Inferring statistical trends of the COVID19 pandemic from current data. Where probability meets fuzziness

We introduce unprecedented tools to infer approximate evolution features of the COVID19 outbreak when these features are altered by containment measures. In this framework we present: (1) a basic tool to deal with samples that are both truncated and non independently drawn, and (2) a two-phase random variable to capture a game changer along a process evolution. To overcome these challenges we lie in an intermediate domain between probability models and fuzzy sets, still maintaining probabilistic features of the employed statistics as the reference KPI of the tools. This research uses as a benchmark the daily cumulative death numbers of COVID19 in two countries, with no any ancillary data. Numerical results show: (i) the model capability of capturing the inflection point and forecasting the end-of-infection time and related outbreak size, and (ii) the out-performance of the model inference method according to conventional indicators.