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Scientists still assume that the Sars-CoV-2 pandemic is being driven by super-propagation events. Potentially dangerous situations can be identified.
Locked seats in a movie theater in Jakarta are supposed to ensure distances are maintained.
The new corona virus is currently spreading across most of Europe at an almost impressive rate. We often speak of a diffuse event, that is, a wide distribution of cases evenly in many places. But according to experts, that’s only a small part of the problem. According to Christian Althaus from the University of Bern or Christian Drosten from the Charité Berlin, the spread of Sars-CoV-2 is still strongly driven by so-called clusters.
This means that a few infected people (the so-called super-spreaders) infect many other people and therefore there are foci of infection in many affected people. Experts currently assume that between 10 and 20 percent of those infected are responsible for 80 percent of Sars-CoV-2 infection cases. However, these groups were hidden, so to speak, in the wide spread, says Drosten. They would no longer be discovered due to faulty contact tracing. Therefore, it calls for the monitoring to focus more closely on expanding the cluster.
A cluster occurs when an infected person emits a large number of virus particles through loud or wet talking, wheezing, or singing. Also, in its highly infectious phase, this infected person must have known many people over a longer period of time and very closely. The risk of infection is particularly pronounced if these super spreaders have been with many other people for a long time in a closed, poorly ventilated room. Because there the virus particles expelled remain in the air in the form of aerosols. In such situations, even a distance of 1.5 meters does not protect against infection, because this prevents direct coughing, but not inhalation of air enriched with viruses.
The clustering behavior of a virus is described by the so-called scattering factor k. The lower the k value, the greater the probability that a very large source of infection will emerge and there will be more over-propagation events. Since the k value also depends on how well the clusters are recognized, it is not a hard-hitting parameter, but changes a bit depending on the situation of the data and the situation. In the case of the flu, it is 1: the flu virus is transmitted from one person to another, and so on. It is different with Sars-CoV-2: several scientists have calculated a k value between 0.1 and 0.45 for this pathogen.
The k value describes how infected people uniformly infect other people
According to experts, to “dry out” the clusters, all participants in a super-spreading event must be identified as quickly as possible and quarantined as part of contact tracing. To achieve this, health authorities employees must not only consult the contacts of an infected person in the last days to find out who could have infected an infected person (in English, this is known as “direct tracking”). Rather, it must also be precisely clarified whether a person concerned has found themselves in a suitable situation for cluster formation in recent days. It could have been infected there (“crawl back”) or, if it was already infectious at this point, it could have also triggered a cluster. In addition, it should be checked whether other infected people are already known to the identified risk situation.
The “crawl back” attempts to locate clusters
According to the health department, the canton of Bern is looking for clues. In some other cantons, according to research, offices may pay little or no attention to grouping situations. The attack was said to be currently due to exploding numbers. In Germany, according to Udo Buchholz of the Robert Koch Institute, many health authorities are now turning their attention, if possible, to the presence of an infected person in a cluster situation. It requires that when interviewing people also identify that they belong to the risk group. The risk situations for the formation of clusters also need to be better understood and then communicated to the authorities and the entire population, adds Ralf Krumkamp, epidemiologist at the Bernhard Nocht Institute in Hamburg.
Japan is hailed by many experts as a successful example of containing the Sars-CoV-2 pandemic by targeting cluster situations in contact tracing. According to virologist Hitoshi Oshitani, even a single contact is neglected from time to time to focus on looking for cluster situations in the days before and after a positive test result. However, the Japanese method of contact tracing is by no means the only factor in the successful control of the pandemic in Japan, epidemiologists emphasize. The generally distant interaction between people and the constant wearing of masks outside one’s own four walls are important factors.
The behavior guidelines there are not only based on distance and wearing a mask, most of them observe them anyway. Rather, in Japan the observance of the so-called three Cs is widespread: “close contacts” (close contacts), “crowds” (dense concentrations of people) and “closed spaces with little ventilation” (crowded and poorly ventilated rooms ) should be avoided at all costs, or at least until now. be avoided as possible. This has apparently caused the Japanese to move into cluster situations less often than the Europeans. As the number of cases increases, as is now the case in the fall and winter, it is increasingly likely that an infected person is present in risk situations where the three Cs apply and trigger a cluster.
Therefore, the population can largely avoid the formation of clusters, while the offices can only extinguish the sources of fire. To support them, Drosten recommends keeping a contact diary. There you must enter when you were where (and with whom). It is important to write down visits to restaurants or bars or meetings in the workplace that are not spectacular, in other words: possible cluster situations. Drosten is convinced that due to all our memory gaps, groups are not currently recognized in contact tracing and therefore do not appear in the statistics.
