In current data projections are abundant due to the spread of the COVID-19 virus. The experts continually contain US numbers and models. But do we know where they are extrapolated? In this article, we define the epidemiological model and look at its limits.
In this period, numbers and statistical data dominate the media. The dead, cured, infected and similar parameters inform us every day on the delicate situation of COVID-19 in our country. An epidemiological model is what helps us better understand what is possible in the future, even if it is not always accurate.
Someone interested in this topic before becoming a worldly renowned, will remember that some studies that tended to calm us down with phrases like: «It is estimated that the number of contagious in our country does not exceed. 10 people.”
When the virus was confined to Wuhan and its surroundings, the projections were much more optimistic and no one could predict what would have happened in the coming months.
We must take into account one aspect: We are not talking about the manipulation of the media. Scientists and researchers rely on currently available schemes to make predictions, but minute variations can drastically disrupt the reported results.
In this article, we want to explain what an epidemiological model is and what its variables are to understand the margin of human error and receive “with the pliers” predicted by the media.
Preview A catastrophe: the epidemiological model
The mathematical epidemic model consists of the use of mathematical tools to explain and predict the behavior of infectious agents. Usually it’s a matter of deterministic models, it’s that they assume that anyone can randomly get the disease. We can talk about two main welds of hypotheses to build two models:
- The percentage of infected people can be altered due to death or cure them. A cured person does not maintain the disease, so we are not talking about cumulative values, but temporary variables.
- The rate of people who pass from vulnerability to disease infection or infected is proportional to the interaction between the number of individuals in both categories. Or the same: the more infected there, the more the generic population will be vulnerable to the disease.
A numbers game against Coronavirus
One of the easiest models for examples of this concept is the SIR model. It is one of the most widely used epidemiological models, precisely because of its simplicity and for compartmentalizing data. The parameters are simple:
- Susceptible population(s): People without immunity to the infectious agent who may become ill. Unfortunately, recent diseases like COVID-19 initially make 100% of the population susceptible. The story changes far with the influence, for example, since the percentage of people vaccinated has a drastic reduction in this value.
- Infected population(s): Sick people can potentially infect the most vulnerable.
- Eliminated population (R): These are people who are immune to infection and therefore cannot infect people with whom they come into contact. Irony of fate: many deaths fall into this parameter so they can’t spread the disease.
The total population would be, therefore, the sum of S, I and R. Using these 3 compartments, by means of complex equations, it is possible to foresee the fluctuation of one compartment to another over a long time. It looks easy, right? The team of researchers on January 28 on January 28 in connection with the spread of the virus. Respect the reflectors on the limits of the mathematical model:
- The degree of contagion of the virus varies according to the place and time of exposure. The basic reproductive rate of the virus (R0) oscillates between the values 2 and 3, and anything else, even minimal, the variation of the parameter distorts the forecasts.
- Many studios may extend only one broadcast medium. In the case of this study, only the air transport of those infected is considered. But what is transportation by car, on foot, by boat or by train?
- We cannot exactly predict the effect of the means that have been taken in each country. Every nation reacts differently to the virus. We cannot know what time a country will decide to limit movements, quarantine or close the limits. We can’t make forecasts take these media into account, if we don’t know when they won’t even take it.
In addition to all these complications, others can be added. For example, the number of people removed (R) given in the SIR model as cured could not be. Cases of documented rectinations and asymptomatic medical care people, by complexes, prognoses. For this reason, an early diagnosis is essential.
Optimism and caution in the epidemiological model
We hope that in this space the immense complexity of developing an effective epidemiological model in this space has been demonstrated. The media and researchers are trying to offer the best information possible, but we must take the future numbers they give us for what they are: forecasts.
For better or worse, they could be wrong. However, one thing is certain: with the right media and the state of home isolation, the spread of the virus will sooner or later come to a halt parade.
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