Estimation of epidemiological parameters for COVID-19 cases using a stochastic SEIRS epidemic model with vital dynamics

Abstract

We estimate and analyze the time-dependent parameters: transmission rate, symptomatic recovery rate, immunity rate, infection noise intensities, and the effective reproduction number for the United States COVID-19 cases for the period 01/22/2020-02/25/2021 using an innovative generalized method of moments estimation scheme. We assume the disease-dynamics is described by a stochastic susceptible-exposed-infected-recovered-susceptible (SEIRS) epidemic model, where the infected class is divided into the asymptomatic infected, and symptomatic infectious classes. Stochasticity appears in the model due to fluctuations in the disease’s transmission and recovery rates. The disease eradication threshold is derived from the reproduction number. The estimated parameters are used to model the disease outbreak’s possible trajectories. Our analysis reveals that current interventions are having positive effects on the transmission and recovery rates. The analysis is demonstrated using the daily United States COVID-19 infection and recovered cases for the period: 01/22/2020-02/25/2021.

Stationary distribution and probability density for a stochastic SEIR-type model of coronavirus (COVID-19) with asymptomatic carriers

“…At present, this disease has drawn much attention of researchers from all over the world and different areas. To defeat the epidemic, many researchers have made great attempts to develop various mathematical models for the transmission dynamics of this epidemic [1] , [4] , [5] , [6] , [7] , [8] , these models include the SEIR-type model and SEIRS-type model. In these models, the authors assumed that after a latent period, exposed individuals can show symptoms that make them easy to identify and isolate, thus they are not able to transmit the disease.…”

“…However, some parameters involved in epidemic models are always affected by the environmental noise. There has been a growing interest in considering the environmental noise in epidemiology models since it has turned out that the stochastic model can describe biological phenomena and infectious diseases in a more exact way [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] . So far, there are different ways to introduce stochastic perturbations in the model.…”

Impact on energy mix incorporated with nuclear energy by the Coronavirus Disease 2019 (COVID-19) for the post-Corona era in the case of South Korea

“…In the study, for the mathematical modeling of the disease pandemic phenomena, Susceptible - Exposed - Infectious - Recovered (SEIR) model is applied to the energy sectors [ [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] ]. Especially, the nuclear energy is one of the major portioned energy sources in the nation's energy industry.…”

Stationary distribution and probability density for a stochastic SEIR-type model of coronavirus(COVID-19) with asymptomatic carriers

“…However, some parameters involved in epidemic models are always affected by the environmental noise. There has been a growing interest in considering the environmental noise in epidemiology models since it has turned out that the stochastic model can describe biological phenomena and infectious diseases in a more exact way [8,9,10,11,12,13,14]. So far, there are different ways to introduce stochastic perturbations in the model.…”

“…At present, this disease has drawn much attention of researchers from all over the world and different areas. To defeat the epidemic, many researchers have made great attempts to develop various mathematical models for the transmission dynamics of this epidemic [1,4,5,6,7,8], these models include the SEIR-type model and SEIRS-type model. In these models, the authors assumed that after a latent period, exposed individuals can show symptoms that makes them easy to identify and isolate, thus they are not able to transmit the disease.…”