The approach obtained optimal strategies by using parallel Relay networks.
However, the authors needed additional assumptions on this Carleman weight function, and the method that was developed is completely different with respect to the approach obtained in our paper.
The approaches obtained after the snowballing were divided into four groups.
The results show that the evolutive approach obtains the best results in the context of the real data from a riparian area in southern Spain.
The WLW approach obtains the estimate of the covariate effects from the partial likelihood function under the working assumption of independence and then adjusts the variance estimate empirically.
The design approach obtains the marginal effect of the characteristic parameters of NPIs, social behavior, and their interactions on various pandemic outcome measures including total number of contacts, infections, and deaths.
Further, the proposed approach obtains balanced prediction performance with the under-sampling technique.
Only for (50~%) of semi-supervision the presented approach obtains a better accuracy, while the active learning has similar results to the ones obtained by SVM.
We show that the Poisson approach obtains almost identical estimates to the Cox model, is additionally computationally efficient and directly estimates the baseline hazard.
