Modeling the Novel Coronavirus (SARS-CoV-2) Outbreak in Sicily, Italy

The continued spread of COVID-19 suggests a significant possibility of reimposing the lockdowns and stricter social distancing similar to the early phase of pandemic control. We present a dynamic model to quantify the impact of isolation for the contagion curves. The model is calibrated to the COVID-19 outbreak in Spain to study the effects of the isolation enforcement following the declaration of the state of alarm (14 March 2020). The simulations indicate that both the timing and the intensity of the isolation enforcement are crucial for the COVID-19 spread. For example, a 4-day earlier intervention for social distancing would have reduced the number of COVID-19 infected people by 67%. The model also informs us that the isolation enforcement does not delay the peak day of the epidemic but slows down its end. When relaxing social distancing, a reduction of the contagion probability (with the generalization of preventive actions, such as face mask wearing and hands sanitizing) is needed to overcome the effect of a rise in the number of interpersonal encounters. We report a threshold level for the contagion pace to avoid a second COVID-19 outbreak in Spain.

Section: Introductionmentioning

confidence: 99%

The Timing and Intensity of Social Distancing to Flatten the COVID-19 Curve: The Case of Spain

Casares

Khan

2020

“…Lately, besides self-organizing models, a variety of numerous other modeling (lattice model, network model, mathematical model, etc.) approaches regarding COVID-19 epidemics have been developed [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]; among these, the compartmental SIR (Susceptible, Infectious, or Recovered) models exhibit very interesting results, which are discussed below in Section 4 .…”

Section: Introductionmentioning

confidence: 99%

A Universal Physics-Based Model Describing COVID-19 Dynamics in Europe

Contoyiannis

Stavrinides

Hanias

et al. 2020

The self-organizing mechanism is a universal approach that is widely followed in nature. In this work, a novel self-organizing model describing diffusion over a lattice is introduced. Simulation results for the model’s active lattice sites demonstrate an evolution curve that is very close to those describing the evolution of infected European populations by COVID-19. The model was further examined against real data regarding the COVID-19 epidemic for seven European countries (with a total population of 290 million) during the periods in which social distancing measures were imposed, namely Italy and Spain, which had an enormous spread of the disease; the successful case of Greece; and four central European countries: France, Belgium, Germany and the Netherlands. The value of the proposed model lies in its simplicity and in the fact that it is based on a universal natural mechanism, which through the presentation of an equivalent dynamical system apparently documents and provides a better understanding of the dynamical process behind viral epidemic spreads in general—even pandemics, such as in the case of COVID-19—further allowing us to come closer to controlling such situations. Finally, this model allowed the study of dynamical characteristics such as the memory effect, through the autocorrelation function, in the studied epidemiological dynamical systems.

“…Among different compartment models, the Susceptible-Infectious-Removed (SIR) model is the simplest model and provides the most primary transmission principles of all compartmental models [ 30 ]. Therefore, the SIR model and its extended versions have been widely used to describe the epidemic pattern of infectious diseases [ 31 , 32 , 33 , 34 , 35 , 36 ]. One basic parameter to describe the dynamics of an epidemic is the reproduction number,

, which represents the average number of secondary cases caused by one case in a large susceptible population.…”

Section: Introductionmentioning

confidence: 99%

Modeling of the Long-Term Epidemic Dynamics of COVID-19 in the United States

Huang

Tao

et al. 2021

Coronavirus 2019 (COVID-19) is causing a severe pandemic that has resulted in millions of confirmed cases and deaths around the world. In the absence of effective drugs for treatment, non-pharmaceutical interventions are the most effective approaches to control the disease. Although some countries have the pandemic under control, all countries around the world, including the United States (US), are still in the process of controlling COVID-19, which calls for an effective epidemic model to describe the transmission dynamics of COVID-19. Meeting this need, we have extensively investigated the transmission dynamics of COVID-19 from 22 January 2020 to 14 February 2021 for the 50 states of the United States, which revealed the general principles underlying the spread of the virus in terms of intervention measures and demographic properties. We further proposed a time-dependent epidemic model, named T-SIR, to model the long-term transmission dynamics of COVID-19 in the US. It was shown in this paper that our T-SIR model could effectively model the epidemic dynamics of COVID-19 for all 50 states, which provided insights into the transmission dynamics of COVID-19 in the US. The present study will be valuable to help understand the epidemic dynamics of COVID-19 and thus help governments determine and implement effective intervention measures or vaccine prioritization to control the pandemic.