The Truth About Coronavirus And How It Will Spread Around The World In 2020

The nuclear facility in Natanz
symptoms of Corona Covid-19
(Foto: imago images/Science Photo Library)
What the data say about Corona and what doesn’t
With the new coronavirus, new numbers hit us every day – more infected people, new deaths, plus calculations on doubling rates and death rates. We explain what that means, what is often done wrong and where scientists still argue.

What do we know about the spread of the virus?
The best strategy for a virus to survive is to spread itself as quickly as possible. It depends on the host cells to multiply. Before the immune system can take up the fight and win it, the virus must have jumped over to the next person. How often this happens is indicated by the base reproduction number (R0). A value between 2 and 3, as is also assumed for SARS-CoV-2, means that one person infects at least two others, these people in turn at least two and so on.

The basic reproduction number looks like it is set in stone. But it is not. Different institutes and authorities name different values ​​or ranges. The base reproduction number is a constant rate of reproduction. In the case of an ongoing epidemic or pandemic, there is no constant rising line, but a curve. The number of infected people initially develops rather slowly with a slight increase, at the end it literally explodes. It is an exponential growth.

The effective number of reproductions varies depending on the measures taken and how much contact there is between people. This ultimately results in the exact course of the curve. In order to curb the spread of the coronavirus, the number of reproductions must be pushed below the value of 1.

There is often a dispute about whether the R value is 0.9 or 1.0. Rather, the fact is that there is a possible area. This confidence interval indicates, for example, that the value is within a range with a 95 percent certainty. At the end of April this range was between 0.8 and 1.1. The R-value is only a snapshot depending on the tests carried out: If the sum of the tests increases, the number of cases very likely increases too. Especially at the beginning of the epidemic, the expansion of the tests could have a significant impact on the R-value.

Fluctuations of 0.1 in the R value are more likely to be subject to estimation errors than actual trends.

An R-value below 1 is still only an average value. Even then, there can be local outbreaks in some places if no one is infected anywhere else at the same time. The reported cases are often shown as a diagram and show just such a curve. The reported cases are simply added up – after a certain time this becomes overdramatized. Because if you only add up the reported cases, you are neglecting all healthy patients. As more people recover than are newly infected, the total number of active, known cases decreases.

Everyone can view the latest live statistics and case numbers on various news sites. They provide information on morbidity, i.e. the frequency of an illness in the population. One actually speaks of the prevalence. This would say how many people in Germany are sick with Covid-19 at a certain point in time.

Usually, the reported cases are related to a defined population (amount of people), for example the entire population or per 100,000 inhabitants. For this information, however, every infection would have to be known – we will explain why this is not the case later. What is meant are the reported, positive laboratory results – not the actual case numbers (this uncertainty persists). Numerical values ​​for an entire country are of little or no practical use. In Italy, half of the Covid-19 cases were concentrated in northern Italy. This information is lost in country numbers. A better classification is the frequency of 100,000 people per federal state. The number of cases per available hospital and intensive care beds could be more effective for political decisions.

It would also be helpful to state the number of tests carried out and to create a reference value. In Germany, only around six to nine percent of suspected SARS-CoV-2 cases are positive. The doubling rate depends largely on how many tests the laboratories carry out. If there are fewer tests than infections, the doubling rate can never tell you how the disease will spread. If the test capacities are then doubled and, accordingly, more cases are diagnosed, this has nothing to do with the speed of propagation.

The delay in reporting also influences the doubling rates. If little is reported on the weekend, the curve is flatter on Monday. Again, the cause is not the spread. In the news there is also the daily reports about the deceased, the mortality or death rate or mortality. With Covid-19 one means a case- or disease-specific mortality. The term mortality represents the deaths in relation to the entire population (or a defined sub-group thereof, e.g. all residents of North Rhine-Westphalia, or more sensibly: per 1000 inhabitants).

A distinction must be made between this and lethality. This term represents the deaths in relation to the number of all sick people. The mortality rate is often given across all age groups. This is useful for comparison with other diseases and infections, but overdramatizes the risk for young people with Covid-19 and underestimates it for older people. With more precise patient data, you can also calculate individual age-specific death rates, which show, for example, that older patients in particular die with Covid-19. This makes sense because the likelihood of dying from Covid-19 is more than 70 times higher for people over 80 than for 20 to 30 year olds, according to Chinese data. Usually the death rate is given for a period of one year. During an ongoing epidemic or pandemic, they are snapshots – which are constantly changing. More on that later.

And deaths are often calculated on the same day. The RKI reports 91,714 laboratory-confirmed cases and 1,342 deaths on April 5. This would result in a same-day mortality rate of 1342 / 91.714 = 1.5 percent.

Item Section:
According to previous data, patients with severe courses spend an average of ten days in hospital before they die. This period lies between the death and its official report. For a comparison with April 5th, the number of cases from March 26th must be used. There are 36,508 laboratory-confirmed cases. That would be quite a high value for lethality. It is well above that of the seasonal flu (2017/2018: 0.4 percent) and the estimates that experts gave at the beginning of the pandemic. These were between 0.3 and 0.7 percent. It is actually not a question of lethality, at best a preliminary calculation. One speaks of the case fatality rate (CFR), because the denominator does not contain the actual number of infected people, but the number of reported cases. The number of people actually infected is higher. This means that the death rate will also be lower than the proportion of people who died in a case.

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