Model building:
The module was built using modular bottom-up approach where every module describes a certain process and then, when modules are connected together like domino tiles, we can reconstruct the emergent behavior of the whole system.
This is a blueprint model and might be used for various country/data. If one wans to use it for a particular country/data, we can recommend following steps:
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Adjust total population by changing initial condition of A-Initial_population_innocent_non-tested (in species initial concentrations)
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Start with adjusting the following parameters in “Global Quantities”:
Social_distance -> abstract value referring to the extend of virus exchange in population (very adjustable)
Testing_randome -> shows how widely people without coronavirus symptoms were tested
Testing_for_symtoms -> shows how widely people with coronavirus symptoms were tested
Time-government_action -> this depends on the date when government initiated measures. The earlier is the date , the less is the infection spread
Government_induced_isolation -> this depends on the strength of governmental measures taken to decrease social distance. The higher is the “Time-government_action”, the less is the infection spread
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If above does not work, you can try to adjust the following parameters in global quantities as well: Infection-from_non-tested_no-symptom -> shows fraction of the contribution from non-tested yet, and without symptoms yet, but already contagious people, into Total Infection Coefficient Infection-from_non_no-symptom -> shows fraction of the contribution from non-tested yet, but already with symptoms, and already contagious people, into Total Infection Coefficient Infection-from_tested_no-symptom -> shows fraction of the contribution from tested, but without symptoms, contagious people, into Total Infection Coefficient Infection-from_tested_symptom-> shows fraction of the contribution of tested contagious people with symptoms into Total Infection Coefficient
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If you like, you can also adjust birth and death rates for your country by changing the following parameters in global quantities: Birth_rate -> computed from newborn per population per time Normal_death -> death rate constant without taking into account coronavirus Corona_death -> death rate constant with taking into account coronavirus Corona_recover -> rate constant representing the fraction and the rate of recovery
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Get your curve for your time scale and simulate it for longer to see the future!
SEEK ID: https://fairdomhub.org/models/693?version=1
1 item (and an image) are associated with this Model:Organism: Homo sapiens
Model type: Ordinary differential equations (ODE)
Model format: Copasi
Execution or visualisation environment: Copasi
Model image: (Click on the image to zoom) (Original)
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Views: 3721 Downloads: 198 Runs: 1
Created: 24th Mar 2020 at 14:07
Last updated: 15th Apr 2020 at 11:05
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Version History
Version 1 (earliest) Created 24th Mar 2020 at 14:07 by Alexey Kolodkin
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