Giordano2020 - SIDARTHE model of COVID-19 spread in Italy

View the 2020-09 Model of the Month entry for this model
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Model Identifier
BIOMD0000000955
Short description
In Italy, 128,948 confirmed cases and 15,887 deaths of people who tested positive for SARS-CoV-2 were registered as of 5 April 2020. Ending the global SARS-CoV-2 pandemic requires implementation of multiple population-wide strategies, including social distancing, testing and contact tracing. We propose a new model that predicts the course of the epidemic to help plan an effective control strategy. The model considers eight stages of infection: susceptible (S), infected (I), diagnosed (D), ailing (A), recognized (R), threatened (T), healed (H) and extinct (E), collectively termed SIDARTHE. Our SIDARTHE model discriminates between infected individuals depending on whether they have been diagnosed and on the severity of their symptoms. The distinction between diagnosed and non-diagnosed individuals is important because the former are typically isolated and hence less likely to spread the infection. This delineation also helps to explain misperceptions of the case fatality rate and of the epidemic spread. We compare simulation results with real data on the COVID-19 epidemic in Italy, and we model possible scenarios of implementation of countermeasures. Our results demonstrate that restrictive social-distancing measures will need to be combined with widespread testing and contact tracing to end the ongoing COVID-19 pandemic.
Format
SBML (L3V1)
Related Publication
  • Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy.
  • Giordano G, Blanchini F, Bruno R, Colaneri P, Di Filippo A, Di Matteo A, Colaneri M
  • Nature medicine , 6/ 2020 , Volume 26 , Issue 6 , pages: 855-860 , PubMed ID: 32322102
  • Department of Industrial Engineering, University of Trento, Trento, Italy. giulia.giordano@unitn.it.
  • In Italy, 128,948 confirmed cases and 15,887 deaths of people who tested positive for SARS-CoV-2 were registered as of 5 April 2020. Ending the global SARS-CoV-2 pandemic requires implementation of multiple population-wide strategies, including social distancing, testing and contact tracing. We propose a new model that predicts the course of the epidemic to help plan an effective control strategy. The model considers eight stages of infection: susceptible (S), infected (I), diagnosed (D), ailing (A), recognized (R), threatened (T), healed (H) and extinct (E), collectively termed SIDARTHE. Our SIDARTHE model discriminates between infected individuals depending on whether they have been diagnosed and on the severity of their symptoms. The distinction between diagnosed and non-diagnosed individuals is important because the former are typically isolated and hence less likely to spread the infection. This delineation also helps to explain misperceptions of the case fatality rate and of the epidemic spread. We compare simulation results with real data on the COVID-19 epidemic in Italy, and we model possible scenarios of implementation of countermeasures. Our results demonstrate that restrictive social-distancing measures will need to be combined with widespread testing and contact tracing to end the ongoing COVID-19 pandemic.
Contributors
Kausthubh Ramachandran

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Curated

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Model files

Giordano2020.xml SBML L3V1 file of SIDARTHE model of COVID-19 spread in Italy 91.16 KB Preview | Download

Additional files

Giordano2020.sedml SED-ML file of SIDARTHE model of COVID-19 spread in Italy 3.99 KB Preview | Download
Giordano2020.cps CPS file created on COPASI version 4.27 (Build 217) of SIDARTHE model of COVID-19 spread in Italy 113.12 KB Preview | Download
Giordano2020.omex COMBINE archive of SIDARTHE model of COVID-19 spread in Italy 17.09 KB Preview | Download

  • Model originally submitted by : Kausthubh Ramachandran
  • Submitted: 29-Jul-2020 11:15:11
  • Last Modified: 05-Oct-2020 23:04:37
Revisions
  • Version: 10 public model Download this version
    • Submitted on: 05-Oct-2020 23:04:37
    • Submitted by: Kausthubh Ramachandran
    • With comment: Automatically added model identifier BIOMD0000000955
  • Version: 8 public model Download this version
    • Submitted on: 29-Sep-2020 12:25:03
    • Submitted by: Kausthubh Ramachandran
    • With comment: Automatically added model identifier BIOMD0000000955
  • Version: 5 public model Download this version
    • Submitted on: 20-Sep-2020 12:29:43
    • Submitted by: Kausthubh Ramachandran
    • With comment: Automatically added model identifier BIOMD0000000955
  • Version: 3 public model Download this version
    • Submitted on: 29-Jul-2020 11:15:11
    • Submitted by: Kausthubh Ramachandran
    • With comment: Automatically added model identifier BIOMD0000000955

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Curator's comment:
(added: 29 Jul 2020, 11:12:59, updated: 05 Oct 2020, 23:03:41)
This is a model created on COPASI 4.27 (Build 217) which reproduces the Figures 2b, 2d, 3b, 3d, 4b, 4d in the article. The y-axis in the uploaded images represent cases as a fraction of the total population. To reproduce Fig 2b, set Event_trigger_Fig3b = 0, Event_trigger_Fig3d = 0, Event_trigger_Fig4b = 0, Event_trigger_Fig4d = 0, epsilon_modifer = 1, alpha_modifer = 1 and run for t = 45 d To reproduce Fig 2b, set Event_trigger_Fig3b = 0, Event_trigger_Fig3d = 0, Event_trigger_Fig4b = 0, Event_trigger_Fig4d = 0, epsilon_modifer = 1, alpha_modifer = 1 and run for t = 350 d Set alpha_modifier = 0 for the remaining 4 cases To reproduce Fig 3b, set Event_trigger_Fig3b = 1, Event_trigger_Fig3d = 0, Event_trigger_Fig4b = 0, Event_trigger_Fig4d = 0, epsilon_modifer = 1 and run for t = 350 days. To reproduce Fig 3d, set Event_trigger_Fig3b = 0, Event_trigger_Fig3d = 1, Event_trigger_Fig4b = 0, Event_trigger_Fig4d = 0, epsilon_modifer = 1 and run for t = 350 days. To reproduce Fig 4b, set Event_trigger_Fig3b = 0, Event_trigger_Fig3d = 0, Event_trigger_Fig4b = 1, Event_trigger_Fig4d = 0, epsilon_modifer = 0 and run for t = 350 days. To reproduce Fig 4d, set Event_trigger_Fig3b = 0, Event_trigger_Fig3d = 0, Event_trigger_Fig4b = 0, Event_trigger_Fig4d = 1, epsilon_modifer = 0 and run for t = 350 days.