1.
2022 Ieee International Geoscience and Remote Sensing Symposium (Igarss 2022)
; : 7705-7708, 2022.
Article
in English
| Web of Science | ID: covidwho-2311271
ABSTRACT
Links between environmental conditions (e.g., meteorological factors and air quality) and COVID-19 infection/mortality have been reported worldwide. However, the existing statistical frameworks are insufficient to investigate the factors that increase the risk for COVID-19 in urban areas. In this paper, we extend the concept of machine learning-based predictive modelling for COVID-19 spread, proposing an explainable AI approach in order to i) prioritize the risk factors, ii) define the interconnections between them and iii) detect positive or negative influence of the factors with respect to COVID-19 morbidity and mortality.