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As the COVID-19 outbreak is developing the two most frequently reportedstatistics seem to be the raw confirmed case and case fatalities counts.Focusing on Italy, one of the hardest hit countries, we look at how these twovalues could be put in perspective to reflect the dynamics of the virus spread.In particular, we find that merely considering the confirmed case counts wouldbe very misleading. The number of daily tests grows, while the daily fractionof confirmed cases to total tests has a change point. It (depending on region)generally increases with strong fluctuations till (around, depending on region)15th-22nd March and then decreases linearly after. Combined with the increasingtrend of daily performed tests, the raw confirmed case counts are notrepresentative of the situation and are confounded with the sampling effort.This we observe when regressing on time the logged fraction of positive testsand for comparison the logged raw confirmed count. Hence, calibrating modelparameters for this virus's dynamics should not be done based only on confirmedcase counts (without rescaling by the number of tests), but take alsofatalities and hospitalization count under consideration as variables not proneto be distorted by testing efforts. Furthermore, reporting statistics on thenational level does not say much about the dynamics of the disease, which aretaking place at the regional level. These findings are based on the officialdata of total death counts up to 15th April 2020 released by ISTAT and up to10th May 2020 for the number of cases. In this work we do not fit models but werather investigate whether this task is possible at all. This work also informsabout a new tool to collect and harmonize official statistics coming fromdifferent sources in the form of a package for the R statistical environmentand presents the COVID-19 Data Hub.

nonlinear dynamics

Are official confirmed cases and fatalities counts good enough to study  the COVID-19 pandemic dynamics? A critical assessment through the case of  Italy