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COVID-19 multi-state epidemic forecast in India
CLINICAL IMPORTANCE: Novel coronavirus disease is spread worldwide with considerable morbidity and mortality and presents an enormous burden on worldwide public health. Due to the non-stationarity and complicated nature of novel coronavirus waves, it is challenging to model such a phenomenon. Few ma...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Indian National Science Academy
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9910244/ http://dx.doi.org/10.1007/s43538-022-00147-5 |
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author | Gaidai, Oleg Wang, Fang Yakimov, Vladimir |
author_facet | Gaidai, Oleg Wang, Fang Yakimov, Vladimir |
author_sort | Gaidai, Oleg |
collection | PubMed |
description | CLINICAL IMPORTANCE: Novel coronavirus disease is spread worldwide with considerable morbidity and mortality and presents an enormous burden on worldwide public health. Due to the non-stationarity and complicated nature of novel coronavirus waves, it is challenging to model such a phenomenon. Few mathematical models can be used because novel coronavirus data are generally not normally distributed. This paper describes a novel bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of novel coronavirus infection rate. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the advantage of dealing efficiently with extensive regional dimensionality and cross-correlation between infection rate and mortality. OBJECTIVE: To determine extreme novel coronavirus death rate probability at any time in any region of interest. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the advantage of dealing efficiently with extensive regional dimensionality and cross-correlation between different regional observations. DESIGN: Apply modern novel statistical methods directly to raw clinical data. SETTING: Multicenter, population-based, medical survey data based bio statistical approach. MAIN OUTCOME AND MEASURE: Due to the non-stationarity and complicated nature of novel coronavirus, it is challenging to model such a phenomenon. Few mathematical models can be used because novel coronavirus data are generally not normally distributed. This paper describes a novel bio-system reliability approach, particularly suitable for multi-country environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of extreme novel coronavirus death rate probability. CONCLUSIONS AND RELEVANCE: The suggested methodology can be used in various public health applications, based on their clinical survey data. |
format | Online Article Text |
id | pubmed-9910244 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Indian National Science Academy |
record_format | MEDLINE/PubMed |
spelling | pubmed-99102442023-02-10 COVID-19 multi-state epidemic forecast in India Gaidai, Oleg Wang, Fang Yakimov, Vladimir Proc.Indian Natl. Sci. Acad. Research Paper CLINICAL IMPORTANCE: Novel coronavirus disease is spread worldwide with considerable morbidity and mortality and presents an enormous burden on worldwide public health. Due to the non-stationarity and complicated nature of novel coronavirus waves, it is challenging to model such a phenomenon. Few mathematical models can be used because novel coronavirus data are generally not normally distributed. This paper describes a novel bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of novel coronavirus infection rate. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the advantage of dealing efficiently with extensive regional dimensionality and cross-correlation between infection rate and mortality. OBJECTIVE: To determine extreme novel coronavirus death rate probability at any time in any region of interest. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the advantage of dealing efficiently with extensive regional dimensionality and cross-correlation between different regional observations. DESIGN: Apply modern novel statistical methods directly to raw clinical data. SETTING: Multicenter, population-based, medical survey data based bio statistical approach. MAIN OUTCOME AND MEASURE: Due to the non-stationarity and complicated nature of novel coronavirus, it is challenging to model such a phenomenon. Few mathematical models can be used because novel coronavirus data are generally not normally distributed. This paper describes a novel bio-system reliability approach, particularly suitable for multi-country environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of extreme novel coronavirus death rate probability. CONCLUSIONS AND RELEVANCE: The suggested methodology can be used in various public health applications, based on their clinical survey data. Indian National Science Academy 2023-02-09 2023 /pmc/articles/PMC9910244/ http://dx.doi.org/10.1007/s43538-022-00147-5 Text en © Indian National Science Academy 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Research Paper Gaidai, Oleg Wang, Fang Yakimov, Vladimir COVID-19 multi-state epidemic forecast in India |
title | COVID-19 multi-state epidemic forecast in India |
title_full | COVID-19 multi-state epidemic forecast in India |
title_fullStr | COVID-19 multi-state epidemic forecast in India |
title_full_unstemmed | COVID-19 multi-state epidemic forecast in India |
title_short | COVID-19 multi-state epidemic forecast in India |
title_sort | covid-19 multi-state epidemic forecast in india |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9910244/ http://dx.doi.org/10.1007/s43538-022-00147-5 |
work_keys_str_mv | AT gaidaioleg covid19multistateepidemicforecastinindia AT wangfang covid19multistateepidemicforecastinindia AT yakimovvladimir covid19multistateepidemicforecastinindia |