Three Mathematical Models for COVID-19 Prediction

نویسندگان

چکیده

The COVID-19 outbreak was a major event that greatly impacted the economy and health systems around world. Understanding behavior of virus being able to perform long-term short-term future predictions daily new cases is working field for machine learning methods mathematical models. This paper compares Verhulst’s, Gompertz´s, SIR models from point view their efficiency describe in Spain. These are used predict pandemic by first solving corresponding inverse problems identify model parameters each wave separately, using as observed data past. posterior distributions then inferred via Metropolis–Hastings algorithm, comparing robustness prediction making different representations visualize results obtained concerning distribution predictions. knowledge acquired about evolution both number infected total during wave. As main conclusion, predictive incomplete without uncertainty analysis problem. invariance output (posterior prediction) with respect forward shows methodology shown this can be adopt decisions real practice (public health).

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ژورنال

عنوان ژورنال: Mathematics

سال: 2023

ISSN: ['2227-7390']

DOI: https://doi.org/10.3390/math11030506