| Simulator: tellurium 2.2.8 | |
| CPU cores: 1 | |
| RAM: 8 GB | |
| Max time: 20 m |
| Submitted: 2023-06-07 06:07:32 PM | |
| Updated: 2023-06-07 06:21:15 PM | |
Status: Succeeded |
This is a model created on COPASI 4.27 (Build 217) which reproduces the Figures 2b, 2d, 3b, 3d, 4b, 4d in the article - https://www.nature.com/articles/s41591-020-0883-7To reproduce Fig 2b and 2d, 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 (for Fig 2b) and t = 350 days (for Fig 2d). 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.
Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy Giulia Giordano, Franco Blanchini, Raffaele Bruno, Patrizio Colaneri, Alessandro Di Filippo, Angela Di Matteo and Marta Colaneri Journal - Nature Medicine DOI - 10.1038/s41591-020-0883-7
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.
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