Spatial modelling of contribution of individual level risk factors for mortality from Middle East respiratory syndrome coronavirus in the Arabian Peninsula
Identifieur interne : 001032 ( Pmc/Corpus ); précédent : 001031; suivant : 001033Spatial modelling of contribution of individual level risk factors for mortality from Middle East respiratory syndrome coronavirus in the Arabian Peninsula
Auteurs : Oyelola A. Adegboye ; Ezra Gayawan ; Fahad HannaSource :
- PLoS ONE [ 1932-6203 ] ; 2017.
Abstract
Middle East respiratory syndrome coronavirus is a contagious respiratory pathogen that is contracted via close contact with an infected subject. Transmission of the pathogen has occurred through animal-to-human contact at first followed by human-to-human contact within families and health care facilities.
This study is based on a retrospective analysis of the Middle East respiratory syndrome coronavirus outbreak in the Kingdom of Saudi Arabia between June 2012 and July 2015. A Geoadditive variable model for binary outcomes was applied to account for both individual level risk factors as well spatial variation via a fully Bayesian approach.
Out of 959 confirmed cases, 642 (67%) were males and 317 (33%) had died. Three hundred and sixty four (38%) cases occurred in Ar Riyad province, while 325 (34%) cases occurred in Makkah. Individuals with some comorbidity had a significantly higher likelihood of dying from MERS-CoV compared with those who did not suffer comorbidity [Odds ratio (OR) = 2.071; 95% confidence interval (CI): 1.307, 3.263]. Health-care workers were significantly less likely to die from the disease compared with non-health workers [OR = 0.372, 95% CI: 0.151, 0.827]. Patients who had fatal clinical experience and those with clinical and subclinical experiences were equally less likely to die from the disease compared with patients who did not have fatal clinical experience and those without clinical and subclinical experiences respectively. The odds of dying from the disease was found to increase as age increased beyond 25 years and was much higher for individuals with any underlying comorbidities.
Interventions to minimize mortality from the Middle East respiratory syndrome coronavirus should particularly focus individuals with comorbidity, non-health-care workers, patients with no clinical fatal experience, and patients without any clinical and subclinical experiences.
Url:
DOI: 10.1371/journal.pone.0181215
PubMed: 28759623
PubMed Central: 5536289
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PMC:5536289Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Spatial modelling of contribution of individual level risk factors for mortality from Middle East respiratory syndrome coronavirus in the Arabian Peninsula</title><author><name sortKey="Adegboye, Oyelola A" sort="Adegboye, Oyelola A" uniqKey="Adegboye O" first="Oyelola A." last="Adegboye">Oyelola A. Adegboye</name><affiliation><nlm:aff id="aff001"><addr-line>Department of Mathematics, Statistics and Physics, College of Arts and Sciences, Qatar University, 2713 Doha, Qatar</addr-line></nlm:aff></affiliation></author><author><name sortKey="Gayawan, Ezra" sort="Gayawan, Ezra" uniqKey="Gayawan E" first="Ezra" last="Gayawan">Ezra Gayawan</name><affiliation><nlm:aff id="aff002"><addr-line>Department of Statistics, Federal University of Technology, Akure, Nigeria</addr-line></nlm:aff></affiliation></author><author><name sortKey="Hanna, Fahad" sort="Hanna, Fahad" uniqKey="Hanna F" first="Fahad" last="Hanna">Fahad Hanna</name><affiliation><nlm:aff id="aff003"><addr-line>Department of Public Health, College of Health Sciences, Qatar University, 2713 Doha, Qatar</addr-line></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">28759623</idno><idno type="pmc">5536289</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5536289</idno><idno type="RBID">PMC:5536289</idno><idno type="doi">10.1371/journal.pone.0181215</idno><date when="2017">2017</date><idno type="wicri:Area/Pmc/Corpus">001032</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">001032</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Spatial modelling of contribution of individual level risk factors for mortality from Middle East respiratory syndrome coronavirus in the Arabian Peninsula</title><author><name sortKey="Adegboye, Oyelola A" sort="Adegboye, Oyelola A" uniqKey="Adegboye O" first="Oyelola A." last="Adegboye">Oyelola A. Adegboye</name><affiliation><nlm:aff