The reproduction rate (R0) of COVID-19 is twice what was estimated and five times higher than that of influenza
The R0 is a figure that speaks of the ability of a pathogen to infect new victims during the period in which it is in the patient's body. This figure is one of the most relevant when it comes to understanding the virulence of a disease.
At the beginning of January, the R0 for the coronavirus was more than 2. Just two months later, the calculations show an R0 of almost 6, more than double what was originally anticipated. Understanding how this number works is important to better understand how the disease will spread in the population. Obviously, this is vital to define realistic strategies and tackle the pandemic in the most efficient way possible. However, in the case of the coronavirus, this number is much larger than we had calculated at the beginning of this situation.
Coronavirus and flu, the dire comparison
At the beginning of the year, the appearance of this disease surprised by the speed of expansion and the impact it predicted. As it progressed, the measures became more crude and the first unknowns we talked about in early March began to appear. The comparisons with the flu and its figures also began: case fatality, infections, R0 ... Despite the fact that the error was warned and the danger of doing so. By the end of March, the death toll and the case of Italy supported the decision to confine the population. The global outlook for the pandemic made clear the difference between this disease and the flu. In addition, another data was beginning to gain strength: the initial calculation of its R0, at the beginning of the year, had fallen far short.
Despite the progress made to understand the virus, there are still many unknowns around its transmissibility. The amount of uncertainty and erroneous data, something we were talking about recently, makes it much more difficult to follow the actual statistics. Initial estimates from the Chinese CDC in January suggested that the number of infected people doubled every 6 or 7 days, with an R0 of 2.2 to 2.7. In contrast, data presented in early April yield more disturbing figures: the coronavirus spreads faster and easier than originally anticipated.
The results published by the epidemiology team at the Los Alamos National Laboratory show that the doubling time is more than 2.3 or 3.3 days, with an R0 of 5.7 on average. What has changed since then? The difference is not so much methodological, since a similar model based on the number of infections and contacts per patient has been used. The disparity between the initial R0 and that of April can be explained by the knowledge acquired in recent months. Especially in the increasingly accurate statistical data that the authorities of each country apply more and more and to the best control measures against COVID19. This helps to provide more reliable data, although the nuances of such data (why lethality changes according to the area, how the measures affect ...) are still impossible to grasp.
Explained in another way, an R0 of 5.7 indicates that an infected person is capable of infecting, on average, 5.7 people. This is due to several reasons that put earth in the middle with respect to influenza: the infective capacity of the virus itself, which does not require much viral load; the possibility of transmitting the virus without showing symptoms and in the early stages of infection; the ability to resist the virus on inert surfaces and even society's ignorance of how it works.
From the forecasts and models that we mentioned, we draw a clear conclusion: the coronavirus is much more contagious, with a much higher R0, than we thought. In comparison, this figure is between two and five times higher than that of influenza, which reaches an R0 of 2.8 according to data from the Chinese CDC. Another interesting comparison: With an R0 of 2.7 on average, according to the Department of Biostatistics at the University of Washington, even Ebola is less contagious than coronavirus.
The data estimated to date, place SARS-CoV-2 among the most contagious diseases, at the level of rubella, whooping cough or deadly smallpox, whose R0 figures are between 3.5 and 6. It is also It is true that this coronavirus falls far short of the two major airborne diseases: measles and chicken pox, whose R0 is between 10 and 18.
What is R0 and how does it work?
Epidemiologists, as with many other statistical sciences, focus their models and claims on numbers. One of the most important, especially at the beginning of a disease, is R0. This name refers to the basic number of reproduction or basic rhythm of reproduction. In a disease, R0 tells us how many people can be infected from a patient (who would be individual 0). Thus, an R0 of one means that an infected person will be able to infect another. If R0 equals five, up to five individuals may contract the disease during the infectious period that lasts in R0.
This figure is calculated from empirical data, although it is affected by many complex variables. There is no single method to calculate the R0, but it is determined directly according to the cases observed in an epidemic, normally, or following statistical models that consider various variables. There are different models, some simpler and others more complex, to determine the R0 and, also, its implications. We see this clearly in the two calculations: the one carried out in January and the one now, in April; that using similar models, which are based on the calculation of the infected and the contact they have had with each other, they reach very different figures. The reason, as we have already explained, is in the quality and quantity of data obtained as the disease progressed.
R0 is useful in that it helps determine when an infectious disease can lead to a serious outbreak. Its interpretation and use are due to the works of Alfred Lotka and Ronald Ross and George McDonald, among other epidemiologists. Thanks to the latter, for example, we know that an R0 less than one means that the disease will stop spreading after a long period, while if it exceeds one, it begins to have significant virulent potential. Generally speaking, the higher R0 is, the more difficult it is to control the disease.
R0, an essential figure, at the beginning of the epidemic
It is essential to know with certainty the R0 of the disease to be able to effectively undertake a contingency plan, especially if we are talking about forecasts. In other words: R0 was probably the most wanted figure at the beginning of the pandemic. At the moment, statistics such as lethality or the probability of cure give us more urgent data to help stop the contagion curve. This is thanks to measures such as confinement, which aim is to reduce R0.
Furthermore, reviews of previous cases indicate that this figure, by itself, is not as useful. However, when put in context with other epidemiological parameters, this figure allows us to better understand an outbreak and to prepare the corresponding public health response in the early stages of the disease.
On the other hand, there is a more technical factor related to R0: the spread of a virus is one more piece of information in the puzzle that allows us to know how the disease evolves. As in the epidemiological context, the contagion capacity can help us better understand the molecular mechanisms used by the virus, a door that must be opened if we want to fight it.
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