## 5 Conclusions and Future Research  
This article presents a model for predicting COVID-19 incidence in three risk categories: low, medium, and critical. Predicting the incidence in categories responds to the need to support the active search strategies carried out in the country for the detection and control of COVID-19. The risk levels of the model can be interpreted following the HGHI framework recommendations as follows: (i) the yellow level means a low transmission rate of the disease and orients decision makers to continue with testing and traceability measures; (ii) the orange level means that the virus transmission speed has increased, so that it is necessary to evaluate measures to increase active search efforts in communities at this risk level; and (iii) the red level means the speed of virus transmission is high, with it therefore being necessary to evaluate more aggressive measures to contain the spread of the disease in the community. The SVM- and LSTM-based classifiers tested in the case study showed good performance ranging between 93% and 96%. On the other hand, the zero-one-loss error decreases as more data are incorporated, with the SVM model having the lowest value. This result is explained by the small amount of data available to train and test the models since LSTM-based classifiers usually require considerably more data. As demonstrated, these models could be potentially helpful to evaluate the risk of incidence in a community; however, they do not replicate the structure proposed by the original HGHI classification, so that further investigation and the incorporation of more data are recommended. Taking into consideration the dynamic commune features (DEATHS and MOBILITY) as the baseline in each classifier, the importance of each of the regional and national features (refer to Figure 10) was analyzed. In this regard, it was observed that, in most  
Figure 10: This bar plot shows the relative importance of the features in terms of zero-one-loss error in predicting risk categories. cases, each of the commune and regional features positively impacted the performance of the classifiers. For example, at the regional level, CRITICAL BED OCCUPANCY and PATIENTS IN ICU rank first in terms of error difference with respect to those of the commune. On the other hand, in the case of national features, a more significant influence was found on the performance of classifiers in the use of features related to the type of hospitalization of patients and the use of mechanical ventilators. These results are consistent with the results of previous studies related to COVID-19 that show an association between COVID-19 and risk factors associated with patients admitted to the Intensive Care Unit (ICU) and occupancy of mechanically ventilated beds [34; 40; 41; 42; 43; 44; 45; 64]. We would like to point out that this study has the following limitations: (i) The data utilized correspond to free reports from MICITEC, so that there is no control over the frequency and quality of the information reported, which resulted in low availability of data for analysis (approximately two weekly reports for the period of study). (ii) The progression of COVID-19 in Chile has not allowed us to have a reasonable amount of data to train a model that considers the low incidence categories proposed by HGHI; in fact, after evaluating several Chilean communes, only Concepcion presented a data distribution that allowed us to identify at least three categories. We therefore aim to continue researching as more data become available to confirm this study's results and to extend the model to more communes. Consequently, in future work, we plan to pursue the following research directions: (i) In this paper, we grouped the incidence data by keeping the thresholds proposed by Harvard but combining the green and yellow categories into a single category that is referred to as low risk.