CAEBM & Covid-19 Pandemie

> Covid-19 pandemie is a relevant and rather tragic example, that today's concentration on incomplete Science-Based Modeling (SBM) and error-prone statistics is not enough to handle the problems at hand in a reliable and efficient way, to avoid unnecessary (millions of) victims + big damages of society & economy.

> In the meantime, the virus delivers myriads of examples of it's behavior under different social, political, psychological, and climatic conditions, and under more or less restrictive measures taken by the responsible but overwhelmed decision makers. 

> Why not listen to the local\ regional\ national examples = "messages" of the virus itself? As a deterministic item, we can learn from the virus "by example", extending our restricted max 2-5 dim human EBM capabilities. 

Why not deploy high-dimensional, inter-disciplinary CABM technology, to predict the number of infected\ hospitalized\ intensive cared\ dead people for a given set of virus-control measures (SoVCMs)?

Why not really optimize the SoVCMs as far as possible to avoid unnecessary victims and unnecessary damage to our society and economy?

> To give some ideas about what can be done, here some examples. Of course, not every modeling example has to be done. The Covid-19 problem owners and decision makers have to decide, what specific modeling is most helpful for them:

> Prediction for 1\ 5\ 10\ 20 days of the Number of Infected (NoI1..20), Hospitalized (NoH1..20), Intensive Cared (NoC1..20), Dead (NoD1..20), depending from Social, Political, Psychological, Climate conditions under specific SoVCMs.

> Quantitative effect of different Social, Political, Psychological, Climate condition changes on NoI1..20, etc.

> Quantitative influence of different Sets of Virus Control Measures (SoVMs) over time on NoI1..20, etc.

> Quantitative evaluation of what to learn from different results in different local\ regional areas.

> Quantitative evaluation of what to learn from other countries.

Refinement of today's (partially meaningless) Covid-19 indicators to gain better control of the epidemie.

> Setup, evaluation, and continuous refinement of an "optimal" Covid-19 strategy, including vaccination and testing campaigns, specialized for different regions and their boundary conditions.

> etc pp

> The overall result can be a network of interconnected adaptive CAEBMs to collect, integrate, consolidate, and use(!!) systematically(!!) the example-based know-how provided by the virus itself, including the findings of the engaged experts so far. 

All this to support the decision makers to move from "try and error" to solid grounds of knowing and controlling the mechanisms of the Covid-19 pandemie in a predictive, pre-caring way.

Please, drop a note, if you are a Covid-19 "problem owner' or "decision maker", and if you want more info or our suggestions, including a free introductional consulting on "How to get a CAEBM project on some of the topics proposed above down to practical work".