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In this paper, the results of the project INSESS-COVID19 are presented, as part of a special call owing to help in the COVID19 crisis in Catalonia. The technological infrastructure and methodology developed in this project allows the quick screening of a territory for a quick a reliable diagnosis in front of an unexpected situation by providing relevant decisional information to support informed decision-making and strategy and policy design. One of the challenges of the project was to extract valuable information from direct participatory processes where specific target profiles of citizens are consulted and to distribute the participation along the whole territory. Having a lot of variables with a moderate number of citizens involved (in this case about 1000) implies the risk of violating statistical secrecy when multivariate relationships are analyzed, thus putting in risk the anonymity of the participants as well as their safety when vulnerable populations are involved, as is the case of INSESS-COVID19. In this paper, the entire data-driven methodology developed in the project is presented and the dealing of the small subgroups of population for statistical secrecy preserving described. The methodology is reusable with any other underlying questionnaire as the data science and reporting parts are totally automatized.

applied sciences

The INSESS-COVID19 Project. Evaluating the Impact of the COVID19 in Social Vulnerability While Preserving Privacy of Participants from Minority Subpopulations