Prediction of the number of deaths in India due to SARS-CoV-2 at 5–6 weeks
Identifieur interne : 000F21 ( Pmc/Corpus ); précédent : 000F20; suivant : 000F22Prediction of the number of deaths in India due to SARS-CoV-2 at 5–6 weeks
Auteurs : Samit Ghosal ; Sumit Sengupta ; Milan Majumder ; Binayak SinhaSource :
- Diabetes & Metabolic Syndrome [ 1871-4021 ] ; 2020.
Abstract
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.
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.
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.
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:
DOI: 10.1016/j.dsx.2020.03.017
PubMed: 32298982
PubMed Central: 7128942
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PMC:7128942Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Prediction of the number of deaths in India due to SARS-CoV-2 at 5–6 weeks</title><author><name sortKey="Ghosal, Samit" sort="Ghosal, Samit" uniqKey="Ghosal S" first="Samit" last="Ghosal">Samit Ghosal</name><affiliation><nlm:aff id="aff1">Consultant Endocrinologist. Nightingale Hospital, Kolkata, India</nlm:aff></affiliation></author><author><name sortKey="Sengupta, Sumit" sort="Sengupta, Sumit" uniqKey="Sengupta S" first="Sumit" last="Sengupta">Sumit Sengupta</name><affiliation><nlm:aff id="aff2">Consultant Pulmonologist. AMRI Hospitals, Kolkata, India</nlm:aff></affiliation></author><author><name sortKey="Majumder, Milan" sort="Majumder, Milan" uniqKey="Majumder M" first="Milan" last="Majumder">Milan Majumder</name><affiliation><nlm:aff id="aff3">Milan Majumder, Independent Statistician, Pune, India</nlm:aff></affiliation></author><author><name sortKey="Sinha, Binayak" sort="Sinha, Binayak" uniqKey="Sinha B" first="Binayak" last="Sinha">Binayak Sinha</name><affiliation><nlm:aff id="aff4">Consultant Endocrinologist. AMRI Hospitals, Kolkata, India</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">32298982</idno><idno type="pmc">7128942</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7128942</idno><idno type="RBID">PMC:7128942</idno><idno type="doi">10.1016/j.dsx.2020.03.017</idno><date when="2020">2020</date><idno type="wicri:Area/Pmc/Corpus">000F21</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000F21</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Prediction of the number of deaths in India due to SARS-CoV-2 at 5–6 weeks</title><author><name sortKey="Ghosal, Samit" sort="Ghosal, Samit" uniqKey="Ghosal S" first="Samit" last="Ghosal">Samit Ghosal</name><affiliation><nlm:aff id="aff1">Consultant Endocrinologist. Nightingale Hospital, Kolkata, India</nlm:aff></affiliation></author><author><name sortKey="Sengupta, Sumit" sort="Sengupta, Sumit" uniqKey="Sengupta S" first="Sumit" last="Sengupta">Sumit Sengupta</name><affiliation><nlm:aff id="aff2">Consultant Pulmonologist. AMRI Hospitals, Kolkata, India</nlm:aff></affiliation></author><author><name sortKey="Majumder, Milan" sort="Majumder, Milan" uniqKey="Majumder M" first="Milan" last="Majumder">Milan Majumder</name><affiliation><nlm:aff id="aff3">Milan Majumder, Independent Statistician, Pune, India</nlm:aff></affiliation></author><author><name sortKey="Sinha, Binayak" sort="Sinha, Binayak" uniqKey="Sinha B" first="Binayak" last="Sinha">Binayak Sinha</name><affiliation><nlm:aff id="aff4">Consultant Endocrinologist. AMRI Hospitals, Kolkata, India</nlm:aff></affiliation></author></analytic><series><title level="j">Diabetes & Metabolic Syndrome</title><idno type="ISSN">1871-4021</idno><idno type="eISSN">1878-0334</idno><imprint><date when="2020">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><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></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Remuzzi, A" uniqKey="Remuzzi A">A. Remuzzi</name></author><author><name sortKey="Remuzzi, G" uniqKey="Remuzzi G">G. Remuzzi</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Cascella, M" uniqKey="Cascella M">M. Cascella</name></author><author><name sortKey="Rajnik, M" uniqKey="Rajnik M">M. Rajnik</name></author><author><name sortKey="Cuomo, A" uniqKey="Cuomo A">A. Cuomo</name></author><author><name sortKey="Dulebohn, S C" uniqKey="Dulebohn S">S.C. Dulebohn</name></author><author><name sortKey="Napoli, R D" uniqKey="Napoli R">R.D. Napoli</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Armitage, R" uniqKey="Armitage