Spatio-temporal predictive modeling framework for infectious disease spread

Abstract A novel predictive modeling framework for the spread of infectious diseases using high-dimensional partial differential equations is developed and implemented. A scalar function representing the infected population is defined on a high-dimensional space and its evolution over all the direct...

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Autores principales:	Sashikumaar Ganesan, Deepak Subramani
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/61735942d474410586d4d07cb189dab4
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id	oai:doaj.org-article:61735942d474410586d4d07cb189dab4
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spelling	oai:doaj.org-article:61735942d474410586d4d07cb189dab42021-12-02T14:02:54ZSpatio-temporal predictive modeling framework for infectious disease spread10.1038/s41598-021-86084-72045-2322https://doaj.org/article/61735942d474410586d4d07cb189dab42021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86084-7https://doaj.org/toc/2045-2322Abstract A novel predictive modeling framework for the spread of infectious diseases using high-dimensional partial differential equations is developed and implemented. A scalar function representing the infected population is defined on a high-dimensional space and its evolution over all the directions is described by a population balance equation (PBE). New infections are introduced among the susceptible population from a non-quarantined infected population based on their interaction, adherence to distancing norms, hygiene levels and any other societal interventions. Moreover, recovery, death, immunity and all aforementioned parameters are modeled on the high-dimensional space. To epitomize the capabilities and features of the above framework, prognostic estimates of Covid-19 spread using a six-dimensional (time, 2D space, infection severity, duration of infection, and population age) PBE is presented. Further, scenario analysis for different policy interventions and population behavior is presented, throwing more insights into the spatio-temporal spread of infections across duration of disease, infection severity and age of the population. These insights could be used for science-informed policy planning.Sashikumaar GanesanDeepak SubramaniNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sashikumaar Ganesan Deepak Subramani Spatio-temporal predictive modeling framework for infectious disease spread
description	Abstract A novel predictive modeling framework for the spread of infectious diseases using high-dimensional partial differential equations is developed and implemented. A scalar function representing the infected population is defined on a high-dimensional space and its evolution over all the directions is described by a population balance equation (PBE). New infections are introduced among the susceptible population from a non-quarantined infected population based on their interaction, adherence to distancing norms, hygiene levels and any other societal interventions. Moreover, recovery, death, immunity and all aforementioned parameters are modeled on the high-dimensional space. To epitomize the capabilities and features of the above framework, prognostic estimates of Covid-19 spread using a six-dimensional (time, 2D space, infection severity, duration of infection, and population age) PBE is presented. Further, scenario analysis for different policy interventions and population behavior is presented, throwing more insights into the spatio-temporal spread of infections across duration of disease, infection severity and age of the population. These insights could be used for science-informed policy planning.
format	article
author	Sashikumaar Ganesan Deepak Subramani
author_facet	Sashikumaar Ganesan Deepak Subramani
author_sort	Sashikumaar Ganesan
title	Spatio-temporal predictive modeling framework for infectious disease spread
title_short	Spatio-temporal predictive modeling framework for infectious disease spread
title_full	Spatio-temporal predictive modeling framework for infectious disease spread
title_fullStr	Spatio-temporal predictive modeling framework for infectious disease spread
title_full_unstemmed	Spatio-temporal predictive modeling framework for infectious disease spread
title_sort	spatio-temporal predictive modeling framework for infectious disease spread
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/61735942d474410586d4d07cb189dab4
work_keys_str_mv	AT sashikumaarganesan spatiotemporalpredictivemodelingframeworkforinfectiousdiseasespread AT deepaksubramani spatiotemporalpredictivemodelingframeworkforinfectiousdiseasespread
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Spatio-temporal predictive modeling framework for infectious disease spread

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