In a new paper published in Proceedings of the National Academy of Sciences, scientists at Complexity Science Hub Vienna (CSH) offer an explanation of the linear expansion of the infection curve.
“At the beginning of the pandemic, the infection curves at COVID-19 showed the expected exponential expansion,” says Stefan Thurner, president of the CSH and professor of complex systems science at the Vienna University of Medicine. This may well be explained through a so-called snowball effect: an inflamed consumer would infect some others and, in a chain reaction, also transmit the virus to some others. With measures such as social estating, governments have tried to boost the rate of expansion below the recovery rate and thus massively decrease the number of new infections. However, in this logic, Americans would have become less inflamed than another user and the curve would have flattened, achieving 0, anything that didn’t happen,” Thurner explains.
“What we’ve noticed, on the other hand, is a constant point of infections with a similar number of new infections every day,” adds co-author Peter Klimek (CSH
The use of classic epidemiological styles would have required many precise parameter changes, making the style increasingly implausible. “If you need to balance the measurements so that the number of effective R replicas remains precisely at 1, which would be linear expansion, you have to reduce the contacts to the same accurate and consistent percentage. Actually, it’s incredibly unlikely,” Klimek says.
In fact, the probability of observing linear expansion in these popular compartment styles is almost zero, according to CSH scientists. They were encouraged to enlarge the style and seek further explanations.
Scientists explained the linear shape of the curves through another form of expansion from which it was originally predicted: they assumed that the expansion dynamics continued in small, limited groups. “Most people went to paint, become inflamed and transmitted the disease to two or more people. 3 more people at home, and then went back to painting or school. The infection basically spread from one group to another,” Thurner explains. “Replacing the S-shape infection curves to a linear habit is obviously a network painting effect. a very different dynamic from the big superpropagation events. “
Scientists have shown that there are a critical number of touches, which they call touch network grade or Dc, under which linear expansion and low infection prevalence will have to occur. They found that Dc was equivalent to 7. 2, assuming other people circulate. in a network applicable to coronaviruses of approximately five other people, which even reduces effective blockage (the length of the home is 2. 5 others on average).
Instead of having to adjust the parameters, its style allows a wide variety of probabilities that linear infection curves will appear in so many countries, regardless of the extent of the non-pharmaceutical interventions imposed.
In one more step, scientists compared Austria, a country that responded temporarily with a severe blockade, and the United States, which had not first imposed serious measures. According to Peter Klimek, his style works for both scenarios: “Both types of countries have shown linear curves, however, in the case of the United States and other countries like Sweden, these have occurred at a much higher level. “
The style not only explains the emergence of a linear expansion regime, but also explains why the epidemic can prevent low degrees of collective immunity due to significant social estrangation. For the style procedure, complexity scientists use a so-called compartmental style with SIR styles, extending it with the cluster transmission described.