## 2 Data Selection and Approach
There is another reason not to undertake discussions on the basis of the daily growth of infected people, as it seems inconsistent with policies followed in several countries (e.g., Sweden, as well as the first stage in the Netherlands, where the policy was changed later), namely, aiming at mass immunity. It has been reported in multiple countries that eventually 60%-70% of the population will be infected, but this must be achieved in a controlled way in order not to overload the medical sector. Nonetheless, the aim would be mass immunity, which implies the majority of people are infected and requires, in turn, an increase, rather than a decrease, in the number of infected people. Finally, a genuine fall in the true number of infections (a number we do not know), inevitably implies a decrease in the number of deaths. There will be a time-lag of several weeks between these events, comprising the weeks during which a person is tested, subsequently taken to hospital, and eventually dying. Relaxing safety measures too early, based on highly unreliable infection rates and disregarding the possibility of a new local outbreak, is therefore a serious risk. The second set of data available regards _the number of people healed_. As it is generally not compulsory to report when a person recovers from the coronavirus, the spread between countries can be extremely large: the collected data set we consulted reports almost no official recoveries for the Netherlands, whereas in other countries this is up to 25%-50% of those infected. Consequently, these numbers are the _least useful_ to be used for analysis. Infectious diseases are present year-round globally, with the flu one of the more deadly examples, infecting numerous people on any given day. However, the stand-out characteristic of COVID-19 is the associated high number of deaths that have occurred in a relatively short period of time. Thus, at the present time, in the absence of extensive testing of the populations of all countries, the most reliable and most relevant analysis needs to be made _using the number of people that have died_ (measurement errors and individual cases where people have died from, for example, a heart-attack, while also being infected with coronavirus, are often not counted as a coronavirus-related death; however, such deviations are likely to be considered as systematic deviations and therefore do not modify the overall trends). With an increasing number infections, the number of deaths will also increase; thus, the mortality will, in a first-order approximation, reflect true infection levels. Consequently, to avoid statements that would more likely involve some kind of speculation, we believe the focus should be placed on the number of deaths. As mentioned earlier, models aiming at describing the entire cycle from beginning to end require a series of parameters to be fitted [6; 16]. When the outbreak of the infection is still in the exponential growth state, this fitting of the overall curve may lead to different predictions for when the spread will flatten, depending on parameter choice (as in many kinds of modelling of experimental data, purely mathematical fitting and selecting the best fit as the best solution does not necessarily reflect reality). Therefore, for the European data we discuss in the present work, we only apply exponential fitting (in this work we use an exponential form for the mathematical function NP, where p is the exponent; a quadratic function is defined when p = 2 and a cubic function when p = 3) for the initial period when the infection rate is still high, as this is appropriate for the circumstances in Western Europe until 24 April (the last date for which we show collected data), although for some countries we see the onset of a change as we will show below. It is important to note that the fitting of these curves is only to reveal their steepness; in other words, the curves reveal exponential behavior, and the precise function has no concrete physical meaning at this moment.