## 1 Introduction
They also established a relationship between the onset and intensity of provincial epidemics and international mobility, suggesting that control measures for travelers from previously affected areas, such as Italy, should have been implemented much earlier. The authors suggested there was a lack of foresight on the part of the Spanish government, since they were not able to anticipate the real development of the pandemic. This paper focuses on the time factor' role when managing an epidemic and specifically aims to overcome the time barrier between the spread of the pandemic (infections that are actually happening today) and perception about the spread (information of cases diagnosed and confirmed that comes to us). It is difficult to effectively manage a pandemic when there is no information available in real time on current growth and expansion. This is similar to the situation of a defense minister of a country that is being invaded and who has to make decisions about the movements and operations of its armies when information about the war front arrives days or weeks late, when the action has already occurred. In this study, the objective is to develop an empirical methodology or algorithm to obtain nowcasts that reduce the delay between the infection and the official number of published cases. The nowcasting is the prediction of the present (what is happening now), the very near future, and the very recent past and is usually used in meteorological predictions [16] and in economics [17]. Nowcasting has recently become popular in economics for the prediction of the current state of an economic indicator, as measures to assess the state of an economy are often determined after a delay. In our research strategy, positives to be confirmed in the future, in fact, reflect the surface of current, but still unobserved in the present, contagions. This is why this document proposes a data-driven approach to nowcasting. It is essential to have easy-to-use indicators for people to use as a reference. As explained in more detail below, we assume that there is a delay of 14 days between the infection and the registration of a positive case by the governmental authorities. This 14 day delay is a rough estimate, but a close estimate of the incubation period of the virus and coincides with the typical quarantine imposed after some dangerous contact. Based on these facts, the algorithm was designed to forecast the cumulative number of cases to be published 14 days after the date of calculation. If the algorithm works correctly, it will generate very valuable information on the spread of the pandemic, since we will have an approximation on:  
1. The number of infections that are occurring today, but will be detected within 14 days. Naturally not all infections that occur now will be registered as cases, but we will have an idea of the current rate of spread of the virus. This nowcast is the one that should help decide which NPIs and restrictions should be adopted immediately. 2. The cumulative number of cases that will be recorded within 14 days allows us to deduce, using appropriate ratios, the number of hospital beds, ICU, ventilators, medication, etc., that will be needed in two weeks and the amount of deaths that will occur. Obviously, it is a single value of the total cumulative number of cases that is taken as an indicator of current infections (nowcasting) and as a prediction of the total cumulative number of cases that will be registered in 14 days (short-term forecasting). Because it is very difficult to know the number of infections that have occurred today and that will finally be registered as cases, the approximation capacity of the algorithm will be assessed by comparing the results with the real values occurring in 14 days. These nowcasts and forecasts can be expected to facilitate early interventions to flatten the curve and help in healthcare systems' preparedness, which are useful to minimize negative pandemic impacts on health, the economy, employment, equality, and sustainability.