R">R. Armitage</name></author><author><name sortKey="Nellums, L B" uniqKey="Nellums L">L.B. Nellums</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Peiris, J S M" uniqKey="Peiris J">J.S.M. Peiris</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Brian, D A" uniqKey="Brian D">D.A. Brian</name></author><author><name sortKey="Baric, R S" uniqKey="Baric R">R.S. Baric</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Burrell, J C" uniqKey="Burrell J">J.C. Burrell</name></author><author><name sortKey="Howard, C R" uniqKey="Howard C">C.R. Howard</name></author><author><name sortKey="Murphy, F A" uniqKey="Murphy F">F.A. Murphy</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Anderson, K G" uniqKey="Anderson K">K.G. Anderson</name></author><author><name sortKey="Rambaut, A" uniqKey="Rambaut A">A. Rambaut</name></author><author><name sortKey="Lipkin, W I" uniqKey="Lipkin W">W.I. Lipkin</name></author><author><name sortKey="Holmes, E C" uniqKey="Holmes E">E.C. Holmes</name></author><author><name sortKey="Gary, R F" uniqKey="Gary R">R.F. Gary</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct></listBibl></div1></back></TEI><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></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">32298982</article-id><article-id pub-id-type="pmc">7128942</article-id><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></subj-group></article-categories><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></contrib><contrib contrib-type="author" id="au2"><name><surname>Sengupta</surname><given-names>Sumit</given-names></name><xref rid="aff2" ref-type="aff">b</xref></contrib><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><body><sec id="sec1"><label>1</label><title>Introduction</title><p id="p0025">The pandemic of COVID-19 (Coronavirus disease 2019) caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) has created a havoc on the human civilization. Since, its appearance in the city of Wuhan (Hebei district) in China, it has been a relentless march of new cases and deaths [<xref rid="bib1" ref-type="bibr">1</xref>]. What makes it more scary is the novel strain of the virus and the unknowns associated with it [<xref rid="bib2" ref-type="bibr">2</xref>]. The present strategy has been to prevent its spread by social isolation and a scientific overdrive to manufacture newer rapid diagnostic kits as well as medications [<xref rid="bib3" ref-type="bibr">[3]</xref>, <xref rid="bib4" ref-type="bibr">[4]</xref>, <xref rid="bib5" ref-type="bibr">[5]</xref>]. Coronavirus belongs to a family of RNA viruses within the virus family Coronaviridae, order Nidovirales [<xref rid="bib6" ref-type="bibr">6</xref>]. Coronaviruses are divided into three groups depending on the antigenic spikes produced by different protein structures of the virus (spike, membrane & nucleocapsid) [<xref rid="bib7" ref-type="bibr">7</xref>]. The SARS coronavirus falls under group 2.</p><p id="p0030">The ability of this family of viruses to readily undergo genetic recombination not only within same group, but also between group, makes them readily susceptible to natural selection and changing its nature of virulence [<xref rid="bib8" ref-type="bibr">8</xref>]. The most striking feature however, is its ability to freely cross from one species to another. HCoV 229 E belongs to the group 1 of the coronaviruses family thought to be responsible for the epidemic of common cold [<xref rid="bib7" ref-type="bibr">7</xref>]. Transmission from bats to humans is thought to be the initial transmission process for HCoV 229 E, which had happened within the last two centuries. However, the two dramatic events- SARS-CoV (originated from bats and got transmitted from civet cats) & MERS-CoV (originated from bats and got transmitted from camels) in 2003 and 2012 respectively brough our focus back on the coronavirus family [<xref rid="bib8" ref-type="bibr">8</xref>,<xref rid="bib9" ref-type="bibr">9</xref>]. The present coronavirus pandemic is the result of changes in the receptor binding domain of the spike protein component via natural selection (?in human host/?in animal vector) resulting in its increased affinity for the ACE2 receptor site [<xref rid="bib10" ref-type="bibr">10</xref>]. ]At present we have more than 450000 individuals affected with Cov-2 resulting in more than 12000 deaths worldwide [<xref