In closely watched developments regarding the highly mutated BA.2.86 SARS-CoV-2 variant, another sequence from the United States was uploaded to a public database, raising the number identified globally to seven.

On Twitter, Rajendram Rajnarayanan, MSc, PhD, a computational biologist with the New York Institute of Technology, said the sequence from Virginia was collected on August 10 as part of the Centers for Disease Control and Prevention (CDC) Traveler Based Genomic Surveillance Program.

The patient had traveled from Japan and was asymptomatic.

So far, the virus—which has more than 30 mutations—has been found in Israel, Denmark, the United Kingdom, and the United States. Denmark has reported three of the sequences, and today the country's Statens Serum Institute said that, of its latest batch of 53 sequence uploads, none were BA.2.86. It added that it will upload the next batch of sequences on August 25.

The first US sequence was reported last week in a patient in Michigan.

The World Health Organization (WHO) last week added BA.2.86 as a variant under monitoring due to its many mutations and because it has been detected in multiple world regions. So far, there are no indications that BA.2.86 causes more severe disease, and scientists are working to characterize the virus, including if it has immune-escape properties.

The US Department of Health and Human Services (HHS), through its Administration for Strategic Preparedness and Response (ASPR) announced recipients for the first round of funding through the White House's $5 billion Project NextGen program. First announced in April, Project NextGen is designed to speed the development of new COVID-19 vaccines and treatments.

In a news release, HHS said the awards announced today are geared toward developing more effective and longer lasting vaccines, a new monoclonal antibody, and technologies to streamline the manufacturing process.

HHS Secretary Xavier Beccera said Project NextGen is a key part of the Biden-Harris administration's plan to keep people safe from COVID-19 variants. "These awards are a catalyst for the program—kickstarting efforts to more quickly develop vaccines and continue to ensure availability of effective treatments."

$1 billion to support phase 2b trials

Of the funding announced today, $1 billion goes to four Biomedical Advanced Research and Development Authority (BARDA) research partners to support vaccine phase 2b clinical trials. They include ICON Government and Public Health Solutions in Hinckley, Ohio, Pharm-Olam, LLC, of Houston, Tex., Technical Resources Intl (TRI), Inc, of Bethesda, Md., Rho Federal Systems, Inc., in Durham, NC.

HHS said the funding will help speed the development of new vaccine candidates, supported for at-the-ready trials designed to focus on the most promising candidates. "The public can expect to see clinical trials for new vaccine candidates targeting longer-lasting protection against future variants as early as this winter under Project NextGen," the HHS said.

Also, the awards include $326 million to Regeneron the develop a next-generation monoclonal antibody, $100 million to Global Health Investment Corp, which manages the BARDA Ventures investment portfolio, and $10 million to Johnson & Johnson Innovation (JLABS) for a competition through Blue Knight, a BARDA-JLABS partnership.

HHS said it expects to announce more awards by the end of the year.

Experts have been making a strong case for better COVID vaccines, including ones that can curb transmission. In February, a group led by the Center for Infectious Disease Research and Policy (CIDRAP), which publishes CIDRAP News, published a roadmap for advancing better vaccines against coronaviruses.

University of Chicago researchers report that they have built a deep-learning (artificial intelligence [AI]) model that can evaluate chest x-rays (CXRs) to predict whether COVID-19 patients will need intensive care, aiding triage and hospital resource allocation during patient surges.

For the study, published yesterday in the Journal of Medical Imaging, the researchers refined a large model that had been trained on ImageNet with 1.2 million CXRs from a National Institutes of Health dataset to detect 14 diseases. Then they fine-tuned the model using data from the Radiological Society of North America to detect pneumonia and finalized it with an in-house dataset of 6,685 CXRs from 3,998 patients with COVID-19.

Fair degree of accuracy

Based only on images, the resulting model could predict whether a patient would require intensive care (intubation or intensive care unit [ICU] admission) within 24, 48, 72, and 96 hours with a fair degree of accuracy.

In an independent in-house test set of 1,672 CXRs from 1,048 patients, the model achieved an area under the receiver operating characteristic curve (AUC) of 0.78 when predicting the need for intensive care within 24 hours and at least 0.76 within 48 or hours or more. Patients identified as high risk were nearly five times as likely to need intensive care as those deemed low risk.

The model's performance was similar to that of existing models that rely on both images and clinical data.

The team acknowledged that the model generated false predictions, which they said could be due to the influence of irrelevant CXR regions or the presence of underlying nonpulmonary diseases that contributed to patients' deteriorating health and ICU admission. They said that the model's performance could be improved by incorporating lung region segmentation, cropping in the model, and clinical variables and lab results.

"Our findings show the promise of AI-assisted medical imaging analysis in COVID-19 prognostic task, which bear the potential to play an important role in clinical decision-making, especially in situations of limited resources," the authors wrote.

Yesterday JAMA Internal Medicine published a study of more than 200,000 US veterans infected with COVID-19 demonstrating that the excess mortality risk from acute infection diminished within 6 months of initial diagnosis.

The study was based on health record data from 208,061 patients seen at Veterans Affairs hospitals for initial COVID-19 infections from March 2020 to April 2021. Mortality outcomes among case-patients were compared to 1,037,423 matched uninfected peers.

Of the veterans with COVID-19, 46,902 (22.5%) were hospitalized within 7 days of their infection; 12,626 (1.2%) comparators were also hospitalized.

Initial death rate almost 9%

Though the unadjusted mortality rate for veterans with COVID was 8.7% during the 2-year period after the initial infection, compared with 4.1% among uninfected comparators, adjusted analyses showed the hazard ratio for death dropped by 180 days post-infection, or 6 months.

The risk of excess death was highest on days 0 to 90 after infection (adjusted hazard of death ratio [aHR], 6.36; 95% confidence interval [CI], 6.20 to 6.51) and declined on days 91 to 180 (aHR, 1.18; 95% CI, 1.12 to 1.23).

"Those who survived COVID-19 had lower mortality (greater survival rates) on days 181 to 365 (aHR, 0.92; 95% CI, 0.89-0.95), and 366 to 730 (aHR, 0.89; 95% CI, 0.85-0.92) than their matched comparators," the authors wrote. "The mortality rate by day 730 was 0.81% among Veterans who survived COVID-19 through day 365, in contrast with 0.94% among their comparators in the same interval."

The authors said this study adds complexity to the understanding of how COVID-19 affects long-term health.

"These results may suggest that for ongoing care after COVID-19, especially among those not initially hospitalized for COVID-19, efforts that focus on increasing survival may be less relevant than approaches addressing the needs of individual survivors," the authors concluded.