The coronavirus pandemic is disrupting universities and research institutes across the world. But the same institutions are also working very hard to find out how the disease can be stopped and its effects mitigated.
Follow this live blog for the latest updates on how the crisis is impacting research and innovation, and what governments, funders, companies, universities, associations and scientists are doing to stop or cope with the pandemic.
A new computer-aided diagnostic tool could help overcome some of the challenges of monitoring lung health following infection with COVID-19.
In common with other respiratory illnesses, COVID-19 can cause lasting harm to the lungs. However, it is hard to visualise this damage because conventional chest scans do not reliably detect signs of lung scarring and other pulmonary abnormalities. That is making it difficult to track the health and recovery of people with persistent breathing problems and other post-COVID complications.
The new method developed by researchers in China and at the King Abdullah University of Science and Technology, Saudi Arabia, overlays artificial intelligence algorithms on top of standard chest imaging data to reveal otherwise indiscernible visual features indicative of lung dysfunction.
As a result, radiologists can identify and analyse novel sub-visual lung lesions,” said computer scientist and computational biologist Xin Gao. “Analysis of these lesions could then help explain patients’ respiratory symptoms,” allowing for better disease management and treatment, he said.
The method first eliminates any anatomical features not associated with the lung parenchyma; the tissues involved in gas exchange that are the main sites of COVID-19–induced damage. That means removing airways and blood vessels, and then enhancing the pictures of what is left behind to expose lesions that might be missed.
The researchers trained and validated their algorithms using computed tomography (CT) chest scans from thousands of people hospitalised with COVID-19 in China.
Gao and colleagues demonstrated the tool could reveal signs of pulmonary fibrosis in people with Long COVID, thus helping to account for shortness of breath, coughing and other lung problems. He says this diagnosis would be impossible with standard CT image analytics.
Football matches that went ahead during the second COVID-19 wave in Germany were linked to local increases in the number of infections, despite the outdoor setting, reduced stadium occupancy and social distancing, a new study suggests.
The researchers found that local COVID-19 incidence on match days played a key role in subsequent infection levels.
Kai Fischer of the University of Düsseldorf compared counties in Germany where football matches took place with counties without matches between August and November 2020, and then looked at how infection rates evolved over time in these counties.
He found that, on average, just one additional football match in a county led to 0.34 - 0.71 additional cases per 100,000 people three weeks later. This might not sound like much, but when extrapolated to the 7-day incidence per 100,000 people, it is an increase of approximately 3-7% for just one match.
During this period, the authorities restricted the number of people who could attend matches, capping stadium occupancy levels at approximately 20%. Harsher occupancy restrictions were imposed when local weekly case numbers exceeded 35 cases per 100,000 inhabitants. Hygiene and social distancing rules also varied, with top league matches imposing stricter regulations.
Infection levels following a match were strongly linked to the local incidence of COVID-19 on the day of the match. In fact, there were very few infections after matches when the local weekly incidence was under 25 per 100,000 people.
The study used smartphone data to show that large increases in mobility occurred on match days, leading to more human interaction, and proposes that this is a possible underlying mechanism for the phenomenon.
Researchers at the University of Alicante who are studying the global spread of the COVID-19 epidemic have published new data showing the SARS-CoV-2 virus entered the US one month earlier than the official data.
Their analysis, conducted with the University of Pennsylvania, shows that the virus likely entered through California on 28 December 2019. That is 16 days before the officially recognised entry date set by the Centres for Disease Control and Prevention, and 3 days before the first outbreak was reported by authorities in Wuhan, China.
In addition, the study provides evidence that SARS-CoV-2 on average entered each US state a month earlier than previously reflected in official data.
The data were obtained using the Retrospective Methodology to Estimate Daily Infections from Deaths methodology, which the researchers say provides more accurate estimates of the initial cases of COVID-19 in the US and has the capacity to be extrapolated to other countries to retrospectively follow the progress of the pandemic.
Between November 2020 and May 2021, adherence to COVID-19 pandemic restrictions decreased in Italy, with the fastest decreases taking place during times of the most stringent restrictions, according to a new study.
Pandemic fatigue, characterised as lower motivation to adhere to social distancing measures and adopt health protective behaviours, is a significant concern for policymakers and health officials.
From November 2020 to May 2021 in Italy, tiered restrictions were adopted to reduce the spread of COVID-19, with regions declared red, orange, yellow or white depending on the level of infection. Restrictions ranged from a night time curfew in the yellow tier to general stay-at-home mandates in the red tier.
In the new study, the researchers used large scale mobility data from Facebook and Google captured in all 20 Italian provinces to analyse the timing of pandemic fatigue. Facebook reports the change in a user’s number of movements over time, while Google data estimates the change in time spent at home.
People’s relative change in movements increased an average of 0.08% per day and time spent outside the home increased by an average 0.04% per day, leading to a more than 15% increase in relative mobility over the seven-month study period.
During times of red tier restrictions, individual mobility increased an additional 0.16% per day and time spent outside the home increased an additional 0.04% when compared to the average. This means for every 2 week period spent in the red tier, there was an additional average 3% increase in relative mobility.
The authors conclude that changes in adherence to pandemic restrictions are faster during periods characterised by the strictest levels of restrictions. Given that milder tiers have been proven to be effective in mitigating the spread of COVID-19, the researchers suggest policymakers should consider the interplay between the efficacy of restrictions and their sustainability over time.
One of the first studies to document the impact of COVID-19 on already existing viruses in Australia has shown the pandemic was responsible for creating a huge change in the incidence and genetics of Respiratory Syncytial Virus (RSV) in the country.
RSV is a common virus that generally causes mild, cold like symptoms but the infection can be serious for infants and older adults.
The researchers say the pandemic disrupted the seasonal pattern of RSV, which is one of the regular ‘winter viruses’. For the first time on record, in 2020 there was no winter RSV epidemic, which is attributed to COVID-19 travel restrictions and infection control measures.
However, RSV was one of the first of the key respiratory pathogens to re-emerge after COVID-19.
The researchers genetically sequenced major outbreaks of RSV occurring out of season over the summer of 2020-21 on both sides of the country. These outbreaks coincided with the easing of COVID-19 control measures.
They found there had been a major collapse in RSV strains known before COVID-19, and the emergence of new RSV strains. These new strains dominated each outbreak in Western Australia, New South Wales and the Australian Capital Territory.
The researchers then tracked the seeding of viruses from each outbreak into Victoria, which led to another major RSV outbreak.
“Our genetic studies showed that most of the previous RSV strains had gone ‘extinct’ and that for each outbreak only a single genetic lineage had survived all the lockdowns,” said lead researcher John-Sebastian Eden, senior research fellow at the University of Sydney Institute for Infectious Diseases.
The study raises important questions as to how rapid spread and evolution of RSV could inform the re-emergence of other viruses including influenza.
“The constellation of flu strains circulating pre and post-COVID-19 has also changed a lot, leading to challenges in how we choose the composition and timing of our annual vaccines. For example, the flu season in Australia has kicked off much earlier than in previous years.” said Eden.
There is currently no approved RSV vaccine, but it is a major focus for vaccine and therapeutic development.
“We need to be vigilant – some viruses may have all but disappeared, but will likely rebound in the near future, possibly at unusual times and with stronger impact,” Eden said. “We need to be prepared for large outbreaks of RSV outside of normal seasonal periods.”
Before COVID-19, two major RSV subtypes, A and B, co-circulated at similar levels.
During late 2020 to early 2021 during the outbreak periods, this changed dramatically. The RSV-A subtype was found to be the dominant strain – making up more than 95% of cases in all the states. The RSV-B had all but disappeared.
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