rid="bib11" ref-type="bibr">11</xref>]. In India, as the present day statistics holds, we have around 718 confirmed cases with 13 deaths [<xref rid="bib12" ref-type="bibr">12</xref>]. Several countries including India have gone into a state of lockdown in order to prevent spread of this deadly virus. With new rapid-diagnostic kits coming in and trials with potentially helpful drugs underway, we need a better understanding of the disease process and what it holds for the near future. With all the CoV-2 related data available through reliable sources, we choose to assimilate the available data on total infection rates, total deaths, case fatality rates (CFR), recovery numbers from across the globe and create a predictive analysis on what we can expect in India in the coming weeks.</p><p id="p0035">The aim was to identify the top 15 countries i.e. those most heavily affected and hence could contribute to a substantial quantity of robust data, and compute a predictive model for India. We thought this was of paramount importance, since it would help understanding as well as planning for the future course of action.</p><p id="p0040">India has entered week 4. 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 id="sec2"><label>2</label><title>Materials and methods</title><p id="p0045">Global data was collected from the WHO COVID-19 situation report and the Indian data was updated from the website <ext-link ext-link-type="uri" xlink:href="http://covid19india.org" id="intref0010">covid19india.org</ext-link>. Data was collected in a CSV file and uploaded in Jupyter notebook and analysed with the Python 3.8.2 software. As a re-validation process and for simplicity of understanding the data was also analysed using excel with XL-STAT statistical software.<disp-quote><p>Inputs: Total number of infected cases, active cases, recovery numbers,.</p></disp-quote><disp-quote><p>Outputs: Total deaths and case fatality rates (CFR)</p></disp-quote></p><p id="p0050">In order to get a good predictive value data was analysed for the top 15 infected countries with India the 16th country.</p><sec id="sec2.1"><label>2.1</label><title>Pre-analysis phase</title><p id="p0055">There was one missing data (NA) in the dataset, which was the recovery numbers from the US. In view of the heterogeneity of data and significant outliers data imputation with mean was ruled out.</p><p id="p0060">As a recovery strategy a correlation analysis was conducted (leaving out the US data) using python and a strong r = 0.99 (P < 0.001) was found between total number of infected cases and recovery. Utilising this robust association and the formula generated from linear regression (Y [Recovery cases | USA] = b0 + b1* [Total cases | USA], with b0 = -781.05 and b1 = 0.869), the missing value (1117) was derived. The analysis was conducted thereafter (<xref rid="tbl1" ref-type="table">Table 1</xref>).<table-wrap position="float" id="tbl1"><label>Table 1</label><caption><p>Raw data including all coronavirus-related variables for week 1 and the total death outputs for week 5 through 9, including the imputed value.</p></caption><alt-text id="alttext0010">Table 1</alt-text><table frame="hsides" rules="groups"><thead><tr><th>Countries</th><th>Total cases</th><th>Active cases</th><th>Recovery cases</th><th>Week 4 deaths</th><th>CFR</th><th>Week 5 deaths</th></tr></thead><tbody><tr><td align="left">China</td><td align="left">74185</td><td align="left">57805</td><td align="left">65112</td><td align="left">2004</td><td align="left">2.701</td><td align="left">2715</td></tr><tr><td align="left">Italy</td><td align="left">21157</td><td align="left">17750</td><td align="left">12207</td><td align="left">1441</td><td align="left">6.811</td><td align="left">4825</td></tr><tr><td align="left">Spain</td><td align="left">5232</td><td align="left">4906</td><td align="left">3097</td><td align="left">133</td><td align="left">2.542</td><td align="left">1093</td></tr><tr><td align="left">Iran</td><td align="left">11364</td><td align="left">7321</td><td align="left">9919</td><td align="left">514</td><td align="left">4.523</td><td align="left">1433</td></tr><tr><td align="left">France</td><td align="left">3661</td><td align="left">3570</td><td align="left">482</td><td align="left">79</td><td align="left">2.158</td><td align="left">450</td></tr><tr><td