UNIVERSITY PARK, Pa. – It is difficult to accurately assess a population’s exposure to a particular virus because the tools to do so do not take into account the fact that many viruses include several circulating strains, or the fact that people can be vaccinated or naturally immunized, among other factors. Using influenza as a model, a team of researchers led by Penn State developed a new technique that overcomes many of these hurdles, and they say the tool can be useful for better assessments of exposure to a variety of viruses, including those that cause COVID-19 and pneumonia.
“Without an accurate picture of a population’s exposure to a particular virus, we cannot effectively plan and implement public health interventions,” said Maciej Boni, associate professor of biology, who led the study.
In their study, published Nov. 18 in the journal Nature Communications, the researchers specifically studied the attack rate of the human influenza A virus in a tropical setting, which includes two subtypes – H3N2 and H1N1 – and several strains. .
According to Boni, an attack rate is an estimate of the number of people infected with a particular disease, whether or not they show symptoms or have been tested or not.
“Accurately estimating the attack rate of a virus sounds like something epidemiologists should be able to do fairly easily,” he said, “but there are at least four major complications. First of all, you need to plan blood sample collections in advance, otherwise there is no way to get a snapshot of who in the population currently has antibodies and who does not. Second, when testing for antibodies, you cannot always tell the difference between an infected person and a vaccinated person. Third, the antibodies go down, so you might not be able to tell if a person has been infected if their antibody levels are low now. Finally, many pathogens circulate as groups of strains or groups of variants, and there may be no laboratory tests to test systemic exposure to any of these variants.
The team, which included researchers from Vietnam and the Netherlands, created a new method that addresses many of these problems and presents accurate estimates of the attack rate of the general population for the influenza A virus. To conduct their study, the researchers used a data set of 24,402 serum or blood plasma samples from the general population collected in central and southern Vietnam between 2009 and 2015 and analyzed the samples for antibodies to eleven different strains of human influenza A, including the 1918 pandemic strain “Spanish flu” and the pandemic virus strain “swine flu” 2009, which belong to the H1N1 subtype. Then they used this large set of antibody measurements to derive a “composite antibody measurement” across the two influenza subtypes and across all of the different strains.
“This composite measure allowed us to more accurately estimate who had been infected with a strain and who had not,” Boni said. “This gives us a general picture of the disease burden of influenza in the tropical environment of southern and central Vietnam.”
The team’s results suggest that on average, 26% of the Vietnamese population is exposed to the H3N2 subtype of influenza each year, and 16% are exposed to the H1N1 subtype. These exposure rates, Boni said, are a bit higher than expected level of influenza exposure in temperate countries, which all have winter flu seasons, but data from temperate countries has yet to come. been analyzed with this new approach.
“The future of antibody testing will be to evolve into technologically advanced platforms that can test multiple types of antibodies in a single blood sample,” said Marion Koopmans, co-lead author of the study and Head of the Viroscience Department at the Erasmus Medical Center. in the Nederlands. “With a small team, we were able to generate 270,000 antibody measurements in just two years. This is proof that this approach could work on a larger scale.
The key synergy that allowed this study to move forward, the team said, was the combination of large pre-planned serum collections, which began in 2009, and a microarray platform. high-throughput that has allowed large-scale testing of many different types of influenza antibodies.
“The pre-planning of the serum collections and the creation of a large structured serum bank were the key to the success of this study,” added Guy Thwaites, director of the clinical research unit at the University of Oxford (OUCRU) in Ho Chi Minh City, Vietnam. “The serum bank that made this work possible also allowed us to do rapid assessments of anti-H7N9 antibodies to avian influenza during the H7N9 epidemics of April 2013 in China, as well as estimates of vaccine coverage against tetanus, chikungunya virus circulation history and more. “
According to Boni, the next key steps in this “big data” approach to antibody testing is to understand the dynamics of immune decline.
“Our study population had virtually no influenza vaccines,” Boni said, “so we were able to use our antibody measurements as real indicators of past influenza infection, but we still need better. understand how to distinguish infected from vaccinated individuals and how to include the effects of decreased antibodies in an assay like this.
Other authors of the article include Dao Nguyen Vinh, Nguyen Thi Duy Nhat, Nguyen Ha Thao Vy, Tran Thi Nhu Thao, Huynh Thi Phuong, Pham Hong Anh, Tran Minh Quan, Nguyen Thi Le Thanh and Nguyen Van Vinh Chau, University of Oxford Clinical Research Unit, Wellcome Trust Major Overseas Program; Marc Choisy, Guy E. Thwaites and Hannah E. Clapham, University of Oxford Clinical Research Unit, Wellcome Trust Overseas Major Program and University of Oxford; Erwin de Bruin, Erasmus Medical Center; Stacy Todd, Liverpool School of Tropical Medicine; Nguyen Thi Nam Lien, Hue Provincial Hospital; Nguyen Thi Hong Ha, Khanh Hoa Provincial Hospital; Tran Thi Kim Hong, Dak Lak General Hospital; Pham Quang Thai, National Institute of Hygiene and Epidemiology, Hanoi, Vietnam; Tran Dang Nguyen, State of Pennsylvania; and Cameron P. Simmons, Monash University.
The Wellcome Trust; British Medical Association; Australian National Board of Health and Medical Research; and the Dutch Ministry of Economic Affairs, Agriculture and Innovation supported this research.