The script CoVici (and CoVeni, elsewhere on this site) predicts the spatial spreading of the COVID-19 virus after a first infection happened. The virtual citizens can go through successive stages of being susceptible, infected, infectious, recovered or death. They can go in quarantine as well. Apart from adding some stages in infection development, CoVici adds spatial dimensions to the classical SIR model, allowing to track the progression in place and time. In the limit that the map contains a single point and that nobody goes in quarantine, this spatio-temporal COVID model becomes identical to the classical SIR-model.
Although I believe these programs bear some resemblance with reality, they should be considered as minimal models, in the sense that simplicity has been prioritized over virological accuracy. They do not reflect the state-of-the-art in the field. In return, they do allow you to change any parameter you like to test your skills as policy maker.
Script CoVici.m is written as follow-up on CoVeni.m to allow dealing with societies of a realistic size (millions of persons) and to overcome the problem that infected regions tended to quickly become 100% infected.
The society is defined on a map, on which different regions my have different properties like population density and composition. The members of the society can be differentiated into types of persons with different characteristics, as in CoVeni. Persons that have come in contact with the virus are treated on an individual level (but still statistically), the healthy majority (hopefully) is treated as a reservoir.
The program has some further advanced possibilities, like defining age groups that have different interaction probabilities (read: likelihood of infection) with the various other age groups. This turns out to make a major difference if the circumstances are right. An example is found in the example-job 'CoVici_job_AgeGroups.dat' (compare 'In.setXinfect = false' and 'true').
Upon running the script, you will be asked to specify an input file. Example input files are included in the zip-archive, the parameters can be changed as you like. Have fun!
Martijn Kemerink (2021). CoVici - a spatio-temporal COVID-19 model (https://www.mathworks.com/matlabcentral/fileexchange/86297-covici-a-spatio-temporal-covid-19-model), MATLAB Central File Exchange. Retrieved .
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