Social Simulations for Intelligently Beating COVID-19


Kammler C, Onnes A, ́e LV̈ıs, Verhagen H, de Bruin B, Davidsson P, Dignum F, Dignum V, Ghorbani A, van den Hurk M, et al. Social Simulations for Intelligently Beating COVID-19, in AI for Social Good Workshop. ; 2020.


The COVID-19 virus has led to a world-wide crisis that requires governments and stakeholders to take far-reaching decisions with limited knowledge of their consequences. This paper presents the AS- SOCC model as a valuable decision-support tool for anticipating the consequences of possible measures by considering many interwoven aspects at the individual, group and societal level. Moreover, this paper illustrates how this model can be applied to study the effects of different testing strategies on the spread of the virus and the healthcare system. We found that excluding age groups from random testing was ineffective, while prioritizing test- ing healthcare and education workers was effective, in combination with isolating the household of an infected person.

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Last updated on 07/01/2021