Combining genetic and experimental data into models about the influenza virus can help predict more accurately which strains will be most common during the next winter, says a study published recently in eLife.
The models could make the design of flu vaccines more accurate, providing fuller protection against a virus that causes around half a million deaths each year globally.
Vaccines are the best protection we have against the flu. But the virus changes its appearance to our immune system every year, requiring researchers to update the vaccine to match. Since a new vaccine takes almost a year to make, flu researchers must predict which flu viruses look the most like the viruses of the future.
The gold-standard ways of studying influenza involve laboratory experiments looking at a key molecule that coats the virus called haemagglutinin. But these methods are labour-intensive and take a long time. Researchers have focused instead on using computers to predict how the flu virus will evolve from the genetic sequence of haemagglutinin alone, but these data only give part of the picture.
“The influenza research community has long recognised the importance of taking into account physical characteristics of the flu virus, such as how haemagglutinin changes over time, as well as genetic information,” explains lead author John Huddleston, a PhD student in the Bedford Lab at Fred Hutchinson Cancer Research Center and Molecular and Cell Biology Program at the University of Washington, Seattle, US. “We wanted to see whether combining genetic sequence-only models of influenza evolution with other high-quality experimental measurements could improve the forecasting of the new strains of flu that will emerge one year down the line.”
Huddleston and the team looked at different components of virus ‘fitness’ — that is, how likely the virus is to thrive and continue to evolve.
Northwell Health makes source code available for health systems to create localized COVID-19 surveillance dashboard
Northwell Health today announced that it has developed a first-of-its-kind predictive tool that can anticipate a spike in coronavirus disease 2019 (COVID-19) cases at its hospitals by mining user data patterns from its Northwell.edu website. With the pandemic still raging worldwide, Northwell plans to give away the source code to other health systems.
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Northwell Health frontline staff will have advance warning of the next COVID-19 surge thanks to its new predictive dashboard. (Credit: Northwell Health)
The two-week advance warning system was created this summer by the customer insights group in collaboration with information technology and clinical teams in the wake of the COVID-19 surge that struck New York State’s largest health system last spring. Northwell Health treated nearly 85,000 COVID-19 patients, including 16,000 hospitalized patients between March and Labor Day – more than any health system in the United States.
Northwell’s new digital dashboard collects 15 different indicators from the Northwell.edu website and feeds them into a machine learning algorithm to recognize patterns in website traffic, which includes everything from emergency department wait time searches to physician page clicks. The result is a rolling two-week forecast that has closely mirrored caseload to date and is expected to help clinical teams prepare for future surges.
“You’re feeling sick, start doing the research online, but you aren’t so ill that you need to see a doctor immediately,” said Ramon Soto, senior vice president and chief marketing and communications officer for Northwell Health. “We found a very strong relationship with website activity to in-facility COVID-19 patients two weeks later. This is an important development in the fight against COVID-19 and Northwell wants to share this game-changing tool