Linear Regression Analysis to predict the number of deaths in India due to SARS-CoV-2 at 6 weeks from day 0 (100 cases - March 14th 2020)

Introduction: and Aims: No valid treatment or preventative strategy has evolved till date to counter the SARS CoV 2 (Novel Coronavirus) epidemic that originated in China in late 2019 and have since wrought havoc on millions across the world with illness, socioeconomic recession and death. This analy...

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Autores:
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Universidad de Bogotá Jorge Tadeo Lozano
Repositorio:
Expeditio: repositorio UTadeo
Idioma:
eng
OAI Identifier:
oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/12036
Acceso en línea:
https://doi.org/10.1016/j.dsx.2020.03.017
http://hdl.handle.net/20.500.12010/12036
Palabra clave:
India
Coronavirus
Death rates
Correlation
Regression
Síndrome respiratorio agudo grave
COVID-19
SARS-CoV-2
Coronavirus
Rights
License
Acceso restringido
id UTADEO2_3666049f76484918828c37342f5d587c
oai_identifier_str oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/12036
network_acronym_str UTADEO2
network_name_str Expeditio: repositorio UTadeo
repository_id_str
dc.title.spa.fl_str_mv Linear Regression Analysis to predict the number of deaths in India due to SARS-CoV-2 at 6 weeks from day 0 (100 cases - March 14th 2020)
title Linear Regression Analysis to predict the number of deaths in India due to SARS-CoV-2 at 6 weeks from day 0 (100 cases - March 14th 2020)
spellingShingle Linear Regression Analysis to predict the number of deaths in India due to SARS-CoV-2 at 6 weeks from day 0 (100 cases - March 14th 2020)
India
Coronavirus
Death rates
Correlation
Regression
Síndrome respiratorio agudo grave
COVID-19
SARS-CoV-2
Coronavirus
title_short Linear Regression Analysis to predict the number of deaths in India due to SARS-CoV-2 at 6 weeks from day 0 (100 cases - March 14th 2020)
title_full Linear Regression Analysis to predict the number of deaths in India due to SARS-CoV-2 at 6 weeks from day 0 (100 cases - March 14th 2020)
title_fullStr Linear Regression Analysis to predict the number of deaths in India due to SARS-CoV-2 at 6 weeks from day 0 (100 cases - March 14th 2020)
title_full_unstemmed Linear Regression Analysis to predict the number of deaths in India due to SARS-CoV-2 at 6 weeks from day 0 (100 cases - March 14th 2020)
title_sort Linear Regression Analysis to predict the number of deaths in India due to SARS-CoV-2 at 6 weeks from day 0 (100 cases - March 14th 2020)
dc.subject.spa.fl_str_mv India
Coronavirus
Death rates
Correlation
Regression
topic India
Coronavirus
Death rates
Correlation
Regression
Síndrome respiratorio agudo grave
COVID-19
SARS-CoV-2
Coronavirus
dc.subject.lemb.spa.fl_str_mv Síndrome respiratorio agudo grave
COVID-19
SARS-CoV-2
Coronavirus
description Introduction: and Aims: No valid treatment or preventative strategy has evolved till date to counter the SARS CoV 2 (Novel Coronavirus) epidemic that originated in China in late 2019 and have since wrought havoc on millions across the world with illness, socioeconomic recession and death. This analysis was aimed at tracing a trend related to death counts expected at the 5th and 6th week of the COVID-19 in India. Material and methods: Validated database was used to procure global and Indian data related to coronavirus and related outcomes. Multiple regression and linear regression analyses were used interchangeably. Since the week 6 death count data was not correlated significantly with any of the chosen inputs, an auto-regression technique was employed to improve the predictive ability of the regression model. Results: A linear regression analysis predicted average week 5 death count to be 211 with a 95% CI: 1.31 e2.60). Similarly, week 6 death count, in spite of a strong correlation with input variables, did not pass the test of statistical significance. Using auto-regression technique and using week 5 death count as input the linear regression model predicted week 6 death count in India to be 467, while keeping at the back of our mind the risk of over-estimation by most of the risk-based models. Conclusion: According to our analysis, if situation continue in present state; projected death rate (n) is 211 and467 at the end of the 5th and 6th week from now, respectively.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2020-08-20T19:25:27Z
dc.date.available.none.fl_str_mv 2020-08-20T19:25:27Z
dc.date.created.none.fl_str_mv 2020
dc.type.local.spa.fl_str_mv Artículo
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format http://purl.org/coar/resource_type/c_6501
