How the influenza virus takes control of a city, and implications for SARS-CoV-2

By Michael Greenwood, M.Sc.Reviewed by Emily Henderson, B.Sc.Nov 19 2020

SARS-CoV-2 has spread around the world, passed across the borders of nations by travelers and cargo. However, the intricate details of the dynamic spread of such viruses within cities remain elusive.

Image Credit: pinkeyes/Shutterstock.com

A recent paper published to the open-access journal PLOS Pathogens by Müller et al. (2020) aims to clarify the dynamics behind the local spread of seasonal influenza, revealing patterns that could potentially prove invaluable in planning epidemic or pandemic interventions.

Seasonal influenza is a rapidly evolving virus, allowing it to return each year as a unique strain that evades host immunity and rapidly spreads through the population. Incidence and prevalence data collected at such times can be used to infer in what way and how rapidly the virus spreads, though lacks information regarding in what specific way the virus has mutated. Phylogenetics, the study of the evolutionary history of an organism, was exploited by the group to construct a phylogenetic tree that identifies virus strain dynamics over time.

Methods

The group collected data relevant to the 2016/2017 Influenza season in the city of Basel, Switzerland. Influenza A (N3H2) was the most common strain, and 663 patients were selected from several healthcare centers around the city.

The genome of the virus was sequenced, and the sequence was compared with those collected and uploaded to an online repository between 2016-2017 from around the world. Computational software was then utilized to construct a phylogenetic tree.

Information regarding each individual suffering from influenza was also collected, including age and family status, allowing the group to observe how each of the six categories of individuals influenced the spread of the virus.

Introduced lineages

The group noted that across the 663 assessed samples collected, a large variation was observed that is comparable with the diversity of the influenza virus seen globally. Around 240 local clusters were identified, suggesting that at least 240 varied strains of the virus were introduced to the city from an external source.

Local Transmission

To assess how the virus was spread locally the group assigned a reproduction rate to the virus, in line with other influenza spread rates observed in Switzerland, and could see that their assigned value was valid when comparing the case numbers across the season.

The group went further, investigating the role that several factors play in adjusting this reproduction rate, including relative humidity and temperature, and whether children were expected to be attending school.

It has been demonstrated in animal studies that viral shedding of viruses is increased at lower temperatures, and the group found this to hold true, with the virus expressing a lower reproduction rate at lower temperatures. Little clear correlation was observed between humidity and the reproduction rate, however.

By extrapolation, it was estimated that between 8-13% of the population of Basel was infected with Influenza in the winter season of 2016/2017, though it is noted that this is likely on the lower boundary of the true number, and the group adds that temperature and humidity conditions may affect the behavior of a person, not only have a direct physical effect on the virus.

Which individuals are super-spreaders?

The collected samples were divided into categories based on age group and family status (living with children or not living with children). School-aged children were found to be more connected individuals than preschoolers, and so as expected more effectively spread the virus than younger preschool children.

Additionally, adults cohabiting with children are subsequently more likely to have contact with other adult influenza patients from other households than adults without children. The elderly were shown to largely spread influenza amongst one another, having the fewest connections to other age groups.

Indeed, the authors note that the elderly and preschool-aged children are somewhat overrepresented in their data set, being the most likely to require medical intervention for the condition, though that these figures are likely representative of the true proportionate figures.

Children effectively spread the virus amongst themselves and to their parents, who go on to spread amongst other adults and introduce the virus to new locations. The elderly, in turn, tend to be exposed to fewer introduced viruses but form a tight network wherein they spread amongst one another.

This work involves one of the most densely sampled genetic datasets of Influenza sequences ever gathered, combined with a large volume of collected data regarding patient age and household status. Improved understanding of how viruses spread locally will aid in streamlining public health intervention in the future.