id="aff001"><addr-line>Department of Mathematics, Statistics and Physics, College of Arts and Sciences, Qatar University, 2713 Doha, Qatar</addr-line></nlm:aff></affiliation></author><author><name sortKey="Gayawan, Ezra" sort="Gayawan, Ezra" uniqKey="Gayawan E" first="Ezra" last="Gayawan">Ezra Gayawan</name><affiliation><nlm:aff id="aff002"><addr-line>Department of Statistics, Federal University of Technology, Akure, Nigeria</addr-line></nlm:aff></affiliation></author><author><name sortKey="Hanna, Fahad" sort="Hanna, Fahad" uniqKey="Hanna F" first="Fahad" last="Hanna">Fahad Hanna</name><affiliation><nlm:aff id="aff003"><addr-line>Department of Public Health, College of Health Sciences, Qatar University, 2713 Doha, Qatar</addr-line></nlm:aff></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2017">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec id="sec001"><title>Background</title><p>Middle East respiratory syndrome coronavirus is a contagious respiratory pathogen that is contracted via close contact with an infected subject. Transmission of the pathogen has occurred through animal-to-human contact at first followed by human-to-human contact within families and health care facilities.</p></sec><sec id="sec002"><title>Data and methods</title><p>This study is based on a retrospective analysis of the Middle East respiratory syndrome coronavirus outbreak in the Kingdom of Saudi Arabia between June 2012 and July 2015. A Geoadditive variable model for binary outcomes was applied to account for both individual level risk factors as well spatial variation via a fully Bayesian approach.</p></sec><sec id="sec003"><title>Results</title><p>Out of 959 confirmed cases, 642 (67%) were males and 317 (33%) had died. Three hundred and sixty four (38%) cases occurred in Ar Riyad province, while 325 (34%) cases occurred in Makkah. Individuals with some comorbidity had a significantly higher likelihood of dying from MERS-CoV compared with those who did not suffer comorbidity [Odds ratio (OR) = 2.071; 95% confidence interval (CI): 1.307, 3.263]. Health-care workers were significantly less likely to die from the disease compared with non-health workers [OR = 0.372, 95% CI: 0.151, 0.827]. Patients who had fatal clinical experience and those with clinical and subclinical experiences were equally less likely to die from the disease compared with patients who did not have fatal clinical experience and those without clinical and subclinical experiences respectively. The odds of dying from the disease was found to increase as age increased beyond 25 years and was much higher for individuals with any underlying comorbidities.</p></sec><sec id="sec004"><title>Conclusion</title><p>Interventions to minimize mortality from the Middle East respiratory syndrome coronavirus should particularly focus individuals with comorbidity, non-health-care workers, patients with no clinical fatal experience, and patients without any clinical and subclinical experiences.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Van Doremalen, N" uniqKey="Van Doremalen N">N Van Doremalen</name></author><author><name sortKey="Bushmaker, T" uniqKey="Bushmaker T">T Bushmaker</name></author><author><name sortKey="Munster, Vj" uniqKey="Munster V">VJ Munster</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Cauchemez, S" uniqKey="Cauchemez S">S Cauchemez</name></author><author><name 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<front><journal-meta><journal-id journal-id-type="nlm-ta">PLoS One</journal-id><journal-id journal-id-type="iso-abbrev">PLoS ONE</journal-id><journal-id journal-id-type="publisher-id">plos</journal-id><journal-id journal-id-type="pmc">plosone</journal-id><journal-title-group><journal-title>PLoS ONE</journal-title></journal-title-group><issn pub-type="epub">1932-6203</issn><publisher><publisher-name>Public Library of Science</publisher-name><publisher-loc>San Francisco, CA USA</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">28759623</article-id><article-id pub-id-type="pmc">5536289</article-id><article-id pub-id-type="publisher-id">PONE-D-16-39457</article-id><article-id pub-id-type="doi">10.1371/journal.pone.0181215</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Biology and life sciences</subject><subj-group><subject>Organisms</subject><subj-group><subject>Viruses</subject><subj-group><subject>RNA