Early in the COVID-19 pandemic, the World Health Organization stressed the importance of daily clinical assessments of infected patients, yet current approaches frequently consider cross-sectional timepoints, cumulative summary measures, or time-to-event analyses. Statistical methods are available that make use of the rich information content of longitudinal assessments. We demonstrate the use of a multistate transition model to assess the dynamic nature of COVID-19-associated critical illness using daily evaluations of COVID-19 patients from 9 academic hospitals. We describe the accessibility and utility of methods that consider the clinical trajectory of critically ill COVID-19 patients.

In this paper we undertake a quantitative analysis of the dynamic process by which ice underneath a dry porous debris layer melts. We show that the incorporation of debris-layer airflow into a theoretical model of glacial melting can capture the empirically observed features of the so-called Østrem curve (a plot of the melt rate as a function of debris depth). Specifically, we show that the turning point in the Østrem curve can be caused by two distinct mechanisms: the increase in the proportion of ice that is debris-covered and/or a reduction in the evaporative heat flux as the debris layer thickens. This second effect causes an increased melt rate because the reduction in (latent) energy used for evaporation increases the amount of energy available for melting. Our model provides an explicit prediction for the melt rate and the temperature distribution within the debris layer, and provides insight into the relative importance of the two effects responsible for the maximum in the Østrem curve. We use the data of Nicholson and Benn (2006) to show that our model is consistent with existing empirical measurements.

The redshifted 21cm line of neutral hydrogen (Hi), potentially observable at low radio frequencies (~50–200 MHz), should be a powerful probe of the physical conditions of the inter-galactic medium during Cosmic Dawn and the Epoch of Reionisation (EoR). The sky-averaged Hi signal is expected to be extremely weak (~100 mK) in comparison to the foreground of up to 104 K at the lowest frequencies of interest. The detection of such a weak signal requires an extremely stable, well characterised system and a good understanding of the foregrounds. Development of a nearly perfectly (~mK accuracy) calibrated total power radiometer system is essential for this type of experiment. We present the BIGHORNS (Broadband Instrument for Global HydrOgen ReioNisation Signal) experiment which was designed and built to detect the sky-averaged Hi signal from the EoR at low radio frequencies. The BIGHORNS system is a mobile total power radiometer, which can be deployed in any remote location in order to collect radio frequency interference (RFI) free data. The system was deployed in remote, radio quiet locations in Western Australia and low RFI sky data have been collected. We present a description of the system, its characteristics, details of data analysis, and calibration. We have identified multiple challenges to achieving the required measurement precision, which triggered two major improvements for the future system.