A simple SIR model with a large set of asymptomatic infectives

Abstract

There is increasing evidence that one of the most difficult problems in trying to control the ongoing COVID-19 epidemic is the presence of a large cohort of asymptomatic infectives. We develop a SIR-type model taking into account the presence of asymptomatic, or however undetected, infective, and the substantially long time these spend being infective and not isolated. We discuss how a SIR-based prediction of the epidemic course based on early data but not taking into account the presence of a large set of asymptomatic infectives would give wrong estimate of very relevant quantities such as the need of hospital beds, the time to the epidemic peak, and the number of people which are left untouched by the first wave and thus in danger in case of a second epidemic wave. In the second part of the note, we apply our model to the COVID-19 epidemics in Italy. We obtain a good agreement with epidemiological data; according to the best fit of epidemiological data in terms of this model, only 10\% of infectives in Italy is symptomatic.

arXiv commentV4 (hopefully final) contains analysis of data up to May 15, 2020

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• Data-driven deep learning algorithms for time-varying infection rates of COVID-19 and mitigation measures
• Social distancing versus early detection and contacts tracing in epidemic management
• Modelling provincial Covid-19 epidemic data in Italy using an adjusted time-dependent SIRD model
• Simulation of Covid-19 epidemic evolution: are compartmental models really predictive?
• Asymptomatic infectives and $R_0$ for COVID
• Modelling the deceleration of COVID-19 spreading
• How long does a lockdown need to be?
• A Monte Carlo approach to model COVID-19 deaths and infections using Gompertz functions
• Surveillance Testing for Rapid Detection of Outbreaks in Facilities
• Kinetic models for epidemic dynamics with social heterogeneity
• Mass vaccination in a roaring pandemic
• Phase transitions and control measures for network epidemics caused by infections with presymptomatic, asymptomatic,and symptomatic stages
• A data-driven epidemic model with social structure for understanding the COVID-19 infection on a heavily affected Italian Province
• The Limits to Learning a Diffusion Model