viruses</subject><subj-group><subject>Coronaviruses</subject></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject><subj-group><subject>Microbiology</subject><subj-group><subject>Medical Microbiology</subject><subj-group><subject>Microbial Pathogens</subject><subj-group><subject>Viral Pathogens</subject><subj-group><subject>Coronaviruses</subject></subj-group></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Medicine and Health Sciences</subject><subj-group><subject>Pathology and Laboratory Medicine</subject><subj-group><subject>Pathogens</subject><subj-group><subject>Microbial Pathogens</subject><subj-group><subject>Viral Pathogens</subject><subj-group><subject>Coronaviruses</subject></subj-group></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject><subj-group><subject>Organisms</subject><subj-group><subject>Viruses</subject><subj-group><subject>Viral Pathogens</subject><subj-group><subject>Coronaviruses</subject></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Medicine and Health Sciences</subject><subj-group><subject>Pulmonology</subject><subj-group><subject>Respiratory Infections</subject></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject><subj-group><subject>Organisms</subject><subj-group><subject>Animals</subject><subj-group><subject>Vertebrates</subject><subj-group><subject>Amniotes</subject><subj-group><subject>Mammals</subject><subj-group><subject>Camels</subject></subj-group></subj-group></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>People and places</subject><subj-group><subject>Geographical locations</subject><subj-group><subject>Asia</subject><subj-group><subject>Saudi Arabia</subject></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Medicine and Health Sciences</subject><subj-group><subject>Endocrinology</subject><subj-group><subject>Endocrine Disorders</subject><subj-group><subject>Diabetes Mellitus</subject></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Medicine and Health Sciences</subject><subj-group><subject>Metabolic Disorders</subject><subj-group><subject>Diabetes Mellitus</subject></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Medicine and Health Sciences</subject><subj-group><subject>Epidemiology</subject><subj-group><subject>Infectious Disease Epidemiology</subject></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Medicine and Health Sciences</subject><subj-group><subject>Infectious Diseases</subject><subj-group><subject>Infectious Disease Epidemiology</subject></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>People and Places</subject><subj-group><subject>Demography</subject><subj-group><subject>Death Rates</subject></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Research and Analysis Methods</subject><subj-group><subject>Mathematical and Statistical Techniques</subject><subj-group><subject>Statistical Methods</subject><subj-group><subject>Test Statistics</subject></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Physical Sciences</subject><subj-group><subject>Mathematics</subject><subj-group><subject>Statistics (Mathematics)</subject><subj-group><subject>Statistical Methods</subject><subj-group><subject>Test Statistics</subject></subj-group></subj-group></subj-group></subj-group></subj-group></article-categories><title-group><article-title>Spatial modelling of contribution of individual level risk factors for mortality from Middle East respiratory syndrome coronavirus in the Arabian Peninsula</article-title><alt-title alt-title-type="running-head">Risk of mortality from Middle East respiratory syndrome coronavirus</alt-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Adegboye</surname><given-names>Oyelola A.</given-names></name><role content-type="http://credit.casrai.org/">Conceptualization</role><role content-type="http://credit.casrai.org/">Data curation</role><role content-type="http://credit.casrai.org/">Formal analysis</role><role content-type="http://credit.casrai.org/">Methodology</role><role content-type="http://credit.casrai.org/">Resources</role><role content-type="http://credit.casrai.org/">Software</role><role content-type="http://credit.casrai.org/">Validation</role><role content-type="http://credit.casrai.org/">Visualization</role><role content-type="http://credit.casrai.org/">Writing – original draft</role><role content-type="http://credit.casrai.org/">Writing – review & editing</role><xref ref-type="aff" rid="aff001"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Gayawan</surname><given-names>Ezra</given-names></name><role