Estimation of a state of Corona 19 epidemic in August 2020 by multistage logistic model: a case of EU, USA, and World

Abstract

The article provides an estimate of the size and duration of the Covid-19 epidemic in August 2020 for the European Union (EU), the United States (US), and the World using a multistage logistical epidemiological model.

An Improved Data Driven Dynamic SIRD Model for Predictive Monitoring of COVID-19

“…The number of Gaussian (or LGM) waves and the parameters a i , b i , c i (or K i , A i , r i , τ i ) for each wave are estimated by the minimization of the objective function (with τ 1 = 0) given by the sum of squares for residuals of values [13,14]. The minimization process uses the simplex search method in order to estimate the optimal values of the unknown parameters.…”

NLSIG-COVID19Lab: A modern logistic-growth tool (nlogistic-sigmoid) for descriptively modelling the dynamics of the COVID-19 pandemic process

“…However, it has been noted (Christopoulos, 2020) that the SEIRD-variant models yield largely exaggerated forecasts. Observing the current state of the COVID-19 pandemic, this is concern is borne out, in the results of various applications of logistic modelling (Batista, 2020;Wu et al, 2020) that have largely led to erroneous assessments of the epidemic's progress and its future projection, leading policymakers astray (Matthew, 2020). Notably, two recurring limitations of the logistic definitions in the literature and other software packages exist.…”

“…The second is that during optimization, estimation of the logistic hyperparameters for the individual logistic-sigmoids that make the multiple logistic-sigmoid sum are computed separately, instead of as a unified function. The effect of this is that as the number of logistic-sigmoids considered in the sum increases, regression analysis becomes more cumbersome and complicated, as can be observed in a number of works (Batista, 2020;Chowell et al, 2019;Hsieh & Cheng, 2006;Lee et al, 2020;Taylor & Letham, 2018;Wu et al, 2020).…”

A comparative study for predictive monitoring of COVID-19 pandemic

“…It is carried out majorly using two popular approaches. The first approach includes compartmental models such as SIR, SIRD, SEIR models [6] and the second is based on time-series learning methods such as curve-fitting [7] , [8] , autoregression [9] , [10] , and deep learning on time-series data [11] , [12] .…”

“…However, fitting the data with only one wave may be incorrect since, in general, there are several recurring waves that emerge and die throughout the epidemic duration [22] . To overcome this drawback, some recent works have decomposed the available data into multiple overlapping waves, where every single wave is a generalized growth model such as the logistic or the Gaussian growth models [7] , [8] , [14] . This is to note that both the compartmental and the curve fitting approaches are model-driven and require the estimation of parameters of some predefined mathematical model.…”