Indeed, it is possible to interpret the β^ parameter associated with models. To this end, we have computed for models (a) and (b), the quantities vi=〈Xi,β^〉, which are the contribution of every curve to the influenza rate. So, if we classify the curves in groups according to these values and average them, we can see the pattern of the curves that have the most (or least) influence with respect to the incidence rate. This is done in Fig 2, which shows the pattern of curves that most contributed to increasing (in red scale) and decreasing (in blue scale) the influenza rate. In particular, we have split the data with respect to the quartiles of vi and assigned (from bottom to top) the following colors: blue, sky blue, red and dark red. This assesses the evaluation of the contribution of these curves in the response. So, as expected, the contribution of an intense increasing pattern of the influenza rate in the last weeks is plotted in dark red (see left panel of Fig 2), which leads to predicting high influenza rates. On the other hand, a decreasing pattern is plotted in dark blue, meaning that this type of pattern corresponds with low influenza rates. The same reasoning can be applied to model (b)(see right panel of Fig 2). Curves of temperature below 7°C are plotted in dark red, meaning that this pattern provides high prediction rates. On the other hand, the curves around 19°C (plotted in dark blue) lead to almost zero influenza rates. The dark red line corresponds to the pattern of the curves that most contribute to increasing the estimated incidence rate. In the week w = 1 begins vq4≈3.3 that, if we undo the logarithmic transformation represents an incipient incidence of 27.1 cases per 100, 000 population and goes up monotonously until last register (w = 13), which takes the value vq4≈4.6, implying an increase of 99.5 cases per 100, 000 population.