align="left">UK</td><td align="left">798</td><td align="left">769</td><td align="left">495</td><td align="left">11</td><td align="left">1.378</td><td align="left">177</td></tr><tr><td align="left">Netherlands</td><td align="left">804</td><td align="left">792</td><td align="left">134</td><td align="left">10</td><td align="left">1.244</td><td align="left">106</td></tr><tr><td align="left">Germany</td><td align="left">3675</td><td align="left">3621</td><td align="left">3130</td><td align="left">8</td><td align="left">0.218</td><td align="left">68</td></tr><tr><td align="left">Belgium</td><td align="left">559</td><td align="left">555</td><td align="left">139</td><td align="left">3</td><td align="left">0.537</td><td align="left">37</td></tr><tr><td align="left">Switzerland</td><td align="left">1139</td><td align="left">1124</td><td align="left">303</td><td align="left">11</td><td align="left">0.966</td><td align="left">56</td></tr><tr><td align="left">South Korea</td><td align="left">7979</td><td align="left">7198</td><td align="left">7294.42</td><td align="left">67</td><td align="left">0.840</td><td align="left">94</td></tr><tr><td align="left">Austria</td><td align="left">504</td><td align="left">497</td><td align="left">431</td><td align="left">1</td><td align="left">0.198</td><td align="left">6</td></tr><tr><td align="left">Brazil</td><td align="left">151</td><td align="left">150</td><td align="left">151</td><td align="left">0</td><td align="left">0.000</td><td align="left">11</td></tr><tr><td align="left">Indonesia</td><td align="left">69</td><td align="left">60</td><td align="left">38</td><td align="left">4</td><td align="left">5.797</td><td align="left">32</td></tr><tr><td align="left">USA</td><td align="left">2183</td><td align="left">2126</td><td align="left">1117</td><td align="left">48</td><td align="left">2.199</td><td align="left">255</td></tr><tr><td align="left"><bold>India</bold></td><td align="left">606</td><td align="left">554</td><td align="left">42</td><td align="left">10</td><td align="left">1.650</td><td></td></tr></tbody></table></table-wrap><table-wrap position="float" id="tbl2"><label>Table 2</label><caption><p>Correlation analysis determining the relationship between week 5 deaths and all the input variables.</p></caption><alt-text id="alttext0015">Table 2</alt-text><table frame="hsides" rules="groups"><thead><tr><th> </th><th><italic>Total cases</italic></th><th><italic>Active cases</italic></th><th><italic>Recovery cases</italic></th><th><italic>Week 4 deaths</italic></th><th><italic>CFR</italic></th><th><italic>Week 5 deaths</italic></th></tr></thead><tbody><tr><td align="left">Total cases</td><td align="left">1</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td align="left">Active cases</td><td align="left">0.99904861</td><td align="left">1</td><td></td><td></td><td></td><td></td></tr><tr><td align="left">Recovery cases</td><td align="left">0.994753954</td><td align="left">0.991471532</td><td align="left">1</td><td></td><td></td><td></td></tr><tr><td align="left">Week 4 deaths</td><td align="left">0.922623423</td><td align="left">0.924523558</td><td align="left">0.883996909</td><td align="left">1</td><td></td><td></td></tr><tr><td align="left">CFR</td><td align="left">0.268208625</td><td align="left">0.266050424</td><td align="left">0.209369645</td><td align="left">0.511501668</td><td align="left">1</td><td></td></tr><tr><td align="left">Week 5 deaths</td><td align="left">0.635636081</td><td align="left">0.644536597</td><td align="left">0.561097633</td><td align="left">0.876211223</td><td align="left">0.696402315</td><td align="left">1</td></tr></tbody></table></table-wrap><table-wrap position="float" id="tbl3"><label>Table 3</label><caption><p>Results from the multiple regression analysis conducted with 5th week death count as output and all the 4th week parameters as input. * Goodness of fit (Adjusted R Square) shows the high predictive power of the model in this multivariate linear regression. However, most of predictors fail to show their significance of contribution in model except Week 4 death.</p></caption><alt-text id="alttext0020">Table 3</alt-text><table frame="hsides" rules="groups"><thead><tr><th colspan="9">SUMMARY OUTPUT</th></tr></thead><tbody><tr><td colspan="2" align="left"><italic>Regression Statistics</italic></td><td colspan="7" rowspan="6"></td></tr><tr><td align="left">Multiple R</td><td align="left">0.990327848</td></tr><tr><td align="left">R Square</td><td align="left">0.980749246</td></tr><tr><td align="left">Adjusted R Square</td><td align="left">0.970054383</td></tr><tr><td align="left">Standard Error</td><td align="left">234.1358914</td></tr><tr><td align="left">Observations</td><td align="left">15</td></tr><tr><td colspan="9" align="left">
