New Mathematical Model Estimates Fitness of Antiviral-Resistant Influenza Strains


Researchers discover a simple method for estimating fitness of antiviral-resistant influenza strains using surveillance data.

For the first time, researchers have developed a simple method for estimating fitness of antiviral-resistant (AVR) influenza strains using surveillance data; their study was published in The Lancet Infectious Diseases.

Antiviral drugs for influenza, such as oseltamivir, are important for controlling influenza epidemics and pandemics and can help to reduce influenza morbidity and mortality, particularly among high-risk populations. However, the potential for development of strains of influenza virus that are resistant to these drugs continually threatens their effectivity. Viral fitness refers to the ability of a virus to replicate in a particular environment. According to the authors, accurate assessment of AVR fitness is particularly important when it comes to antiviral strategies used during pandemics because AVR could markedly reduce the effectiveness of these strategies in prophylaxis and treatment of severe infections.

With this in mind, Leung and colleagues developed a simple mathematical model to predict the potential of antiviral-resistant influenza strains to outcompete susceptible ones. The method requires only minimal surveillance data on the incidence of resistant and susceptible influenza virus strains, as well as information about the generation interval between successive cases, which is usually well-known for influenza.

In their study, the researchers defined the fitness of AVR influenza strains as the ratio of their reproductive numbers relative to their antiviral-susceptible (AVS) strains. They applied their model to two case studies of pandemic influenza and one hypothetical scenario.

The first case study evaluated the oseltamivir-resistant seasonal influenza A H1N1 strain that emerged in 2007. The mathematical model showed that this strain was 4% more transmissible than the preexisting oseltamivir-sensitive strain. As a consequence, the authors concluded that even a small fitness advantage of 4% is enough for an AVR strain of influenza to spread to fixation, or evolutionary fitness, within months.

The second case study examined the oseltamivir-resistant pandemic influenza A H1N1 strain that occurred in Japan between 2013 and 2014. The researchers showed that this strain was 24% less transmissible than an oseltamivir-sensitive strain that had emerged around the same time and displaced it.

The hypothetical case study considered an influenza pandemic in which AVR and AVS strains are circulating simultaneously. The researchers showed that, under large scale antiviral use during this type of situation, their model can help to guide optimal use of antiviral drugs in real time by monitoring the fitness of the AVR strain and the drug pressure on the AVS strain.

Leung and colleagues emphasize that using robust real-time interpretation of AVR surveillance data to estimate AVR fitness is an important aspect of AVR surveillance that has previously been lacking.

“Our method has the potential to fill this knowledge gap and can be easily integrated into contemporary surveillance systems,” they conclude.

Dr. Parry graduated from the University of Liverpool, England in 1997 and is a board-certified veterinary pathologist. After 13 years working in academia, she founded Midwest Veterinary Pathology, LLC where she now works as a private consultant. She is passionate about veterinary education and serves on the Indiana Veterinary Medical Association’s Continuing Education Committee. She regularly writes continuing education articles for veterinary organizations and journals, and has also served on the American College of Veterinary Pathologists’ Examination Committee and Education Committee.

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