Cases confirmed worldwide by national authorities stand at 823,626 (72,736 of them reported in the preceding 24 hours). 40,598 deaths have been recorded (4193). (Source: WHO Situation Report 72; at 10:00 CET on Wednesday 1 April)
Johns Hopkins University’s Center of Systems Science and Engineering (CSSE) reported (at 14:30 AEDT on Thursday 2 April) 935,817 confirmed cases and 47,231 deaths.
At 06:00 AEDT on Thursday 2 April
Nationwide, confirmed cases stand at 4976, a rise of 264 in 24 hours. 21 deaths have been recorded. More than 261,000 tests have been conducted.
ACT 84 cases (first case reported 12 March); NSW 2298 (25 January); NT 18 (20 March); Qld 781 (29 January); SA 367 (2 February); Tas 68 (2 March); Vic 968 (25 January); WA 392 (21 February).
Contact tracing is a critical tool for epidemiologists seeking to limit the spread of coronavirus, but there’s little they can do to detect asymptomatic infections. Now, a team at the UK’s Cranfield University has published its work on a wastewater-based epidemiology (WBE) approach to predicting the potential spread of COVID-19 – by picking up on biomarkers in faeces and urine from disease carriers that enter the sewer system. WBE is already recognised as an effective way to trace illicit drugs and obtain information on health, disease, and pathogens.
As data accumulate so too do new ways of using it to visualise the pandemic. Animated graphics recently produced by MIT Technology Review using CSSE data are particularly compelling representations of the growth over time in cases and deaths.
New Zealand has released six reports on data modelling of various coronavirus impacts in the country. “It’s critical to understand that each of the models presents a number of potential future scenarios; there are no ‘predictions’” reads the accompanying press release.
Researchers at New York University have published results of a new study aimed at determining whether artificial intelligence (AI) techniques could assist clinicians in accurately predicting which patients with COVID-19 would go on to develop Acute Respiratory Distress Syndrome (ARDS), the fluid build-up in the lungs that can be fatal in the elderly.
Using demographic, laboratory and radiological data collected from 53 COVID-19 patients at two Chinese hospitals, the researchers designed computer models that make data-driven decisions, with programs getting “smarter” the more data they consider.
Unexpectedly, the AI tool has found that changes in three markers – levels of the liver enzyme alanine aminotransferase (ALT), reported myalgia (muscle pain), and haemoglobin levels – were the most accurate predictors of subsequent severe disease. Together with other factors, the team reports being able to predict ARDS risk with up to 80% accuracy.
A world reimagining and reshaping itself provides rich fodder for new ways of doing things, and with everyone locked out of the usual places to socialise the concept of virtual dating seems timely.
Would you consider volunteering for a COVID-19 vaccine if and when testing starts? You should probably meet a young American who’s signed up for trials in the US’s Pacific Northwest.
It seems that streets deserted because of coronavirus lockdown in the Welsh village of Llandudno have proved inspirational for a wild herd of Kashmiri goats.