dc.identifier.issn.spa.fl_str_mv 1871-4021
dc.identifier.other.spa.fl_str_mv https://doi.org/10.1016/j.dsx.2020.03.017
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/20.500.12010/12036
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1016/j.dsx.2020.03.017
identifier_str_mv 1871-4021
url https://doi.org/10.1016/j.dsx.2020.03.017
http://hdl.handle.net/20.500.12010/12036
dc.language.iso.spa.fl_str_mv eng
language eng
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_f1cf
dc.rights.local.spa.fl_str_mv Acceso restringido
rights_invalid_str_mv Acceso restringido
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dc.format.extent.spa.fl_str_mv 5 páginas
dc.format.mimetype.spa.fl_str_mv image/jepg
dc.publisher.spa.fl_str_mv Diabetes & Metabolic Syndrome: Clinical Research & Reviews
dc.source.spa.fl_str_mv reponame:Expeditio Repositorio Institucional UJTL
instname:Universidad de Bogotá Jorge Tadeo Lozano
instname_str Universidad de Bogotá Jorge Tadeo Lozano
institution Universidad de Bogotá Jorge Tadeo Lozano
reponame_str Expeditio Repositorio Institucional UJTL
collection Expeditio Repositorio Institucional UJTL
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spelling 2020-08-20T19:25:27Z2020-08-20T19:25:27Z20201871-4021https://doi.org/10.1016/j.dsx.2020.03.017http://hdl.handle.net/20.500.12010/12036https://doi.org/10.1016/j.dsx.2020.03.017Introduction: and Aims: No valid treatment or preventative strategy has evolved till date to counter the SARS CoV 2 (Novel Coronavirus) epidemic that originated in China in late 2019 and have since wrought havoc on millions across the world with illness, socioeconomic recession and death. This analysis was aimed at tracing a trend related to death counts expected at the 5th and 6th week of the COVID-19 in India. Material and methods: Validated database was used to procure global and Indian data related to coronavirus and related outcomes. Multiple regression and linear regression analyses were used interchangeably. Since the week 6 death count data was not correlated significantly with any of the chosen inputs, an auto-regression technique was employed to improve the predictive ability of the regression model. Results: A linear regression analysis predicted average week 5 death count to be 211 with a 95% CI: 1.31 e2.60). Similarly, week 6 death count, in spite of a strong correlation with input variables, did not pass the test of statistical significance. Using auto-regression technique and using week 5 death count as input the linear regression model predicted week 6 death count in India to be 467, while keeping at the back of our mind the risk of over-estimation by most of the risk-based models. Conclusion: According to our analysis, if situation continue in present state; projected death rate (n) is 211 and467 at the end of the 5th and 6th week from now, respectively.5 páginasimage/jepgengDiabetes & Metabolic Syndrome: Clinical Research & Reviewsreponame:Expeditio Repositorio Institucional UJTLinstname:Universidad de Bogotá Jorge Tadeo LozanoIndiaCoronavirusDeath ratesCorrelationRegressionSíndrome respiratorio agudo graveCOVID-19SARS-CoV-2CoronavirusLinear Regression Analysis to predict the number of deaths in India due to SARS-CoV-2 at 6 weeks from day 0 (100 cases - March 14th 2020)Artículohttp://purl.org/coar/resource_type/c_6501Acceso restringidohttp://purl.org/coar/access_right/c_f1cfGhosal, SamitSengupta, SumitMajumder, MilanSinha, BinayakTHUMBNAILCaptura.PNGCaptura.PNGPortadaimage/png185512https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/12036/4/Captura.PNGef4673b49695118226ad5bdab07525bbMD54open accessLinear-Regression-Analysis-to-predict-the-number-o_2020_Diabetes---Metabolic.pdf.jpgLinear-Regression-Analysis-to-predict-the-number-o_2020_Diabetes---Metabolic.pdf.jpgIM Thumbnailimage/jpeg14858https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/12036/5/Linear-Regression-Analysis-to-predict-the-number-o_2020_Diabetes---Metabolic.pdf.jpg428eceb465cfb23105fa6c5faf06f4abMD55open accessORIGINALCaptura.PNGCaptura.PNGVer portadaimage/png185512https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/12036/1/Captura.PNGef4673b49695118226ad5bdab07525bbMD51open accessLinear-Regression-Analysis-to-predict-the-number-o_2020_Diabetes---Metabolic.pdfLinear-Regression-Analysis-to-predict-the-number-o_2020_Diabetes---Metabolic.pdfArtículo reservadoapplication/pdf229894https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/12036/3/Linear-Regression-Analysis-to-predict-the-number-o_2020_Diabetes---Metabolic.pdf854d4a9c093d48e1d5aa297403ef777fMD53open accessLICENSElicense.txtlicense.txttext/plain; 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