About us

We are a group of software developers and mathematicians that are developing and tracking models. We decided to call this informal social cooperation propagmath.org We are located in different parts of the world and working from home to understand and contribute. We are in Northern Europe, Central/South of Europe, Central Asia, Oceania, Asia. We are grateful to our companies for giving us time and resources to work on such research pro bono. We welcome all intellectual contributions (we need ideas, models, thinking, skills, people with understanding of Americas, and Africa).

We are grateful that Professor's Bricaire has accepted to join and contribute to refining our models with his extensive knowledge and experience of infectious diseases.

This input, has confirmed tendencies, we were observing through data and has opened more insight as to better tuning of our models such as

1) Changing severity as epidmic progresses (data was suggesting it was becoming milder)

2) Existence of a population that is insensitive (or has low sensitivity) because of genetics or other variable capacities to a pathogen in addition to those who become immunized. Natural insensitivity seems to be often due to genetics but could be due to epigenitics or lifestyle. Data was strongly suggesting that such population may exist. Now we can incorporate that factor into the model.

3) Delays as to reappearance of antibodies in a later phase relying on immunological memory in case of a later future episode. 

These models are mostly meant to check facts, spot contradictions,  raise tendencies, explore scenarios, monitor progression. They are not meant to predict the future, only shed light on the present and open up horizons as to future options. 

With Professor's Bricaire input, we feel our models will become even sharper and contribute further to goals.

This is a work in progress we are sharing for suggestions to correct, improve, challenge by other computer scientists, mathematicians, or other people with a skill that can improve/correct the model. We have tried to make sense out of some the data available to produce possible numbers  To achieve this, we tested different options to retain certain plausible numbers that could fit together to provide possible explanations of what is happening. We are assuming, we think righlty that the immense majority is acting in good faith and trying its best. We are assuming that specialists and experts provide valuable input when they document their purposes. We also feel we should all be humble as each one is only seeing some facets. We also assume that models evolve and that new variables affect all the time. This is NOT A VIROLOGY OR EPIDEMIOLOGY model it is a spreadsheet simplified simulation tool based extracting a representation of some plausible scenarios we are exploring.

Since the contagion is very high, we believe that everyone gets the virus. And we can also see (also backed by public information) that as the  numbers are starting to increase, governments are not eager to report losses.

Main variables explored

 Replication of pathogen  We used different scenarios from one infecting 2 others up to 3,5
 Duration over which replication happens  We explored different options, 9 to 11 days spread was chosen
Start date in explored region Very tough to identify, it has been positionned by running multiple scenarios around estimates provided by studies
Normal region loss rate loss rate may explain in part why the virus may seem deadlier (people different rates at different countries because of age, lifestyle, health)
Dead infected with Corona This variable is to handle with care for 2 reasons, testing varies between countries, at the beginning some go under the radar and later if numbers rise some may not be tested

Corona impact ( can only be calculated after the fact by statistical analysis. If we applied the same standards to influenza the number maybe higher.)


 Region population Corona impact ( can only be calculated after the fact by statistical analysis. If we applied the same standards to influenza the number maybe higher.)
Variables discarded  This is discarded for reliability reasons as countries have very different criteria to define a case and within each country as numbers rise, from a practical point of view identification becomes approximate
Countries number of cases   

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