A Novel Approach for Estimating the Final Outcome of Global Diseases Like COVID-19

Abstract

<jats:p>The existence of a universal law which maps the bell curve of daily cases to a sigmoid curve for cumulative ones is used for making robust estimations about the final outcome of a disease. Computations of real time effective reproduction rate are presented and its limited usefulness is derived. After using methods ESE and EDE we are able to find the inflection point of the cumulative curve under consideration and study its time evolution. Since mortality processes tend to follow a Gompertz distribution, we apply the properties of it and introduce novel estimations for both the time remaining after inflection time and the capacity of the curve. Special properties of sigmoid curves are used for assessing the quality of estimation and as indices for the cycle completion. Application is presented for COVID-19 evolution for most affected countries and the World.</jats:p>

Modeling latent infection transmissions through biosocial stochastic dynamics

“…To predict the extent of the infection 33,46,49 , the course of the infection curve is standardly fitted by the sigmoid (logistic) function …”

“…4. Averaging over the infected agents in a given period, we can get a local measure of the infection spreading, a quantity similar to what is in the epidemiology literature known as "R-factor" 46,47 . Our network presentation of the process clearly shows that the R-factor is given by the relative ratio between two successive layers of the Hnods.…”

“…Our network presentation of the process clearly shows that the R-factor is given by the relative ratio between two successive layers of the Hnods. Recently, studies have shown that the reproduction rate is a local measure with a limited predictive value, see the discussion and the empirical data analysed in [46][47][48] .…”

“…6, we show comparative results with/out mutations and two different lockdown scenarios, while keeping the exposure fixed. To predict the extent of the infection 33,46,49 , the course two lines in each case are the scenarios with/without virus mutations. Fits according to the logistic function (see text).…”

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

“…Epidemiological models such as the SEIRD variants (Lee et al, 2020;Okabe & Shudo, 2020) are just another form of representing sigmoidal growth (Xs et al, 2012). 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).…”