlates with the ultraviolet flux and temperature significantly (correlation coefficients r = -0.54 +/- 0.09 and -0.39 +/- 0.10 at p<0.01$$ p, respectively), but not with precipitation, humidity, and wind. The ultraviolet correlation exhibits a delay of about 7 days, providing a meteorological measure of the incubation period. Our work reveals a seasonality of COVID-19 and a high risk of a pandemic resurgence in winter, implying a need for seasonal adaption in public policies.