Figure One

Science. Communication. Community.

Gaming Out the Flu

The classic approach to influenza vaccination is to give priority to at-risk groups, like the very young and very old. But in the face of an emerging pandemic, is that the best strategy?

by Jessica McDonald

(Credit: Flickr/ANGELOUX)

(Credit: Flickr/ANGELOUX)

It’s that time of year, again—the leaves are changing hues, it’s perfect weather for football, and the pumpkin spice latte has returned. Along with the many perks of autumn, however, also comes the start of the influenza season.

As an asthmatic, I recently received a letter from my health plan reminding me that I’m at higher risk for complications to flu, and along with the elderly, pregnant women, young children, and others with various diseases, I will get priority for vaccination. That’s typically how it goes. Since the flu is not usually serious unless you’re a developing fetus or your immune system is compromised, the policy is to try and protect the most vulnerable. Indeed, when the Centers for Disease Control estimates that 90% of the people who die from seasonal influenza are over 65 years old, it makes a lot of sense to target that population.

In recent years, public health officials across the globe have also been concerned with vaccine preparation for emerging influenza viruses, especially following the H1N1 influenza pandemic in 2009. Quickly ratcheting up vaccine production for potentially virulent strains of flu is a difficult and expensive endeavor, and is likely to still fall short of demand. To figure out the best way to deploy a limited number of vaccines, a group of Japanese researchers decided to model the spread of disease with different vaccination strategies.

To simulate viral transmission in an urban population, the scientists built their models on a simplified version of Tokyo (five towns linked by a commuter rail with shops, parks, schools, and workplaces), with each citizen classified as an office worker, student, or stay-at-home resident. Over the course of the simulation, the health status of each citizen would change: from susceptible, exposed, infectious, to recovered (or dead)–based on equations incorporating interactions with others and efficacy of the vaccine, among others.

Schematic of the simulator used to evaluate vaccination strategies in a Tokyo-like model city. From: Saito MM, Imoto S, Yamaguchi R, Tsubokura M, Kami M, et al. (2013) Enhancement of Collective Immunity in Tokyo Metropolitan Area by Selective Vaccination against an Emerging Influenza Pandemic. PLoS ONE 8(9): e72866.

Published last month in the journal PLOS ONE, the researchers found that selectively vaccinating all of the office workers was the most effective intervention, reducing the infected population to just 2% compared to 30% of the city without any vaccination. Because the office workers travel and interact in more spaces, keeping them disease-free prevents transmission, and generally protects other members of the community as well.

As the simulation does not take specific tallies of extreme outcomes such as death, and the ethical issues therein, it’s difficult to imagine any government changing its vaccination policies at the moment. But the model nevertheless make a compelling case for vaccination as an amazingly powerful tool against influenza infection.

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This entry was posted on October 1, 2013 by in Health, Research and tagged , , , , .
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