ArticlePhysica. B, Condensed matter2022
Non-extensive thermodynamic entropy to predict the dynamics behavior of COVID-19.
Article in Physica. B, Condensed matter, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 5 citations in OpenAlex.
- Prediction of the morbidity and mortality rates of COVID-19 in Egypt using non-extensive statistics.Scientific reports · 2023Article
- Fuzzy Clustering Methods to Identify the Epidemiological Situation and Its Changes in European Countries during COVID-19.Entropy (Basel, Switzerland) · 2021Article
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3 authors at 1 institution in 1 country.
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Abstract
The current world observations in COVID-19 are hardly tractable as a whole, making situations of information to be incompleteness. In pandemic era, mathematical modeling helps epidemiological scientists to take informing decisions about pandemic planning and predict the disease behavior in the future. In this work, we proposed a non-extensive entropy-based model on the thermodynamic approach for predicting the dynamics of COVID-19 disease. To do so, the epidemic details were considered into a single and time-dependent coefficients model. Their four constraints, including the existence of a maximum point were determined analytically. The model was worked out to give a log-normal distribution for the spread rate using the Tsallis entropy. The width of the distribution function was characterized by maximizing the rate of entropy production. The model predicted the number of daily cases and daily deaths with a fairly good agreement with the World Health Organization (WHO) reported data for world-wide, Iran and China over 2019-2020-time span. The proposed model in this work can be further calibrated to fit on different complex distribution COVID-19 data over different range of times.
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