</td></tr><tr><td colspan="9" align="left">ANOVA</td></tr><tr><td align="left"> <hr></hr></td><td align="left"><italic>df</italic><hr></hr></td><td align="left"><italic>SS</italic><hr></hr></td><td align="left"><italic>MS</italic><hr></hr></td><td align="left"><italic>F</italic><hr></hr></td><td align="left"><italic>Significance F</italic><hr></hr></td><td colspan="3" rowspan="4"><hr></hr></td></tr><tr><td align="left">Regression</td><td align="left">5</td><td align="left">25135569.86</td><td align="left">5027113.972</td><td align="left">91.70283143</td><td align="left">1.92537E-07</td></tr><tr><td align="left">Residual</td><td align="left">9</td><td align="left">493376.5407</td><td align="left">54819.61564</td><td></td><td></td></tr><tr><td align="left">Total</td><td align="left">14</td><td align="left">25628946.4</td><td></td><td></td><td></td></tr><tr><td colspan="9" align="left">
</td></tr><tr><td align="left"> <hr></hr></td><td align="left"><italic>Coefficients</italic><hr></hr></td><td align="left"><italic>Standard Error</italic><hr></hr></td><td align="left"><italic>t Stat</italic><hr></hr></td><td align="left"><italic>P-value</italic><hr></hr></td><td align="left"><italic>Lower 95%</italic><hr></hr></td><td align="left"><italic>Upper 95%</italic><hr></hr></td><td align="left"><italic>Lower 95.0%</italic><hr></hr></td><td align="left"><italic>Upper 95.0%</italic><hr></hr></td></tr><tr><td align="left">Intercept</td><td align="left">84.42512812</td><td align="left">115.0074695</td><td align="left">0.734083868</td><td align="left">0.481583332</td><td align="left">−175.7398428</td><td align="left">344.590099</td><td align="left">−175.7398428</td><td align="left">344.590099</td></tr><tr><td align="left">Total cases</td><td align="left">−0.069994422</td><td align="left">0.218157313</td><td align="left">−0.320843804</td><td align="left">0.755653571</td><td align="left">−0.56350055</td><td align="left">0.423511705</td><td align="left">−0.56350055</td><td align="left">0.423511705</td></tr><tr><td align="left">Active cases</td><td align="left">0.121557776</td><td align="left">0.155384517</td><td align="left">0.782303013</td><td align="left">0.454125958</td><td align="left">−0.229946423</td><td align="left">0.473061974</td><td align="left">−0.229946423</td><td align="left">0.473061974</td></tr><tr><td align="left">Recovery cases</td><td align="left">−0.095715087</td><td align="left">0.109664289</td><td align="left">−0.872800868</td><td align="left">0.405455473</td><td align="left">−0.343792945</td><td align="left">0.152362771</td><td align="left">−0.343792945</td><td align="left">0.152362771</td></tr><tr><td align="left">Week 4 deaths</td><td align="left">3.49748606</td><td align="left">0.70391798</td><td align="left">4.968598845</td><td align="left">0.000771382</td><td align="left">1.905112961</td><td align="left">5.08985916</td><td align="left">1.905112961</td><td align="left">5.08985916</td></tr><tr><td align="left">CFR</td><td align="left">33.51344079</td><td align="left">46.33770995</td><td align="left">0.723243355</td><td align="left">0.487899902</td><td align="left">−71.30974168</td><td align="left">138.3366233</td><td align="left">−71.30974168</td><td align="left">138.3366233</td></tr></tbody></table></table-wrap><table-wrap position="float" id="tbl4"><label>Table 4</label><caption><p>The maximum, minimum and average predicted death counts for week 6 based on the equation of the linear regression model.