PubMed COVID-19 Clinical Care

In response to unprecedented surges in the demand for hospital care during the SARS-CoV-2 pandemic, health systems have prioritized patients with COVID-19 to life-saving hospital care to the detriment of other patients. In contrast to these ad hoc

We analyze a plurality of epidemiological models through the lens of physics-informed neural networks (PINNs) that enable us to identify time-dependent parameters and data-driven fractional differential operators. In particular, we consider several

CONCLUSIONS: The VRP appeared to be a measure with substantial impact on well-being for some, mostly because social structures were disrupted. The measure showed fluctuating compliance, in which feasibility and frequent changes in the VRP played a

In this original research we present new emerging data in COVID-19 that create urgent challenges for health providers in prevention and treatment. Health providers should be aware that COVID-19 cases, hospitalizations, and deaths have increased

Age-related heterogeneity in a host population, whether due to how individuals mix and contact each other, the nature of host-pathogen interactions defining epidemiological parameters, or demographics, is crucial in studying infectious disease

BACKGROUND: There is substantial evidence that patients with COVID-19 were treated with sustained deep sedation during the pandemic. However, it is unknown whether such guideline-discordant care had spillover effects to patients without COVID-19.

CONCLUSIONS: During the COVID-19 pandemic, individuals were required to stay home due to varying levels of lockdown protocols and provide their usual services using online platforms both increasing sedentary behaviour in computer based workers and

Previous studies indicate that COVID-19 confinement has led to an increase in psychological distress and a decrease in overall well-being. This longitudinal study aims to investigate how stress mindset and personality traits moderate the impact of

The roles of climate and true seasonal signatures in the epidemiology of emergent pathogens, and that of SARS-CoV-2 in particular, remain poorly understood. With a statistical method designed to detect transitory associations, we show, for COVID-19

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