## 4 Discussion
It can be affirmed that the growing trend of infections began to slow down in the first half of September, which indicates that the curve began to flatten because the measures that began to be adopted at that time were effective; although, today, it cannot be known yet if they were sufficient. To sum up, the developed nowcasting methodology is useful to anticipate the adoption of interventions. As was seen in the case of Spain, the nowcasts indicated very well the July dates in which the NPIs, that had relaxed greatly in the second half of June, should have been retaken. However, they did not begin to resume until the beginning of September, with greater effort and with less efficiency than if they had advanced to July. When compared to alternative estimation methodologies, the algorithm provides similar numerical results to estimating rolling regressions, but our approach also provides the location of the negative and positive \"jumps\" that facilitate the understanding of the epidemiological moment and the need to undertake new measures. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Observed Cumulative Cases**} & \multicolumn{2}{c}{**Nowcasts Cumulative Infections**} \\ \cline{2-5}
**Date** & **Cumulative** & **Average 14d** & **Favorable-** & **Average 14 Days** \\  & **Number of Cases** & **Daily Cases** & **Unfavorable** & **Daily Infections** \\ \hline
28 June 2020 & 248,069 & 354 & 245,345–260,450 & \(-\)215–1238 \\
12 July 2020 & 253,917 & 418 & 260,511–276,626 & 1083–1150 \\
26 July 2020 & 272,331 & 1315 & 302,745–321,471 & 3017–3203 \\
9 August 2020 & 313,303 & 2927 & 369,300–392,143 & 4754–5048 \\
23 August 2020 & 385,135 & 5131 & 467,390–496,301 & 7006–7440 \\
6 September 2020 & 498,302 & 8083 & 594,392–631,159 & 7007–9633 \\
19 September 2020 & 626,918 & 9187 & 779,674–827,901 & 13,723–14,572 \\
3 October 2020 & 778,216 & 10,807 & 859,776–912,958 & 6810–7232 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Nowcast estimations. In addition to the short-term forecast and nowcast capacity, the algorithm developed in this work has the potential for its simplicity and easy implementation, and at the same time, it can be useful in the public communication of the evolution of the pandemic, which is also an important NPI, to explain to the general public how the spread is taking place and why it is necessary to apply interventions that anticipate the dynamics of the pandemic. Noticeably, this algorithm, which was developed during the second wave in Spain, must be readjusted to offer more accurate predictions and forecasts and must be tested with historical series from other countries to confirm its validity. Likewise, its predictive ability to project hospital needs and other epidemic parameters should also be tested. Moreover, positive deviations from the estimated might indicate either a failure of the current containment efforts, while negative deviations might indicate a success in mitigation strategies. Regarding the issue of the limitations associated with the significance of the results, in the field of forecasting, it is difficult to compute a test for the fitness of a model over a non-existing set of data. However, this difficulty grows in cases like the present one, since the evolution of COVID is determined by a multiplicity of factors, such as government intervention, external factors, such as weather, or even genetic factors. This makes it extremely difficult to use sample tests in this model. Nevertheless, we carried out sample tests weekly during the last month, and it worked fairly well. A good example to prove this is the small number of recalculations we needed to do when the actual data fall out of bounds.