A new logistic growth model applied to COVID-19 fatality data

Background:: Recent work showed that the temporal growth of the novel coronavirus disease (COVID-19) follows a sub-exponential power-law scaling whenever effective control interventions are in place. Taking this into consideration, we present a new phenomenological logistic model that is well-suited...

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Autores principales:	S. Triambak, D.P. Mahapatra, N. Mallick, R. Sahoo
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	COVID-19 Subexponential power-law growth Logistic growth Non-linear least squares Infectious and parasitic diseases RC109-216
Acceso en línea:	https://doaj.org/article/9e6987e514d44062a862f32b715af842
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spelling	oai:doaj.org-article:9e6987e514d44062a862f32b715af8422021-11-10T04:21:41ZA new logistic growth model applied to COVID-19 fatality data1755-436510.1016/j.epidem.2021.100515https://doaj.org/article/9e6987e514d44062a862f32b715af8422021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1755436521000608https://doaj.org/toc/1755-4365Background:: Recent work showed that the temporal growth of the novel coronavirus disease (COVID-19) follows a sub-exponential power-law scaling whenever effective control interventions are in place. Taking this into consideration, we present a new phenomenological logistic model that is well-suited for such power-law epidemic growth. Methods:: We empirically develop the logistic growth model using simple scaling arguments, known boundary conditions and a comparison with available data from four countries, Belgium, China, Denmark and Germany, where (arguably) effective containment measures were put in place during the first wave of the pandemic. A non-linear least-squares minimization algorithm is used to map the parameter space and make optimal predictions. Results:: Unlike other logistic growth models, our presented model is shown to consistently make accurate predictions of peak heights, peak locations and cumulative saturation values for incomplete epidemic growth curves. We further show that the power-law growth model also works reasonably well when containment and lock down strategies are not as stringent as they were during the first wave of infections in 2020. On the basis of this agreement, the model was used to forecast COVID-19 fatalities for the third wave in South Africa, which was in progress during the time of this work. Conclusion:: We anticipate that our presented model will be useful for a similar forecasting of COVID-19 induced infections/deaths in other regions as well as other cases of infectious disease outbreaks, particularly when power-law scaling is observed.S. TriambakD.P. MahapatraN. MallickR. SahooElsevierarticleCOVID-19Subexponential power-law growthLogistic growthNon-linear least squaresInfectious and parasitic diseasesRC109-216ENEpidemics, Vol 37, Iss , Pp 100515- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	COVID-19 Subexponential power-law growth Logistic growth Non-linear least squares Infectious and parasitic diseases RC109-216
spellingShingle	COVID-19 Subexponential power-law growth Logistic growth Non-linear least squares Infectious and parasitic diseases RC109-216 S. Triambak D.P. Mahapatra N. Mallick R. Sahoo A new logistic growth model applied to COVID-19 fatality data
description	Background:: Recent work showed that the temporal growth of the novel coronavirus disease (COVID-19) follows a sub-exponential power-law scaling whenever effective control interventions are in place. Taking this into consideration, we present a new phenomenological logistic model that is well-suited for such power-law epidemic growth. Methods:: We empirically develop the logistic growth model using simple scaling arguments, known boundary conditions and a comparison with available data from four countries, Belgium, China, Denmark and Germany, where (arguably) effective containment measures were put in place during the first wave of the pandemic. A non-linear least-squares minimization algorithm is used to map the parameter space and make optimal predictions. Results:: Unlike other logistic growth models, our presented model is shown to consistently make accurate predictions of peak heights, peak locations and cumulative saturation values for incomplete epidemic growth curves. We further show that the power-law growth model also works reasonably well when containment and lock down strategies are not as stringent as they were during the first wave of infections in 2020. On the basis of this agreement, the model was used to forecast COVID-19 fatalities for the third wave in South Africa, which was in progress during the time of this work. Conclusion:: We anticipate that our presented model will be useful for a similar forecasting of COVID-19 induced infections/deaths in other regions as well as other cases of infectious disease outbreaks, particularly when power-law scaling is observed.
format	article
author	S. Triambak D.P. Mahapatra N. Mallick R. Sahoo
author_facet	S. Triambak D.P. Mahapatra N. Mallick R. Sahoo
author_sort	S. Triambak
title	A new logistic growth model applied to COVID-19 fatality data
title_short	A new logistic growth model applied to COVID-19 fatality data
title_full	A new logistic growth model applied to COVID-19 fatality data
title_fullStr	A new logistic growth model applied to COVID-19 fatality data
title_full_unstemmed	A new logistic growth model applied to COVID-19 fatality data
title_sort	new logistic growth model applied to covid-19 fatality data
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/9e6987e514d44062a862f32b715af842
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A new logistic growth model applied to COVID-19 fatality data

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