</p></caption><alt-text id="alttext0025">Table 4</alt-text><table frame="hsides" rules="groups"><thead><tr><th>In 95% Confidence Interval</th><th colspan="2">Intercept and Co-efficient</th><th>5th Week predicted death</th></tr></thead><tbody><tr><td rowspan="2" align="left">Mean point of estimation</td><td align="left">b0</td><td align="left">191.644</td><td rowspan="2" align="left">211</td></tr><tr><td align="left">b1</td><td align="left">1.957</td></tr><tr><td rowspan="2" align="left">Lower point of estimation</td><td align="left">b0</td><td align="left">−229.314</td><td rowspan="2" align="left">−216</td></tr><tr><td align="left">b1</td><td align="left">1.312</td></tr><tr><td rowspan="2" align="left">Upper point of estimation</td><td align="left">b0</td><td align="left">612.602</td><td rowspan="2" align="left">639</td></tr><tr><td align="left">b1</td><td align="left">2.602</td></tr></tbody></table></table-wrap><table-wrap position="float" id="tbl5"><label>Table 5</label><caption><p>Multiple regression analysis with week 6 death counts as input and all the 4th week variables as input. * Goodness of fit (Adjusted R Square) shows the high predictive power of the model in this multivariate linear regression. However, all the predictors fail to show their significance of contribution in model.</p></caption><alt-text id="alttext0030">Table 5</alt-text><table frame="hsides" rules="groups"><thead><tr><th colspan="7">SUMMARY OUTPUT</th></tr></thead><tbody><tr><td colspan="2" align="left"><italic>Regression Statistics</italic></td><td colspan="5" rowspan="6"></td></tr><tr><td align="left">Multiple R</td><td align="left">0.955366444</td></tr><tr><td align="left">R Square</td><td align="left">0.912725042</td></tr><tr><td align="left">Adjusted R Square</td><td align="left">0.864238954</td></tr><tr><td align="left">Standard Error</td><td align="left">687.4807679</td></tr><tr><td align="left">Observations</td><td align="left">15</td></tr><tr><td colspan="7" align="left">
</td></tr><tr><td colspan="7" align="left">ANOVA</td></tr><tr><td align="left"> <hr></hr></td><td align="left"><italic>Df</italic><hr></hr></td><td align="left"><italic>SS</italic><hr></hr></td><td align="left"><italic>MS</italic><hr></hr></td><td align="left"><italic>F</italic><hr></hr></td><td align="left"><italic>Significance F</italic><hr></hr></td><td><hr></hr></td></tr><tr><td align="left">Regression</td><td align="left">5</td><td align="left">44485034.68</td><td align="left">8897006.936</td><td align="left">18.82447281</td><td align="left">0.000158714</td><td></td></tr><tr><td align="left">Residual</td><td align="left">9</td><td align="left">4253668.256</td><td align="left">472629.8062</td><td></td><td></td><td></td></tr><tr><td align="left">Total</td><td align="left">14</td><td align="left">48738702.93</td><td></td><td></td><td></td><td></td></tr><tr><td colspan="7" align="left">
</td></tr><tr><td align="left"> <hr></hr></td><td align="left"><italic>Coefficients</italic><hr></hr></td><td align="left"><italic>Standard Error</italic><hr></hr></td><td align="left"><italic>t Stat</italic><hr></hr></td><td align="left"><italic>P-value</italic><hr></hr></td><td align="left"><italic>Lower 95%</italic><hr></hr></td><td align="left"><italic>Upper 95%</italic><hr></hr></td></tr><tr><td align="left">Intercept</td><td align="left">115.6866622</td><td align="left">337.6903173</td><td align="left">0.342582112</td><td align="left">0.739777934</td><td align="left">−648.2219079</td><td align="left">879.5952322</td></tr><tr><td align="left">Total cases</td><td align="left">0.174235894</td><td align="left">0.640563717</td><td align="left">0.272004002</td><td align="left">0.791755917</td><td align="left">−1.274819906</td><td align="left">1.623291695</td></tr><tr><td align="left">Active cases</td><td align="left">0.159410883</td><td align="left">0.456247295</td><td align="left">0.349395788</td><td align="left">0.734828115</td><td align="left">−0.872692204</td><td align="left">1.19151397</td></tr><tr><td align="left">Recovery cases</td><td align="left">−0.392375137</td><td align="left">0.322001422</td><td align="left">−1.218550947</td><td align="left">0.253991509</td><td align="left">−1.12079296</td><td align="left">0.336042685</td></tr><tr><td align="left">Week 4 deaths</td><td align="left">3.061382182</td><td align="left">2.066876933</td><td align="left">1.481163263</td><td align="left">0.172701393</td><td align="left">−1.614218276</td><td align="left">7.736982641</td></tr><tr><td align="left">CFR</td><td align="left">101.8408025</td><td align="left">136.0589538</td><td align="left">0.748504966</td><td align="left">0.4732618</td><td align="left">−205.9459343</td><td align="left">409.6275393</td></tr></tbody></table></table-wrap><table-wrap position="float" id="tbl6"><label>Table 6</label><caption><p>Prediction for 6th week death count in India based on the auto-regression analysis technique.