April 14, 2024

New Study Finds Flu Interactions More Important than Virus Evolution in Predicting Epidemic Severity

Researchers have recently published a study in eLife that sheds new light on the factors contributing to the severity of seasonal flu epidemics. The study explores the relationships between viral evolution, population immunity, and the co-circulation of other flu viruses in shaping the spread of the flu.

The researchers used a comprehensive set of data sources to provide compelling evidence on the roles of genetic distance, influenza subtype dynamics, and epidemiological indicators in predicting flu epidemics. The findings highlight the significance of genetic distance and subtype interference in determining the timing and severity of annual flu outbreaks.

Influenza viruses undergo genetic changes in proteins called haemagglutinin (HA) and neuraminidase (NA) through a process known as antigenic drift. This allows the virus to evade immune recognition in individuals who have been vaccinated or previously infected, making them susceptible to re-infection.

Influenza A, particularly subtype A(H3N2), has the fastest rates of antigenic drift and causes the most severe illness and death among the two types of influenza viruses that routinely circulate in humans.

The lead author of the study, Amanda Perofsky, explains that antigenic drift should theoretically result in more susceptible individuals, leading to larger or more severe flu epidemics. However, previous evidence on the impact of antigenic drift on flu epidemics has been mixed.

Surveillance efforts to monitor and predict influenza evolution have primarily focused on the genetic changes in HA, while there has been less emphasis on NA. Yet, antibodies against NA also influence infection severity and correlate with immunity. Additionally, little is known about how different co-circulating influenza subtypes interact with each other and affect the size of outbreaks.

To address these gaps in knowledge, Perofsky and her colleagues tracked the evolutionary dynamics of A(H3N2) viruses using genetic sequences and serological assays. They linked this data to epidemiological flu surveillance data over 22 influenza seasons prior to the COVID-19 pandemic.

The researchers measured the genetic and antigenic distances between viruses circulating in consecutive seasons to determine how far apart they had evolved. They found that genetic distances based on broad sets of antigenic sites in HA and NA had the strongest and most consistent relationship with the severity of flu outbreaks, rates of virus transmission, and the dominant subtype of influenza A.

Specifically, the antigenic drift of H3 (the HA antigen of the A(H3N2) virus) was strongly linked to epidemic size, viral transmissibility, the age distribution of cases, and the number of excess deaths caused by the A(H3N2) virus. On the other hand, the antigenic drift of N2 (the NA antigen of the A(H3N2) virus) was more strongly linked to epidemic intensity, the prevalence of A(H3N2) cases relative to A(H1N1) cases, and the speed of the epidemic.

Furthermore, the researchers utilized machine learning models to predict regional A(H3N2) epidemic dynamics. Surprisingly, they found that the incidence of A(H1N1) was more predictive of A(H3N2) epidemics than viral evolution. This suggests that interactions between different viral subtypes, known as subtype interference, play a significant role in the spread of influenza A.

The study highlights the importance of monitoring the evolution of both HA and NA in informing annual flu vaccine strains and epidemic forecasting efforts. It also underscores the need to consider interactions between different flu subtypes when assessing the severity of flu epidemics. By understanding the factors that contribute to epidemic severity, public health officials can develop more effective strategies for preventing and controlling flu outbreaks.

1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it