Estimating, monitoring, and forecasting COVID-19 epidemics: a spatiotemporal approach applied to NYC data

Abstract We propose a susceptible-exposed-infective-recovered-type (SEIR-type) meta-population model to simulate and monitor the (COVID-19) epidemic evolution. The basic model consists of seven categories, namely, susceptible (S), exposed (E), three infective classes, recovered (R), and deceased (D)...

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Autores principales:	Vinicius V. L. Albani, Roberto M. Velho, Jorge P. Zubelli
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0d9237327f4042a091c0875700acee01
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spelling	oai:doaj.org-article:0d9237327f4042a091c0875700acee012021-12-02T17:39:19ZEstimating, monitoring, and forecasting COVID-19 epidemics: a spatiotemporal approach applied to NYC data10.1038/s41598-021-88281-w2045-2322https://doaj.org/article/0d9237327f4042a091c0875700acee012021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88281-whttps://doaj.org/toc/2045-2322Abstract We propose a susceptible-exposed-infective-recovered-type (SEIR-type) meta-population model to simulate and monitor the (COVID-19) epidemic evolution. The basic model consists of seven categories, namely, susceptible (S), exposed (E), three infective classes, recovered (R), and deceased (D). We define these categories for n age and sex groups in m different spatial locations. Therefore, the resulting model contains all epidemiological classes for each age group, sex, and location. The mixing between them is accomplished by means of time-dependent infection rate matrices. The model is calibrated with the curve of daily new infections in New York City and its boroughs, including census data, and the proportions of infections, hospitalizations, and deaths for each age range. We finally obtain a model that matches the reported curves and predicts accurate infection information for different locations and age classes.Vinicius V. L. AlbaniRoberto M. VelhoJorge P. ZubelliNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Vinicius V. L. Albani Roberto M. Velho Jorge P. Zubelli Estimating, monitoring, and forecasting COVID-19 epidemics: a spatiotemporal approach applied to NYC data
description	Abstract We propose a susceptible-exposed-infective-recovered-type (SEIR-type) meta-population model to simulate and monitor the (COVID-19) epidemic evolution. The basic model consists of seven categories, namely, susceptible (S), exposed (E), three infective classes, recovered (R), and deceased (D). We define these categories for n age and sex groups in m different spatial locations. Therefore, the resulting model contains all epidemiological classes for each age group, sex, and location. The mixing between them is accomplished by means of time-dependent infection rate matrices. The model is calibrated with the curve of daily new infections in New York City and its boroughs, including census data, and the proportions of infections, hospitalizations, and deaths for each age range. We finally obtain a model that matches the reported curves and predicts accurate infection information for different locations and age classes.
format	article
author	Vinicius V. L. Albani Roberto M. Velho Jorge P. Zubelli
author_facet	Vinicius V. L. Albani Roberto M. Velho Jorge P. Zubelli
author_sort	Vinicius V. L. Albani
title	Estimating, monitoring, and forecasting COVID-19 epidemics: a spatiotemporal approach applied to NYC data
title_short	Estimating, monitoring, and forecasting COVID-19 epidemics: a spatiotemporal approach applied to NYC data
title_full	Estimating, monitoring, and forecasting COVID-19 epidemics: a spatiotemporal approach applied to NYC data
title_fullStr	Estimating, monitoring, and forecasting COVID-19 epidemics: a spatiotemporal approach applied to NYC data
title_full_unstemmed	Estimating, monitoring, and forecasting COVID-19 epidemics: a spatiotemporal approach applied to NYC data
title_sort	estimating, monitoring, and forecasting covid-19 epidemics: a spatiotemporal approach applied to nyc data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0d9237327f4042a091c0875700acee01
work_keys_str_mv	AT viniciusvlalbani estimatingmonitoringandforecastingcovid19epidemicsaspatiotemporalapproachappliedtonycdata AT robertomvelho estimatingmonitoringandforecastingcovid19epidemicsaspatiotemporalapproachappliedtonycdata AT jorgepzubelli estimatingmonitoringandforecastingcovid19epidemicsaspatiotemporalapproachappliedtonycdata
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Estimating, monitoring, and forecasting COVID-19 epidemics: a spatiotemporal approach applied to NYC data

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