Applying an Epidemiological Model to Evaluate the Propagation of Toxicity related to COVID-19 on Twitter

Abstract Summary We present StochSS Live!, a web-based service for modeling, simulation and analysis of a wide range of mathematical, biological and biochemical systems. Using an epidemiological model of COVID-19, we demonstrate the power of StochSS Live! to enable researchers to quickly develop a deterministic or a discrete stochastic model, infer its parameters and analyze the results. Availability and implementation StochSS Live! is freely available at https://live.stochss.org/ Supplementary information Supplementary data are available at Bioinformatics online.

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The stability analysis and numerical simulation based on Sinc Legendre collocation method for solving a fractional epidemiological model of the Ebola virus

Partial Differential Equations in Applied Mathematics ◽

10.1016/j.padiff.2021.100037 ◽

2021 ◽

Vol 3 ◽

pp. 100037

Author(s):

M.H. Derakhshan

Keyword(s):

Numerical Simulation ◽

Stability Analysis ◽

Collocation Method ◽

Ebola Virus ◽

Epidemiological Model ◽

Simulation Based ◽

Legendre Collocation Method ◽

The Stability

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A probabilistic epidemiological model

Model Assisted Statistics and Applications ◽

10.3233/mas-210511 ◽

2021 ◽

Vol 16 (1) ◽

pp. 15-23

Author(s):

Hal M. Switkay

Keyword(s):

United States ◽

United Kingdom ◽

United States Of America ◽

Compartmental Model ◽

The United States ◽

Epidemiological Model ◽

Probabilistic Methods ◽

Infinite Intervals ◽

Best Fit ◽

Family Of Distributions

We construct a model for the progress of the 2020 coronavirus epidemic in the United States of America, using probabilistic methods rather than the traditional compartmental model. We employ the generalized beta family of distributions, including those supported on bounded intervals and those supported on semi-infinite intervals. We compare the best-fit distributions for daily new cases and daily new deaths in America to the corresponding distributions for United Kingdom, Spain, and Italy. We explore how such a model might be justified theoretically in comparison to the apparently more natural compartmental model. We compare forecasts based on these models to observations, and find the forecasts useful in predicting total pandemic deaths.

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