A particle swarm optimization approach for predicting the number of COVID-19 deaths

Abstract The rapid spread of the COVID-19 pandemic has raised huge concerns about the prospect of a major health disaster that would result in a huge number of deaths. This anxiety was largely fueled by the fact that the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for t...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Mohamed Haouari, Mariem Mhiri
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e630d2f8dd8a4100a5e000ef039537d5
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:e630d2f8dd8a4100a5e000ef039537d5
record_format	dspace
spelling	oai:doaj.org-article:e630d2f8dd8a4100a5e000ef039537d52021-12-02T18:51:46ZA particle swarm optimization approach for predicting the number of COVID-19 deaths10.1038/s41598-021-96057-52045-2322https://doaj.org/article/e630d2f8dd8a4100a5e000ef039537d52021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96057-5https://doaj.org/toc/2045-2322Abstract The rapid spread of the COVID-19 pandemic has raised huge concerns about the prospect of a major health disaster that would result in a huge number of deaths. This anxiety was largely fueled by the fact that the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for the disease, was so far unknown, and therefore an accurate prediction of the number of deaths was particularly difficult. However, this prediction is of the utmost importance for public health authorities to make the most reliable decisions and establish the necessary precautions to protect people’s lives. In this paper, we present an approach for predicting the number of deaths from COVID-19. This approach requires modeling the number of infected cases using a generalized logistic function and using this function for inferring the number of deaths. An estimate of the parameters of the proposed model is obtained using a Particle Swarm Optimization algorithm (PSO) that requires iteratively solving a quadratic programming problem. In addition to the total number of deaths and number of infected cases, the model enables the estimation of the infection fatality rate (IFR). Furthermore, using some mild assumptions, we derive estimates of the number of active cases. The proposed approach was empirically assessed on official data provided by the State of Qatar. The results of our computational study show a good accuracy of the predicted number of deaths.Mohamed HaouariMariem MhiriNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mohamed Haouari Mariem Mhiri A particle swarm optimization approach for predicting the number of COVID-19 deaths
description	Abstract The rapid spread of the COVID-19 pandemic has raised huge concerns about the prospect of a major health disaster that would result in a huge number of deaths. This anxiety was largely fueled by the fact that the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for the disease, was so far unknown, and therefore an accurate prediction of the number of deaths was particularly difficult. However, this prediction is of the utmost importance for public health authorities to make the most reliable decisions and establish the necessary precautions to protect people’s lives. In this paper, we present an approach for predicting the number of deaths from COVID-19. This approach requires modeling the number of infected cases using a generalized logistic function and using this function for inferring the number of deaths. An estimate of the parameters of the proposed model is obtained using a Particle Swarm Optimization algorithm (PSO) that requires iteratively solving a quadratic programming problem. In addition to the total number of deaths and number of infected cases, the model enables the estimation of the infection fatality rate (IFR). Furthermore, using some mild assumptions, we derive estimates of the number of active cases. The proposed approach was empirically assessed on official data provided by the State of Qatar. The results of our computational study show a good accuracy of the predicted number of deaths.
format	article
author	Mohamed Haouari Mariem Mhiri
author_facet	Mohamed Haouari Mariem Mhiri
author_sort	Mohamed Haouari
title	A particle swarm optimization approach for predicting the number of COVID-19 deaths
title_short	A particle swarm optimization approach for predicting the number of COVID-19 deaths
title_full	A particle swarm optimization approach for predicting the number of COVID-19 deaths
title_fullStr	A particle swarm optimization approach for predicting the number of COVID-19 deaths
title_full_unstemmed	A particle swarm optimization approach for predicting the number of COVID-19 deaths
title_sort	particle swarm optimization approach for predicting the number of covid-19 deaths
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e630d2f8dd8a4100a5e000ef039537d5
work_keys_str_mv	AT mohamedhaouari aparticleswarmoptimizationapproachforpredictingthenumberofcovid19deaths AT mariemmhiri aparticleswarmoptimizationapproachforpredictingthenumberofcovid19deaths AT mohamedhaouari particleswarmoptimizationapproachforpredictingthenumberofcovid19deaths AT mariemmhiri particleswarmoptimizationapproachforpredictingthenumberofcovid19deaths
_version_	1718377418890149888

A particle swarm optimization approach for predicting the number of COVID-19 deaths

Ejemplares similares