2020/11/22 by Nicola Parolini, Giovanni Ardenghi, Parolini, Nicola +6
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Medicine · #Anomaly Detection Techniques and Applications #Applications (stat.AP) #COVID-19 diagnosis using AI #COVID-19 epidemiological studies #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Mathematics #Numerical Analysis (math.NA) #Populations and Evolution (q-bio.PE) #cs.NA #math.NA #q-bio.PE #stat.AP
paper · pdf · doi:10.48550/arxiv.2011.11109
openalex publication_date 2020/11/22 · arxiv created 2021/05/24 · arxiv updated 2021/05/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
An analysis of the COVID-19 epidemic is proposed on the basis of the epiMOX dashboard (publicly accessible at https://www.epimox.polimi.it) that deals with data of the epidemic trends and outbreaks in Italy from late February 2020. Our analysis provides an immediate appreciation of the past epidemic development, together with its current trends by fostering a deeper interpretation of available data through several critical epidemic indicators. In addition, we complement the epiMOX dashboard with a predictive tool based on an epidemiological compartmental model, named SUIHTER, for the forecast on the near future epidemic evolution.