Language as Filter

August Willemsen chose “De taal als bril” (he translates this with “Language as Filter”) as the title for a collection of essays in 1987. These prompted him to trust the phenomenon that translating, even if it remains an attempt, clarifies. This also applies to knowledge stories when you try to translate them into the formal languages ​​of mathematics or algorithms. He is now a participant in a world trying to survive the COVID-19 pandemic. Enough reason to see if he can get a better understanding of this world through such translations. First try if there are concepts in the world of common sense that he would like to see translatable into numbers. He made a preliminary list:

  1. Population compartments (Susceptible, Infected, sick & contagious, Resistant & cured, Deceased)
  2. Date and duration of a reporting period
  3. The risk of contamination with an individual infected ⟶ susceptible contact
  4. The average number of contaminations that a first infected patient would make during the period that he was contagious, in a world with only susceptible people.
  5. The average number of physical contacts that a person would have in the first period of the pandemic (if not yet recognized).
  6. The recovery rate or the chance of recovery once infected
  7. The average recovery time
  8. The average incubation time
  9. The average duration of infectivity
  10. The geographical location of individuals
  11. The architecture and accessibility of mobility networks
  12. The architecture and accessibility of logistics networks
  13. The architecture and accessibility of (mobile) communication networks
  14. The architecture and accessibility of care networks

He would like to know more about all these topics. In order to classify people in one of the four partitions (ad 1), they must be able to be measured and registered as belonging to a category. If we want to have an orderly news provision, we must have good communication networks (ad 13). Many of the other concepts seem useful in creating models that can answer questions such as:

  • How do the numbers and percentages of susceptible, sick, resistant and deceased develop when no measures are taken?
  • What factors / measures can promote / hinder contamination?
  • How are the numbers and percentages of susceptible, sick, resistant and deceased developing under planned and taken measures?

The first question is descriptive, he thought, and could benefit from mathematical statistics and large collections of data. He considers the second question to be a mixture of common sense, science and politics. And the third can be approached by translating behavioral intentions into algorithms that then show how assumptions and parameter values work out, at least in a toy world that emulates them.