2020/04/16 by M. Giudici, Mauro Giudici, Alessandro Comunian +4 · 1 citation
Biochemistry, Genetics and Molecular Biology · Mathematics · Medicine · Physics and Astronomy · #2019-20 coronavirus outbreak #92B05 #Applied mathematics #COVID-19 diagnosis using AI #COVID-19 epidemiological studies #Calibration #Computer science #Coronavirus disease 2019 (COVID-19) #Econometrics #Epidemic model #FOS: Biological sciences #FOS: Physical sciences #Geology #Infectious disease (medical specialty) #Inverse #Inverse problem #Inversion (geology) #Law #Mathematical analysis #Mathematics #Operations research #Outbreak #Pandemic #Physics #Physics and Society (physics.soc-ph) #Political science #Populations and Evolution (q-bio.PE) #Relevance (law) #Reliability (semiconductor) #SARS-CoV-2 and COVID-19 Research #Seismology #Set (abstract data type) #Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) #Sociology #Statistical Mechanics and Entropy #Statistics #Viral Infections and Outbreaks Research #Virology #msc:92B05 #physics.soc-ph #q-bio.PE
paper · pdf · doi:10.48550/arxiv.2004.07738
openalex publication_date 2020/04/16 · arxiv created 2020/06/08 · arxiv updated 2020/06/09 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
Calibration of a SIR (Susceptibles-Infected-Recovered) model with official\ninternational data for the COVID-19 pandemics provides a good example of the\ndifficulties inherent the solution of inverse problems. Inverse modeling is set\nup in a framework of discrete inverse problems, which explicitly considers the\nrole and the relevance of data. Together with a physical vision of the model,\nthe present work addresses numerically the issue of parameters calibration in\nSIR models, it discusses the uncertainties in the data provided by\ninternational authorities, how they influence the reliability of calibrated\nmodel parameters and, ultimately, of model predictions.\n