eprintid: 4076
rev_number: 13
eprint_status: archive
userid: 69
dir: disk0/00/00/40/76
datestamp: 2020-04-16 09:53:42
lastmod: 2022-12-20 15:03:26
status_changed: 2020-04-16 10:06:09
type: monograph
metadata_visibility: show
creators_name: Paggi, Marco
creators_id: marco.paggi@imtlucca.it
title: Simulation of Covid-19 epidemic evolution:
are compartmental models really predictive?
ispublished: pub
subjects: HA
subjects: TJ
divisions: CSA
full_text_status: public
monograph_type: imt_cs_techninal_report
keywords: SARS-CoV-2; Covid-19; Epidemic evolution simulation
abstract: Computational models for the simulation ofthe severe acute respiratory syndrome
coronavirus 2 (SARS-CoV-2) epidemic evolution would be extremely useful to
support authorities in designing healthcare policies and lockdown measures to
contain its impact on public health and economy. In Italy, the devised forecasts
have been mostly based on a pure data-driven approach, by fitting and
extrapolating open data on the epidemic evolution collected by the Italian Civil
Protection Center. In this respect, SIR epidemiological models, which start from the
description of the nonlinear interactions between population compartments,
would be a much more desirable approach to understand and predict the collective
emergent response. The present contribution addresses the fundamental question
whether a SIR epidemiological model, suitably enriched with asymptomatic and
dead individual compartments, could be able to provide reliable predictions on the
epidemic evolution. To this aim, a machine learning approach based on particle
swarm optimization (PSO) is proposed to automatically identify the model
parameters based on a training set of data of progressive increasing size,
considering Lombardy in Italy as a case study. The analysis of the scatter in the
forecasts shows that model predictions are quite sensitive to the size of the dataset
used for training, and that further data are still required to achieve convergent -
and therefore reliable- predictions.
date: 2020-04-16
date_type: published
number: 11
publisher: IMT School for Advanced Studies Lucca
place_of_pub: Lucca
pages: 15
institution: IMT School for Advanced Studies Lucca
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citation:   Paggi, Marco  Simulation of Covid-19 epidemic evolution: are compartmental models really predictive?  CSA Technical Report  #11/2020    IMT School for Advanced Studies Lucca , Lucca.        
document_url: http://eprints.imtlucca.it/4076/1/TR_11_2020_Paggi.pdf