We propose a pipeline for the stochastic analysis of a SIR model for COVID-19 through the stochastic model checker PRISM. The pipeline consists in: (i) the definition of a modified SIR model, able to include governmental restriction and prevention measures through an additional time-dependent coefficient; (ii) parameter estimation based on real epidemic data; (iii) translation of the modified SIR model into a Continuous Time Markov Chain (CTMC) expressed using the PRISM input language; and (iv) stochastic analysis (simulation and model checking) with PRISM.
Analysis of COVID-19 Data with PRISM: Parameter Estimation and SIR Modelling
Milazzo P.
2021-01-01
Abstract
We propose a pipeline for the stochastic analysis of a SIR model for COVID-19 through the stochastic model checker PRISM. The pipeline consists in: (i) the definition of a modified SIR model, able to include governmental restriction and prevention measures through an additional time-dependent coefficient; (ii) parameter estimation based on real epidemic data; (iii) translation of the modified SIR model into a Continuous Time Markov Chain (CTMC) expressed using the PRISM input language; and (iv) stochastic analysis (simulation and model checking) with PRISM.File in questo prodotto:
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