The Numbers

Global

Cases confirmed worldwide by national authorities stand at 2,471,136 (73,930 of them reported in the preceding 24 hours). 169,006 deaths have been recorded (6058). (Source: WHO Situation Report 93; at 10:00 CET on Wednesday 22 April)

Johns Hopkins University’s Center of Systems Science and Engineering (CSSE) reported (at 15:00 AEST on Thursday 23 April) 2,628,916 confirmed cases and 183,441 deaths.

Australia

At 06:00 AEST on Thursday 23 April

Nationwide, confirmed cases stand at 6654, a rise of 7 in 24 hours. 74 deaths have been recorded. More than 458,000 tests have been conducted.

The state by state

State     Total cases          First case reported         

ACT        104                         12 March

NSW      2976                       25 January

NT          27                           20 March

Qld         1024                       29 January

SA           438                         2 February

Tas.        203                         2 March

Vic.         1336                       25 January

WA         546                         21 February

Science

Ethicists Alex London, from Carnegie Mellon University, US, and Jonathan Kimmelman, from McGill University, Canada, are calling on the global research community to guard against using the COVID-19 outbreak as grounds for making exceptions to rigorous research standards in pursuit of treatments and vaccines.

The duo caution that urgency should not be used as an excuse for lowering scientific standards. They argue that many of the deficiencies in the way medical research is conducted under normal circumstances seem to be amplified in this pandemic.

Their paper, just published in the journal Science, provides recommendations for conducting clinical research during times of crises.

“Although crises present major logistical and practical challenges, the moral mission of research remains the same: to reduce uncertainty and enable care givers, health systems and policy makers to better address individual and public health,” say London and Kimmelman.

“Rigorous research practices can’t eliminate all uncertainty from medicine, but they can represent the most efficient way to clarify the causal relationships clinicians hope to exploit in decisions with momentous consequences for patients and health systems.”

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As the creatures thought most likely to be the source of the SARS-CoV-2 coronavirus, bats haven’t been getting the best press of late. Actually, bats do a lot of good for the world – they pollinate plants, eat disease-carrying insects, and help disperse seeds that help with the regeneration of tropical forest trees.

They’re also among the range mammal groups that are natural carriers of coronaviruses.

To better understand this very diverse family of viruses, which includes SARS-CoV-2, a scientific team compared the different kinds of coronaviruses living in 36 bat species from the western Indian Ocean and nearby areas of Africa. Their findings are published in a new paper in Scientific Reports.

“We found that there’s a deep evolutionary history between bats and coronaviruses,” says Steve Goodman, based at Chicago’s Field Museum, US.

“Developing a better understanding of how coronaviruses evolved can help us build public health programs in the future.”

A lot of people use “coronavirus” as a synonym for the specific virus causing the current pandemic, but there are a vast number of different coronavirus types, potentially as many as bat species, and most of them pose no known threat to humans.

The study examines the genetic relationships between different strains of coronaviruses and the animals they live in, which sets the stage for a better understanding of the transfer of viruses from animals to humans.

Data

Research just published in Nature Medicine reveals a new model that predicts the course of the COVID-19 pandemic in Italy using data from the outbreak.

The new model, which considers eight stages of infection and differentiates between diagnosed and non-diagnosed people, could provide policymakers in Italy and elsewhere with a tool to assess the consequences of possible strategies, including lockdown and social distancing, as well as testing and contact tracing.

The study shows that the adopted social-distancing measures are necessary and effective and should be promptly enforced at the earliest stage. Lockdown measures can be relieved safely only in the presence of widespread testing and contact tracing, the findings suggest.

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Researchers searching for a vaccine, drug, or combination of treatments that can disrupt the SARS-CoV-2 virus, which causes the COVID-19 disease, could rapidly identify the small number of already-approved drugs that may be promising.

But screening every possible small molecule that might interact with the virus – and the proteins that control its behaviour – to disrupt its activity is another matter.

The problem is, says a report from the University of Texas’s Texas Advanced Computing Center (TACC), that there are more than a billion such molecules, and researchers would conceivably want to test each one against the two dozen or so proteins in SARS-CoV-2 to see their effects. Such a project could use every wet lab in the world and still not be completed for centuries.

One of the computer modelling efforts aimed at this challenge is large collaborative effort led by researchers at US Department of Energy’s Argonne National Laboratory, which combines artificial intelligence with physics-based drug docking and molecular dynamics simulations to rapidly narrow the list of most promising molecules to test in the lab.

This approach has turned the challenge into a data, or machine-learning-oriented, problem, says Argonne computational biologist Arvind Ramanathan.

“We’re trying to build infrastructure to integrate AI and machine learning tools with physics-based tools. We bridge those two approaches to get a better bang for the buck.”

The project is using several of the world’s most powerful supercomputers to run millions of simulations, train the machine learning system to identify the factors that might make a given molecule a good candidate, and then do further explorations on the most promising results.

In the first week, the team tested six million molecules.

Though AI is frequently considered a “black box”, Ramanathan says their methods do not just blindly generate a list of targets. They deduce what common aspects of a protein make it a better candidate, and communicate those insights to researchers to help them understand what is actually happening in the virus with and without drug interactions.

Share Index

As the world has turned online the range of digital amusements has grown algorithmically from giggle-worthy through alarming into a market of diversions ranging from the merely childish to the genuinely grotesque. Where does the Yorkshire Museum’s weekly “curator battles” fit? YOU be the judge…

Reporting from the New York Times – smack in the middle if the world COVID-19 epicentre – is worthy of attention. This week an opinion piece by emergency room doctor Richard Levitan highlighted the deceptive dangers of COVID pneumonia.

Elsewhere in the land of the free, the very idea of freedom may be accelerating the spread of COVID-19. In America’s southern states, to a report in The Economist, “the virus is spreading throughout the region with unnerving speed. Between the end of March and April 20th, the number of cases in the dozen southern states rose from 11,700 to 127,500, three times as fast as the national increase.”