Connecting with others is at the heart of being human and has been linked to greater physical and mental health, longevity and – ironically – better immunity.
Considering the negative ramifications of social distancing and isolation, European researchers have modelled three strategies that could enhance social connection while limiting the spread of COVID-19 in a “post-lockdown world”.
Reporting in the journal Nature Human Behaviour, they found through a series of computer simulations that all strategies were far more effective than no intervention or random social distancing.
Limiting social interactions and keeping physical distance has been a key approach to “flattening the curve” of the virus’s transmission and lightening the burden on health care systems.
As restrictions are eased under social and economic pressures, it’s critical to avoid a second wave as people have unavoidable contact with others, compounded by “compliance fatigue”.
Although social distancing has proven effective, Per Block, from the University of Oxford, UK, and colleagues note that it’s been haphazard and tends to be based on expert recommendations rather than scientific analysis.
Aiming to reduce the need for confinement and isolation, their strategies would enable people to purposefully manage their interpersonal contacts, drawing from simulations that track how things like gossip and ideas flow through networks.
“This understanding of how network structure influences the rate and extent of how far these things travel in networks can also be applied to disease spread,” Block says.
“What we model is disease relevant contact, so spending time in proximity to a friend, family member or so, where infection can occur.
“Within the model, we assume that everyone has a number of others they would like to meet, which basically overlaps with your group of people you have met before the start of lockdown.
“Now, compared to before the lockdown, where you could meet all of them however often and whoever you wanted, in the model people have to make more conscious decisions with whom they meet.”
The first strategy, based on individual attributes, allows small communities to be formed with similar others who are in the same area or organisation, like a work department, or are alike on demographic variables such as age or income.
Geographically, for instance, you could base your decision about who you want to interact with from your pool of potential contacts on how far away they live – and the closer the better.
“What this does is eliminate long-range infections,” Block explains. “Imagine that I carry the disease: if we only interact with the people living within three blocks of our homes, it takes a very long time until people living 100 blocks away can catch my infection.”
The second strategy localises interactions within pre-determined communities. “So think about your group of friends,” Block says, “some of whom will know each other well, while some other friends might not be integrated in these cliques.”
Taking this approach means you would only interact with people embedded in those tighter groups – and the same logic applies to families.
“Here the only thing that guides your choices is: who have I seen the last two (or x) number of times, and I should probably see them again and not somebody that I did not see recently.
“This is for very practical purposes very close to the bubble concept, since if I see the same two people over and over (as they were my last contacts), we basically form a bubble. The size of the bubble is determined by the number of previous contacts that I interacted with.”
Although social distancing has proven effective… it’s been haphazard and tends to be based on expert recommendations rather than scientific analysis.
Results showed that all strategies successfully slowed the spread of the virus, with social bubbles the most effective.
In reality, though, people are likely to mingle with others across several social circles, so the team also evaluated the outcome if the strategies were combined.
Combining two or all three scenarios turned out to be as effective as single strategies, and was still superior to non-strategic contact. The results held firm whether the strategies were modelled for 500 or 4000 people.
For these to work, though, we would need to agree to implement one or more of them – it wouldn’t work if I form a bubble, but you interact freely in your neighbourhood.
Block notes that the study is a simulation, so more work is needed to bring it closer to reality and determine which strategies work best for which community – a point made also by Linda Bauld from the University of Edinburgh, who was not involved in the study.
“This idea of forming social bubbles, as with the other two approaches set out in the paper, assumes that individuals will behave in a predictable way,” she says. “It also assumes that people can maintain a consistent pattern of contacts over time.”
But she does note the modelling could be useful while we navigate relaxed restrictions – bearing in mind that no approach is watertight.
“We need to recognise that lockdown release is about harm reduction, not compete safety.”
The good news is that, because the models simulate infection-relevant contact, you can hug whomever you like in your chosen network. But you need to be careful when outside your group.
“Transmission in haphazard contacts like the pub are not in the model and we rely on some degree of social distancing in these situations to keep infection minimal,” says Block.
“So you should probably not hug strangers.”
Natalie Parletta is a freelance science writer based in Adelaide and an adjunct senior research fellow with the University of South Australia.
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