# Art and knowledge

On March 16, the COVID-19 pandemic exploded in Europe. Looking at the worldometer on that day, and kind of remembering when it started, and having picked a credible reproduction number from the internet, he came up with an extremely simple model to compute the number of infected people (I_{t}) at moment t: the number of infected at the end of a specific infection period is expressed as the number of infected in the last period (L_{t}) plus the number of newly infected (or contaminated) people during the past period (N_{t}) times the contamination number (C) (or the mean number of persons that an infected person contaminates during the infection period).  In crude and simple terms:

what we will get tomorrow

is what we have today increased with what we contaminate today.

In formula:

I_{t} = L_{t} + N_{t} * C

He had already seen how the formula behaves when the length of the contamination/infection/reproduction period is assumed to be 5 days and the contamination number is 2 and the beginning of the pandemic is assumed to be in the end of December 2019. He wondered how the results that were artificially produced through the formula would resemble what the real world had shown. The formula was created on March 16. Below is a table showing how the results of March 16-26 compare to the numbers generated by reality (harvested from worldometers.info):

Knowledge is what has been observed or measured, and art is what has been calculated, he thought. After all, the latter is the result of creative work, even if it was done with scientific ambition. So theories and algorithms belong to the arts and not to knowledge. Since they are among the instruments of science, there is a risk of misclassification. Theories and algorithms are works of art that can be known but do not automatically become knowledge themselves. The latter can happen, but only if what is calculated is of sufficient quality.

That is a difficult issue, he knew. Especially when it comes to knowledge and art. If he remembered well, art often becomes knowledge because the accompanying story (the theory, the algorithm) is coherent and shows inner logic, and moreover proves to be reliable when applied in or to reality as the evaluator experiences it.

This means that the formula (and therefore the algorithm that realizes the formula) are art, but not knowledge. In terms of reliability, it would still go for those who are mainly interested in the exponential growth of the pandemic. But in terms of coherence, algorithm-0 falls short. It does not take into account phenomena that are important according to common sense: how many people are susceptible, how many infected and how many resistant (recovered or dead). Of these three from the most common simple epidemic model, the SIR model, only the number of infections can be found in algorithm-0. That is therefore incoherent because we know the other two are additionally needed to be able to show the long-term dynamics of an epidemic.