Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy
By G. Giordano, Franco Blanchini, R. Bruno, Patrizio Colaneri, A. Di Filippo, A. D. Di Matteo, M. Colaneri. Nature Medicine, 2020
1,687 citations recorded by the source corpus.
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. A new epidemiological model, termed SIDARTHE, distinguishes between diagnosed and undiagnosed cases of SARS-CoV-2 infection, as well as modeling effects of social distancing and widespread testing, to predict possible outcomes of the COVID-19 epidemic in Italy.
Subfields: Infectious Diseases, Modeling and Simulation
View Paper: https://www.semanticscholar.org/paper/7e2719064be04fd488be75a358c40fc717730faf
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Citations in the corpus
- A SIR model assumption for the spread of COVID-19 in different communities (2020, positive): “They provide us with useful predictions in the context of the impact of intervention in decreasing the number of infected-susceptible incidence rates [30, 31, 32].”
- COVID-ABS: An agent-based model of COVID-19 epidemic to simulate health and economic effects of social distancing interventions (2020, neutral): “There is also a Dead status, since part of the population dies due to the disease and its complications [59].”
- Covasim: An agent-based model of COVID-19 dynamics and interventions (2020, background): “(8) and Zhao and Chen (9), compartments are further divided to provide more nuance in simulating progression through different disease states, and have been deployed to study the effects of various population-wide interventions such as social distancing and testing on COVID-19 transmission.”
- Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts (2020, background): “For example, the work in [97] leveraged real datasets collected from the COVID-19 pandemic in Italy to estimate the outbreak possibility which is of significant importance to plan effective disease control strategies.”