  Rapid and affordable methods of summarizing the multitude of data relating to
the pandemic can be useful to health authorities and policy makers who are
dealing with the COVID-19 pandemic at various levels in the territories
affected by SARSCoV-2. This is the goal of the Synthetic COVID Index, an index
based on an ensemble of Unsupervised Machine Learning techniques which focuses
on the identification of a latent variable present in data that contains
measurement errors. This estimated latent variable can be interpreted as "the
strength of the pandemic". An application to the Italian case shows how the
index is able to provide a concise representation of the situation.
