Today numbers and statistics dominate the media. Deceased, recovered, infected and other parameters inform us every day of the delicate situation of COVID-19 in the country. An epidemiological model helps us understand what the future holds, although it is not always right.
Those who showed interest in this issue before its globalization will recall that some studies reassured us with phrases such as “it is estimated that there will be no more than 10 infected in our country.”
When the virus was confined to Wuhan and its surroundings, the predictions were much more optimistic and no one was able to predict what would happen in the following months.
We have to be clear about one issue: it is not about media manipulation. Scientists and researchers rely on patterns in the here and now to predict, but minimal variables can drastically disrupt modeled results.
In this space we want to show you what an epidemiological model is and its variables, in order to understand the margin of human error and take the predictions discussed in the media with caution.
Modeling a catastrophe
The mathematical modeling of epidemics consists of the use of mathematics to explain and predict the behavior of infectious agents. They are usually deterministic models, that is, they assume that anyone can contract the disease at random.
Two main hypotheses can be established on which to build the models:
The population of infected people is altered by their death or cure. Someone who is cured does not maintain the disease, so they are not cumulative values but variable over time.
The rate of individuals who go from being susceptible to contracting the disease or being infected is proportional to the interaction between the number of individuals in both classes. Or what is the same: the more infected there are, the more susceptible the general population will be to contracting the disease.
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