</p></caption><alt-text id="alttext0035">Table 6</alt-text><table frame="hsides" rules="groups"><thead><tr><th>In 95% Confidence Interval</th><th colspan="2">Intercept and Co-efficient</th><th>6th Week predicted death</th></tr></thead><tbody><tr><td rowspan="2" align="left">Mean point of estimation</td><td align="left">b0</td><td align="left">184.33</td><td rowspan="2" align="left">467</td></tr><tr><td align="left">b1</td><td align="left">1.34</td></tr><tr><td rowspan="2" align="left">Lower point of estimation</td><td align="left">b0</td><td align="left">−119.77</td><td rowspan="2" align="left">120</td></tr><tr><td align="left">b1</td><td align="left">1.14</td></tr><tr><td rowspan="2" align="left">Upper point of estimation</td><td align="left">b0</td><td align="left">488.44</td><td rowspan="2" align="left">813</td></tr><tr><td align="left">b1</td><td align="left">1.54</td></tr></tbody></table></table-wrap></p></sec></sec><sec id="sec3"><label>3</label><title>Results</title><p id="p0065">Analysis for week 5 death number prediction:<list list-type="simple" id="ulist0015"><list-item id="u0025"><label>•</label><p id="p0070">Step1: A correlation analysis was performed to ascertain the presence of and thereafter the strength of association between the output (week 5 death count) and the inputs from week 4. There was a strong correlation between week 5 deaths and all the input variables (<xref rid="tbl2" ref-type="table">Table 2</xref>).</p></list-item><list-item id="u0030"><label>•</label><p id="p0075">Step 2: A multivariate regression analysis ascertained the most important input parameters which would be used to build the model for the 5th week death prediction in India. The model came out to have a very strong predictive capacity (r = 0.99, R<sup>2</sup> = 0.98, adjusted R<sup>2</sup> = 0.97). However, the P-value was significant only for the 4th week death input parameter (<xref rid="tbl3" ref-type="table">Table 3</xref>).</p></list-item><list-item id="u0035"><label>•</label><p id="p0080">Step 3: A simple regression analysis was subsequently done to predict the death counts from the strongest input variable (<xref rid="tbl4" ref-type="table">Table 4</xref>). The model was robust with r = 0.87, R<sup>2</sup> = 0.77 & adjusted R<sup>2</sup> = 0.75, P < 0.001, 95% CI: 1.31–2.60. Based on the upper limit(maximum) & the lower limit (minimum) of the confidence intervals, the minimum, maximum and average death counts for week 5 was computed-211 (<xref rid="tbl4" ref-type="table">Table 4</xref>). Hence the week 5 death counts for India was predicted based on the available data from the top 15 infected countries.</p></list-item></list></p><sec id="sec3.1"><label>3.1</label><title>Death number prediction for week 6</title><p id="p0085"><list list-type="simple" id="ulist0020"><list-item id="u0040"><label>•</label><p id="p0090">Step 1: Correlation study was conducted to ascertain the relationship between the output (week 6 death counts) and the input variables from week 4. A good correlation was observed with all the input variables.</p></list-item><list-item id="u0045"><label>•</label><p id="p0095">Step 2: Multiple regression analysis was done to ascertain the strength of association between the input variables and the output, including ruling out issues related to multi-collinearity. Once again the model created was very robust with r = 0.95, R<sup>2</sup> = 0.91 & adjusted R<sup>2</sup> = 0.86 (<xref rid="tbl5" ref-type="table">Table 5</xref>). However, the P-value for significance was not evident for any of the input variables.</p></list-item><list-item id="u0050"><label>•</label><p id="p0100">Step 3: <bold>Auto-Regression technique-</bold> The 5th week death count data was incorporated as the input variable, in view of the fact that this end-point was significantly associated with the week 4 death count. A separate correlation analysis was performed between week 5 and week 6 death counts and a very robust association (r = 0.97) was found justifying its inclusion as the input variable.</p></list-item><list-item id="u0055"><label>•</label><p id="p0105">Step 4: Using week 5 death count as input a simple linear regression was performed to create a model predicting the week 6 outcomes. The model was robust (r = 0.96, R2 = 0.94, adjusted R2 = 0.94) and statistically significant (P-value <0.001, 95% CI: 1.13–1.54).