## 3 Results
### Adaptive Learning Integration with ABMS
Now, in relation to the comparison with the implemented learning method, a reference densities curve should be selected. In this case, the maximum weekly density time series is picked, the reason being that it suppresses the effect of the weekend figures and allows for a smoother comparison with experimental results. Figure 9 displays the aforementioned data points on the right. Figure 7: Base densities’ independence analysis. What is interesting about the data in Figure 9 is that maximum densities seem to shift randomly near the 0.55 yardstick up to the 80 th day. Then, the specified figures go down slightly and converge near 0.53. Noticeably, the above behavior matches the scheduled outbreak progression, since it starts at day 60 and should begin reporting dead cases in \begin{table}
\begin{tabular}{c c c} \hline \hline
**Day** & **Lower Bound** & **Upper Bound** \\ \hline Monday & 0.1413 & 0.1494 \\ Tuesday & 0.1616 & 0.1708 \\ Wednesday & 0.1528 & 0.1616 \\ Thursday & 0.1532 & 0.1621 \\ Friday & 0.0854 & 0.0904 \\ Saturday & 0.0130 & 0.0141 \\ Sunday & 0.0016 & 0.0017 \\ \hline Week & 0.1038 & 0.1067 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Mean confidence intervals for the base densities in a week. Figure 8: Base densities’ boxplots. Figure 9: Densities’ boxplots. the following 15 to 20 days. The differences suggest that the epidemic itself reduces the maximum crowding values by 0.02, which is not meaningful at all. Base epidemic outcomes are showcased in Figure 10. It is apparent from the graphic that broad compartmental dynamics hold because the manifested behavior resembles a classical SEIRD scheme. Despite that, notable differences are revealed, since irregularities are detected in the susceptible and exposed curves. The results are quite revealing in several ways. First, a peak of 9424 infected subjects on day 20 conveys that COVID-19 progresses astonishingly fast in a semi-enclosed community where no action is taken, recording a 0.92 prevalence rate in a couple of weeks from the initial case. Second, a 3.9% mortality rate is registered, comparable to recently reported fatalities ratios [73]. Finally, a rather odd phenomenon is observed in the exposed curve, as the data does not follow a bell-shaped form; more precisely, exposed cases break in the middle and go up a few days later. The last proceeding could be explained by the latency term being around 2.4 days shorter than the incubation period and no new infections appearing on weekends. [PERSON] [74] states that multiple replications are required to obtain a good enough estimate of a model's mean performance. Unequivocally, a central question is: how many simulation runs must be performed? A rule of thumb hints that at least five repetitions should be carried out. However, a precise derivation is preferred instead. For instance, a statistically reliable method involves rearranging the confidence interval formula, as shown in Equation (8), where \(X\) is the variable of interest, \(\bar{X}\) is its expected value, \(\hat{\sigma}\) is its estimated standard deviation, \(\alpha\) is the selected significance level, and \(d\) is the percentage deviation of the confidence interval about the mean. Now, taking the maximum weekly density as the variable under study, a significance level of five percent (\(\alpha=0.05\)) and a deviation of ten percent (\(d=0.10\)), \(n=5.39\) minimum replications are obtained. Therefore, at least six simulation runs are recommended per experiment. Indeed, that means that the initial ten runs are sufficient. \[n=\left(\frac{\hat{\sigma}\cdot t_{n-1\alpha/2}}{d\bar{X}}\right)^{2} \tag{8}\]

### Experimentation

The preceding results show that the learning-lacking scenario does not comply with the social distancing policy. Thus, a design of experiments is aimed to determine the effect of RL-based social distancing with different sets of parameters. Truly, it would be ideal to examine a vast array of parameter settings.