Biomarker identification using dynamic time warping analysis: a longitudinal cohort study of patients with COVID-19 in a UK tertiary hospital.

Objectives: COVID-19 is a heterogeneous disease, and many reports have described variations in demographic, biochemical and clinical features at presentation influencing overall hospital mortality. However, there is little information regarding longitudinal changes in laboratory prognostic variables in relation to disease progression in hospitalised patients with COVID-19.

Design And Setting: This retrospective observational report describes disease progression from symptom onset, to admission to hospital, clinical response and discharge/death among patients with COVID-19 at a tertiary centre in South East England.

The impacts of COVID-19 containment on the Australian economy and its agricultural and mining industries.

We simulate the economic impacts of the COVID-19 pandemic on the Australian economy using VURM, a detailed computable general equilibrium model for Australia. We identify five sources of economic perturbations: changes to productivity due to changing work practices, changes in household demand imposed by voluntary and mandated social distancing behaviour, changes in international trade due to a weakened world economy and severe curtailment of international travel, reduced population growth due to lower net migration and large debt-financed fiscal stimulus. Variants of these shocks and associated recovery paths are simulated in VURM, with three scenarios describing potential recovery arcs.

Research Evaluation Alongside Clinical Treatment in COVID-19 (REACT COVID-19): an observational and biobanking study.

Introduction: The COVID-19 pandemic caused by SARS-CoV-2 places immense worldwide demand on healthcare services. Earlier identification of patients at risk of severe disease may allow intervention with experimental targeted treatments, mitigating the course of their disease and reducing critical care service demand.

Methods And Analysis: This prospective observational study of patients tested or treated for SARS-CoV-2, who are under the care of the tertiary University Hospital Southampton NHS Foundation Trust (UHSFT), captured data from admission to discharge; data collection commenced on 7 March 2020.

Inflammatory phenotyping predicts clinical outcome in COVID-19.

Background: The COVID-19 pandemic has led to more than 760,000 deaths worldwide (correct as of 16th August 2020). Studies suggest a hyperinflammatory response is a major cause of disease severity and death. Identitfying COVID-19 patients with hyperinflammation may identify subgroups who could benefit from targeted immunomodulatory treatments.