Abstract
We applied a generalized SEIR epidemiological model to the recent SARS-CoV-2 outbreak in the world, with a focus on Italy and its Lombardy, Piedmont, and Veneto regions. We focused on the application of a stochastic approach in fitting the model parameters using a Particle Swarm Optimization (PSO) solver, to improve the reliability of predictions in the medium term (30 days). We analyzed the official data and the predicted evolution of the epidemic in the Italian regions, and we compared the results with the data and predictions of Spain and South Korea. We linked the model equations to the changes in people’s mobility, with reference to Google’s COVID-19 Community Mobility Reports. We discussed the effectiveness of policies taken by different regions and countries and how they have an impact on past and future infection scenarios.
Highlights
We present an updated version of the predictive model of epidemic phenomena based on the approach called SEIR (Susceptible-Exposed-Infective-Recovered), widely used to analyze infection data during the different stages of an epidemic outbreak
We present the time series obtained by the standard deterministic approach and the data series obtained by the stochastic approach, based on the Particle Swarm Optimization (PSO) algorithm
We adopted a generalized SEIR model to offer a quantitative overview of the complex analysis of
Summary
We present an updated version of the predictive model of epidemic phenomena based on the approach called SEIR (Susceptible-Exposed-Infective-Recovered), widely used to analyze infection data during the different stages of an epidemic outbreak. The SEIR model represents one of the most adopted mathematical models to characterize the epidemic dynamics and to predict possible contagion scenarios. The SEIR model can be useful to assess the effectiveness of various measures, such as lock-down, since the infectious disease outbreak. It is based on a series of dynamic ordinary differential equations that consider the amount of the population subject to contagion, the trend over time of individuals who recover after infection, and the individuals who die [1]. We are aware that any political decision potentially lacking any rational and critical evaluations of all the available data risks being based on mere sensations, often dictated by sentimental suggestions [2]
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