</p></list-item><list-item id="u0060"><label>•</label><p id="p0110">Step 5: From the regression model formula the minimum, maximum, and average death count was estimated (<xref rid="tbl6" ref-type="table">Table 6</xref>). The average predicted death count for India was estimated to be 467.</p></list-item></list></p></sec></sec><sec id="sec4"><label>4</label><title>Discussion</title><p id="p0115">India is in the 4th week of the coronavirus pandemic. What lies ahead for India is the crucial stage, week 5–6 where effective preventive measures can prevent a potential catastrophe, which countries like China, Italy and the United States of America are experiencing, with an exponential growth of both infection as well as deaths. Exponential progression in the number of infected cases have occurred from the 4th week onwards, in the above mentioned countries [<xref rid="bib11" ref-type="bibr">11</xref>].</p><p id="p0120">At the point of going to the press, there are approximately 4,89,853 confirmed cases of coronavirus infection worldwide including 22,152 deaths [<xref rid="bib11" ref-type="bibr">11</xref>]. Luckily, at 4th week, India has escaped the brunt of the disease with figures hovering around 693 confirmed infections and 13 deaths. It is the next couple of weeks which holds the key to the direction the virus takes or doesn't take if we take adequate preventive steps.</p><p id="p0125">India has already taken strong measures including complete lockdown of both its internal and external borders as well as social isolation.</p><sec id="sec4.1"><label>4.1</label><title>How does this analysis help?</title><p id="p0130">Assessing the trends of the top 15 most infected countries a predictive model was created for India assuming that the same trend would follow. In other words can we justify the drastic measures being taken? What can we expect, if we allow the present trend to continue and mimic the exponential growth experienced by China and our western counterparts? Our analysis predicts a jump from approximately 10 deaths at week 4–211 at week 5 and then 467 by week 6.</p><p id="p0135">We speculate the need for urgent interventions (which are being taken as of now), to prevent this drastic and sharp rise in death rates which indirectly also indicates an increase in infection rate.</p></sec><sec id="sec4.2"><label>4.2</label><title>Limitations of this analysis</title><p id="p0140">The main limitation of this analysis was that it takes most input data into consideration without taking into account the logistic actions being taken or not taken during the process. However, the end of weeks results are highly indicative of both the virus-related natural trajectory as well as the local government's reactions.</p><p id="p0145">Secondly, limiting our analysis to the top 15 most infected countries could lead to an over-estimation of the outcomes. However, faced with a catastrophe of such magnitude, it is worth over-estimating rather than under-estimating.</p></sec><sec id="sec4.3"><label>4.3</label><title>Strength of the study</title><p id="p0150">In spite of all the limitations the biggest strength of this study was very high adjusted R<sup>2</sup> found in all the predictive models. In addition there was cross-validation with two different software practically ruling out any error creeping in from one mode of analysis.</p></sec></sec><sec id="sec5"><label>5</label><title>Conclusion</title><p id="p0155">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. Keeping these projected mortality data in mind, current measured for containment of COVID-19 must be strengthened or supplemented.</p></sec><sec id="sec6"><title>Funding</title><p id="p0160">None.</p></sec><sec sec-type="COI-statement"><title>Declaration of competing interest</title><p id="p0165">None to declare.</p></sec></body><back><ref-list id="cebib0010"><title>References</title><ref id="bib1"><label>1</label><element-citation publication-type="book" id="sref1"><person-group person-group-type="author"><name><surname>Remuzzi</surname><given-names>A.</given-names></name><name><surname>Remuzzi</surname><given-names>G.</given-names></name></person-group><chapter-title>COVID-19 and Italy: what next? 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