Little Differences in Behavior Decide Between Success and Complete Failure

Fluid and turbulence physicist Björn Hof and his team applied the statistical methods to the spread of epidemics and discovered surprising features of the infection curves. Credit: IST Austria / Nadine Poncioni

Scientists from IST Austria show that small differences in behavior decide between success and complete failure of epidemic control.

What does fluid physics have to do with the spread of the Coronavirus? Whirlpools and pandemics seem to be quite different things, especially in terms of comfort. Still, the latest findings on the spread of epidemics come from physics professor Björn Hof and his research group at the Institute of Science and Technology Austria (IST Austria), who specialize in liquids and turbulent currents. When Björn Hof had to cancel his planned visit to Wuhan, the birthplace of his wife, early last year, his attention shifted abruptly to the spread of epidemics.

“My group normally researches turbulent flows in pipes and channels,” he explains. “Over the past 10 years, we have shown that the onset of turbulence is described by statistical models that are equally used to describe forest fires and epidemics.” Given this experience, programming an epidemic model was a simple exercise for Burak Budanur, the group’s theorist and computational expert.

The epidemic curve does not flatten, it collapses

Standard epidemic models suggest that the degree of mitigation has a continuous effect on the height of the epidemic peak. “The curve is expected to flatten in relation to the level of social distance,” said Davide Scarselli, lead author of the paper. However, when he first simulated epidemics, taking into account the limits in testing and contact tracking, the picture was very different. The maximum number of infected people initially dropped as expected, but then suddenly dropped to almost zero when the mitigation level reached a certain threshold. In one limit, about half of the people became infected during the epidemic. In the other, only three percent got the disease. Surprisingly, it was impossible to get a result in between these two outcomes: either there is a significant outbreak or there is almost none.

Failure delivers faster than exponential growth

Testing known contacts (not necessarily testing) is one of the most powerful ways to slow an epidemic. However, the number of cases that can be detected on a daily basis is limited, as is the number of tests that can be administered. As the researchers found, exceeding these limits at some point during the epidemic has far-reaching consequences. “When this happens,” says Timme, “the disease starts to spread more quickly in the uncontrolled areas and this inevitably causes a super-exponential increase in infections.” The exponential growth is already enormous. It means a doubling of infections every few days. However, super-exponential means that even the rate of doubling becomes faster and faster.

As long as this acceleration can be avoided, epidemic curves collapse to a relatively low case level. Interestingly, it makes relatively little difference whether contact tracking is protected by a small or a large margin of safety. The numbers remain relatively low. On the other hand, if the limit is only exceeded by a single case, the super-exponential growth will cause the total number of cases to jump to a tenfold level.

Marginal differences and disproportionate effects

“Like most countries, Austria did not react early to the second wave. When all contacts could not be traced back in September last year, it was not difficult to predict that the number of cases would increase rapidly faster than exponentially, ”says Scarselli. While it has become clear over the past year that an early and decisive response is essential in exponential growth, the team’s study shows that test limits make timing even more important. The difference between the success and failure of a lockdown is marginal, or as Budanur puts it, “A policy that would have worked yesterday will not only take much longer to take effect, but it could fail altogether if it were implemented too late. . Hof adds: “Most European countries only responded when health capacity was compromised. Actually, policymakers should have paid attention to their contact tracking teams and locked them up before this protective shield fell apart.”

More recently, the team has looked at optimal strategies, using lockdowns as a preventative tool rather than an emergency brake. A manuscript is currently being worked on outlining the optimal strategy, which will minimize both the number of people infected and the required lockdown time.

Reference: May 10, 2021, Nature Communications.
DOI: 10.1038 / s41467-021-22725-9