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Prediction of the number of deaths in India due to SARS-CoV-2 at 5–6 weeks

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Prediction of the number of deaths in India due to SARS-CoV-2 at 5–6 weeks

Auteurs : Samit Ghosal [Inde] ; Sumit Sengupta [Inde] ; Milan Majumder [Inde] ; Binayak Sinha [Inde]

Source :

Diabetes & Metabolic Syndrome [ 1871-4021 ] ; 2020.

RBID : PMC:7128942

Abstract

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: 1.31–2.60). 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.

Url:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7128942

DOI: 10.1016/j.dsx.2020.03.017
PubMed: 32298982
PubMed Central: 7128942

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<author><name sortKey="Ghosal, Samit" sort="Ghosal, Samit" uniqKey="Ghosal S" first="Samit" last="Ghosal">Samit Ghosal</name>
<affiliation wicri:level="1"><nlm:aff id="aff1">Consultant Endocrinologist. Nightingale Hospital, Kolkata, India</nlm:aff>
<country xml:lang="fr">Inde</country>
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<author><name sortKey="Sengupta, Sumit" sort="Sengupta, Sumit" uniqKey="Sengupta S" first="Sumit" last="Sengupta">Sumit Sengupta</name>
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<author><name sortKey="Majumder, Milan" sort="Majumder, Milan" uniqKey="Majumder M" first="Milan" last="Majumder">Milan Majumder</name>
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<series><title level="j">Diabetes & Metabolic Syndrome</title>
<idno type="ISSN">1871-4021</idno>
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<front><div type="abstract" xml:lang="en"><sec><title>Introduction</title>
<p>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.</p>
</sec>
<sec><title>Material and methods</title>
<p>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.</p>
</sec>
<sec><title>Results</title>
<p>A linear regression analysis predicted average week 5 death count to be 211 with a 95% CI: 1.31–2.60). 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.</p>
</sec>
<sec><title>Conclusion</title>
<p>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.</p>
</sec>
</div>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
  <front><journal-meta><journal-id journal-id-type="nlm-ta">Diabetes Metab Syndr</journal-id>
<journal-id journal-id-type="iso-abbrev">Diabetes Metab Syndr</journal-id>
<journal-title-group><journal-title>Diabetes & Metabolic Syndrome</journal-title>
</journal-title-group>
<issn pub-type="ppub">1871-4021</issn>
<issn pub-type="epub">1878-0334</issn>
<publisher><publisher-name>Diabetes India. Published by Elsevier Ltd.</publisher-name>
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<article-id pub-id-type="publisher-id">S1871-4021(20)30057-6</article-id>
<article-id pub-id-type="doi">10.1016/j.dsx.2020.03.017</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Article</subject>
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<title-group><article-title>Prediction of the number of deaths in India due to SARS-CoV-2 at 5–6 weeks</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" id="au1"><name><surname>Ghosal</surname>
<given-names>Samit</given-names>
</name>
<email>ramdasghosal@gmail.com</email>
<xref rid="aff1" ref-type="aff">a</xref>
<xref rid="cor1" ref-type="corresp">∗</xref>
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<contrib contrib-type="author" id="au2"><name><surname>Sengupta</surname>
<given-names>Sumit</given-names>
</name>
<xref rid="aff2" ref-type="aff">b</xref>
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<contrib contrib-type="author" id="au3"><name><surname>Majumder</surname>
<given-names>Milan</given-names>
</name>
<xref rid="aff3" ref-type="aff">c</xref>
</contrib>
<contrib contrib-type="author" id="au4"><name><surname>Sinha</surname>
<given-names>Binayak</given-names>
</name>
<xref rid="aff4" ref-type="aff">d</xref>
</contrib>
</contrib-group>
<aff id="aff1"><label>a</label>
Consultant Endocrinologist. Nightingale Hospital, Kolkata, India</aff>
<aff id="aff2"><label>b</label>
Consultant Pulmonologist. AMRI Hospitals, Kolkata, India</aff>
<aff id="aff3"><label>c</label>
Milan Majumder, Independent Statistician, Pune, India</aff>
<aff id="aff4"><label>d</label>
Consultant Endocrinologist. AMRI Hospitals, Kolkata, India</aff>
<author-notes><corresp id="cor1"><label>∗</label>
Corresponding author. <email>ramdasghosal@gmail.com</email>
</corresp>
</author-notes>
<pub-date pub-type="pmc-release"><day>2</day>
<month>4</month>
<year>2020</year>
</pub-date>
<pmc-comment> PMC Release delay is 0 months and 0 days and was based on .</pmc-comment>
      <pub-date pub-type="epub"><day>2</day>
<month>4</month>
<year>2020</year>
</pub-date>
<elocation-id></elocation-id>
<history><date date-type="received"><day>26</day>
<month>3</month>
<year>2020</year>
</date>
<date date-type="rev-recd"><day>27</day>
<month>3</month>
<year>2020</year>
</date>
<date date-type="accepted"><day>27</day>
<month>3</month>
<year>2020</year>
</date>
</history>
<permissions><copyright-statement>© 2020 Diabetes India. Published by Elsevier Ltd. All rights reserved.</copyright-statement>
<copyright-year>2020</copyright-year>
<copyright-holder></copyright-holder>
<license><license-p>Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.</license-p>
</license>
</permissions>
<abstract id="abs0010"><sec><title>Introduction</title>
<p>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.</p>
</sec>
<sec><title>Material and methods</title>
<p>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.</p>
</sec>
<sec><title>Results</title>
<p>A linear regression analysis predicted average week 5 death count to be 211 with a 95% CI: 1.31–2.60). 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.</p>
</sec>
<sec><title>Conclusion</title>
<p>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.</p>
</sec>
</abstract>
<abstract abstract-type="author-highlights" id="abs0015"><title>Highlights</title>
<p><list list-type="simple" id="ulist0010"><list-item id="u0010"><label>•</label>
<p id="p0010">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.</p>
</list-item>
<list-item id="u0015"><label>•</label>
<p id="p0015">According to our analysis, if situation continue in present state; projected death rate (n) is 211 and 467 at the end of the 5th and 6th week from now, respectively.</p>
</list-item>
<list-item id="u0020"><label>•</label>
<p id="p0020">Keeping these projected mortality data in mind, current measured for containment of COVID-19 must be strengthened or supplemented.</p>
</list-item>
</list>
</p>
</abstract>
<kwd-group id="kwrds0010"><title>Keywords</title>
<kwd>India</kwd>
<kwd>Coronavirus</kwd>
<kwd>Death rates</kwd>
<kwd>Correlation</kwd>
<kwd>Regression</kwd>
</kwd-group>
</article-meta>
</front>
</pmc>
</record>

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Prediction of the number of deaths in India due to SARS-CoV-2 at 5–6 weeks

Prediction of the number of deaths in India due to SARS-CoV-2 at 5–6 weeks

Source :

Abstract

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