An Agent-Based Model of the Diffusion of Covid-19 Using NetLogo, with Susceptible, Infected, symptomatic, asymptomatic, and Recovered people (+)
Version 0.9.6.1, release date: Jul. 31st, 2021.
You can run the model directly online or download SIsaR_0.9.6.1.nlogo, and the related file vaccinatePeople.nls, with right click and run it locally with NetLogo.
You have also: (i) the e-print of a paper on the model,
(ii) a YouYube channel of the model;
(iii) an short article published in RofASSS;
(iv) a seminar in February 2021 (in Italian) organized by
(v) the slides of a presentation in June 2021
within the Inverse Generative Social Science Workshop 2021;
(vi) a poster prepared in September 2021.
Script capability: the red arrow shows a script window and the blue one a list of short names (with their correspondent long name) to be used there. The instructions are into the Model Info. If you do not want to use the script, simply clear the content of the script window.
We suggest downloading the program to observe the repetition of the experiments, with different starting points (seeds) of the random numbers. This operation is quite smooth, via Behavior Search, which is in the menu Tools of NetLogo. Run the row "experiments with fixed parameters [considering NH [and schools]]." It is possible to analyze the different results of the model at the various checkpoints.
Nota bene. In each series of experiments, we will have variability in the results, although internally consistent. The model makes many agents act and interact; from the complexity point of view, each execution produces a "story," with a specific and unique sequence of emerging effects. Real events would behave in the same way if it were possible to repeat them.
Everywhere in the world, government leaders are facing DIFFICULT DECISIONS: we hope our model could help, instead of ...
How to cite:
Terna P., Pescarmona G., Acquadro A., Pescarmona P., Russo G., Terna S. (2020), An Agent-Based Model of the Diffusion of Covid-19 Using NetLogo, URL https://terna.to.it/simul/SIsaR.html