A modelling study across the Italian regions: Lockdown, testing strategy, colored zones, and skew-normal distributions. How a numerical index of pandemic criticality could be useful in tackling the CoViD-19

TL;DR

This study analyzes Italian regional data during the first COVID-19 wave to identify key parameters like skew-normal distribution metrics and mortality rates, proposing a numerical index to guide containment measures effectively.

Contribution

It introduces a set of universal parameters, including skew-normal distribution metrics, to assess pandemic criticality and inform regional containment strategies.

Findings

Identification of key parameters for pandemic assessment

Proposal of a numerical index for regional risk evaluation

Comparison of first wave data across Italian regions

Abstract

As Europe is facing the second wave of the CoViD-19 pandemic, each country should carefully review how it dealt with the first wave of outbreak. Lessons from the first experience should be useful to avoid indiscriminate closures and, above all, to determine universal (understandable) parameters to guide the introduction of containment measures to reduce the spreading of the virus. The use of few (effective) parameters is indeed of extreme importance to create a link between authorities and population, allowing the latter to understand the reason for some restrictions and, consequently, to allow an active participation in the fight against the pandemic. Testing strategies, fitting skew parameters (as mean, mode, standard deviation, and skewness), mortality rates, and weekly CoViD-19 spreading data, as more people are getting infected, were used to compare the first wave of the outbreak in the Italian regions and to determine which parameters have to be checked before introducing restrictive containment measures. We propose few \textit{universal} parameters that, once appropriately weighed, could be useful to correctly differentiate the pandemic situation in the national territory and to rapidly assign the properly pandemic risk to each region.