# High-Performance Computing and ABMS for High-Resolution COVID-19 Spreading Simulation
## 3 Agent Based Modeling and Simulation for Epidemic Scenarios  
Different ABMS solutions have been proposed to model and solve real and complex epidemic scenarios and, in particular, COVID-19 spreading [34]. [PERSON] et al. [35] assert that such scenarios can be modeled both with agents (ABMS) and with equations (EBM), but also that: (i) ABMS is most appropriate for domains characterized by a high degree of localization and distribution and dominated by discrete decisions, and (ii) EBM is most naturally applied to systems that can be modeled centrally, and in which the dynamics are dominated by physical laws rather than information processing. Moreover, they foresee that ABMS will be offered better solutions than EBM if they will provide comparable tools for constructing and analyzing system dynamics models. [PERSON] and [PERSON] [36] demonstrated that stochastic ABMS can show better performance in respect to differential equation models, when several parameters are unknown and there is the need for capturing heterogeneity across individuals and their network of reciprocal interactions. [PERSON] et al. [37] compare ABMS and meta-population strategies for modeling the epidemic in Italy, concluding that a trade-off between the two methods depends on data availability and suggesting the use of hybrid models.  
[PERSON] et al. [38] proposed an individual-based simulation model for exploring scenarios for COVID-19 where individuals are modeled as moving particles. In particular, COVID-19 infections take place when two particles come closer than a certain contact radius. Social distancing for COVID-19 is modeled as changes in the contact radius or introducing a momentum term. [PERSON] et al. [39] modeled COVID-19 spread by replacing the moving particles with contact networks for households, work, and random contacts. Their model should enable scientists and policymakers to quickly compare the effectiveness of non-pharmaceutical interventions like lockdowns, testing, quarantine, and digital and manual contact tracing. In particular, the model is experimented in an environment represented by a city with a default population of 1 million people whose ages and contact patterns are parameterized according to UK demographics. Moreover, the use of contact networks has been proposed by several researchers (see, for example, [40; 41; 42]). [PERSON] et al. [43] proposed a high-resolution model using single individuals' features with real data about the COVID-19 outbreak in New Rochelle (NY), where thousands of citizens live. Moreover, they extend the typical simulation by modeling employees, hospitals, schools, and retirement homes and simulating the use of different test strategies, different types of treatment, and the presence of infections that cause similar COVID-19 symptoms. Finally, [PERSON] et al. [44] proposed an ABMS model that divides the individuals on the basis of their epidemic state (susceptible, exposed, infected and recovered); such conditions can be used to model the epidemic spread before the introduction of vaccination of the individuals. Individuals interact with each other and with the modeling environment, and transmission of morbidity and the transitions of individuals between states occur on the basis of probabilistic coefficients. These coefficients are determined experimentally on the basis of official statistics data on the incidence of COVID-19 coming from the Ukraine Health Center. The results of the simulations showed that the most effective measures to reduce epidemic dynamics are self-isolation of patients and tracking of contact individuals of the population, and that the isolation of the entire population is not necessary, but it is enough to isolate 80% of patients in the active phase.