# High-Performance Computing and ABMS for High-Resolution COVID-19 Spreading Simulation
## 8 Conclusions  
In this paper, we introduced an approach for modeling and simulating large-scale complex systems exploiting an ABMS approach and the high-performance computing with a high-resolution. In particular, we modeled as a use-case the spread of COVID-19 across two Italian regions (Lombardy and Emilia-Romagna). To prove the efficacy of our solutions, we implemented a fine-grained model that takes into account single individuals with a socio-demographic model that also involves a novel commuting model to model for this kind of simulation. The results prove that this kind of solution is be able to solve complex tasks like predicting COVID-19 by exploiting the light-weight actor model and HPC. The results we obtained for both cases Lombardy (RMSE 56,009 during spring 2020) and Emilia-Romagna (RMSE 10,730 simulating autumn 2020) with respect to the real data suggest that the epidemiological domain can really benefit from such kinds of simulations, especially at the beginning of a spreading phenomenon when few reliable data are available. In particular, our ABMS model offers some interesting features which distinguish it from other ABMS: (i) the scheduling of the agents is driven by the messages exchanged from the agents of the application. In particular, at each step of execution an agent can receive more messages, but it can only process the messages received in the previous step; therefore, the behavior of the agents never depends on the order of execution of the agents during a simulation step. (ii) The availability of \"remote proxy\" reference provided by ActoDeS simplifies the development of agent-based applications because developers do not have to worry if an agent needs to communicate with some local agents  
Figure 16: Comparison among our model, the serological data, the real data and the SEIR models (incremental representation—y-axis scale is logarithmic).  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **RMSE** & **Pearson Correlation** \\ \hline Simulated Data—Serological & 105,343 & 0.990 \\ \hline SEIR 1—Serological & 269,197 & 0.769 \\ \hline SEIR 2—Serological & 270,060 & 0.859 \\ \hline SEIR 3—Serological & 318,034 & 0.713 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of our model to the SEIR models in terms of root mean squared error (RMSE) and the Pearson correlation.  
or even with some remote agents, (iii) The use of aggregation messages guarantees a strong reduction in the cost of communication and, therefore, allows the development of real large-scale applications. Finally, in our future works, we would like to also stress these large-scale modeling capabilities in other domains and with complex tasks like financial simulation [68], temporal graph-based tasks [69; 70] and troll detection [71; 72], and also to refine our simulation models [73] and extend ActoDeS, taking advantage of some features offered by the JADE framework [74].  
Conceptualization, A.P.; methodology, M.P. and G.L.; software, M.P.; validation, S.C.; writing--original draft preparation, A.P., M.P. and G.L.; writing--review and editing, S.C.; supervision, A.P. All authors have read and agreed to the published version of the manuscript.  
This research has been partially supported by the Supercomputing Unified Platform Emilia-Romagna--SUPER project.  
Not applicable.  
The authors declare no conflict of interest.