It’s become a common-sense policy suggested by health stakeholders the world over: stay home when you’re sick, to avoid spreading infection.
But during an epidemic of infectious diseases such as flu, replacing infected key individuals with healthy ones may actually spread the disease more rapidly through a community, due to the underlying structure of the social network, suggests a paper published online this week in Nature Physics.
The study combines modelling with empirical data analysis to explain epidemic patterns in influenza outbreaks at a state and national level in the US.
The policy of systematically replacing key individuals — such as teachers and health care workers — that become infected during an epidemic with healthy substitutes follows clear logic: infected workers are incapacitated, and run the risk of infecting others, the authors say.
However, standard models for understanding the dynamics of a spreading disease fail to incorporate the fact that these healthy substitutes are being introduced into a more dangerous situation than they were in previously.
By specifically encoding this increased risk into a network model, Samuel Scarpino and colleagues show that this policy of replacement — named relational exchange — can cause a disease at its peak to transmit at a rate faster than would be expected at the onset of the outbreak.
“Consider the school teacher who is infected with influenza by a student,” the authors write.
“At some point, they may stay home from work due to the illness and a replacement instructor will fill their role.
“For the ill teacher, this is probably a benefit, in that they have time to recover; however, the replacement teacher is now in a social situation where infection may be much more likely.”
The authors test their model on data for influenza and dengue. Both diseases are subject to seasonal effects, but dengue, unlike influenza, features a delay between transmission and behaviour, and is expected to be less susceptible to the impact of relational exchange.
The authors show their model is consistent with data for 17 influenza outbreaks in the United States at a national level, 25 years of influenza data at a state level, and 19 years of dengue virus data from Puerto Rico.
The findings are expected to better inform public health decision-making during outbreaks.