content-type="http://credit.casrai.org/">Data curation</role><role content-type="http://credit.casrai.org/">Formal analysis</role><role content-type="http://credit.casrai.org/">Investigation</role><role content-type="http://credit.casrai.org/">Software</role><role content-type="http://credit.casrai.org/">Writing – original draft</role><xref ref-type="aff" rid="aff002"><sup>2</sup></xref></contrib><contrib contrib-type="author"><contrib-id authenticated="true" contrib-id-type="orcid">http://orcid.org/0000-0002-0494-9585</contrib-id><name><surname>Hanna</surname><given-names>Fahad</given-names></name><role content-type="http://credit.casrai.org/">Conceptualization</role><role content-type="http://credit.casrai.org/">Formal analysis</role><role content-type="http://credit.casrai.org/">Methodology</role><role content-type="http://credit.casrai.org/">Validation</role><role content-type="http://credit.casrai.org/">Visualization</role><role content-type="http://credit.casrai.org/">Writing – original draft</role><role content-type="http://credit.casrai.org/">Writing – review & editing</role><xref ref-type="aff" rid="aff003"><sup>3</sup></xref><xref ref-type="corresp" rid="cor001">*</xref></contrib></contrib-group><aff id="aff001"><label>1</label><addr-line>Department of Mathematics, Statistics and Physics, College of Arts and Sciences, Qatar University, 2713 Doha, Qatar</addr-line></aff><aff id="aff002"><label>2</label><addr-line>Department of Statistics, Federal University of Technology, Akure, Nigeria</addr-line></aff><aff id="aff003"><label>3</label><addr-line>Department of Public Health, College of Health Sciences, Qatar University, 2713 Doha, Qatar</addr-line></aff><contrib-group><contrib contrib-type="editor"><name><surname>Khatab</surname><given-names>Khaled</given-names></name><role>Editor</role><xref ref-type="aff" rid="edit1"></xref></contrib></contrib-group><aff id="edit1"><addr-line>UNITED KINGDOM</addr-line></aff><author-notes><fn fn-type="COI-statement" id="coi001"><p><bold>Competing Interests: </bold>The authors have declared that no competing interests exist.</p></fn><corresp id="cor001">* E-mail: <email>fhanna@qu.edu.qa</email></corresp></author-notes><pub-date pub-type="collection"><year>2017</year></pub-date><pub-date pub-type="epub"><day>31</day><month>7</month><year>2017</year></pub-date><volume>12</volume><issue>7</issue><elocation-id>e0181215</elocation-id><history><date date-type="received"><day>3</day><month>10</month><year>2016</year></date><date date-type="accepted"><day>28</day><month>6</month><year>2017</year></date></history><permissions><copyright-statement>© 2017 Adegboye et al</copyright-statement><copyright-year>2017</copyright-year><copyright-holder>Adegboye et al</copyright-holder><license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</ext-link>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p></license></permissions><self-uri content-type="pdf" xlink:href="pone.0181215.pdf"></self-uri><abstract><sec id="sec001"><title>Background</title><p>Middle East respiratory syndrome coronavirus is a contagious respiratory pathogen that is contracted via close contact with an infected subject. Transmission of the pathogen has occurred through animal-to-human contact at first followed by human-to-human contact within families and health care facilities.</p></sec><sec id="sec002"><title>Data and methods</title><p>This study is based on a retrospective analysis of the Middle East respiratory syndrome coronavirus outbreak in the Kingdom of Saudi Arabia between June 2012 and July 2015. A Geoadditive variable model for binary outcomes was applied to account for both individual level risk factors as well spatial variation via a fully Bayesian approach.</p></sec><sec id="sec003"><title>Results</title><p>Out of 959 confirmed cases, 642 (67%) were males and 317 (33%) had died. Three hundred and sixty four (38%) cases occurred in Ar Riyad province, while 325 (34%) cases occurred in Makkah. Individuals with some comorbidity had a significantly higher likelihood of dying from MERS-CoV compared with those who did not suffer comorbidity [Odds ratio (OR) = 2.071; 95% confidence interval (CI): 1.307, 3.263]. Health-care workers were significantly less likely to die from the disease compared with non-health workers [OR = 0.372, 95% CI: 0.151, 0.827]. Patients who had fatal clinical experience and those with clinical and subclinical experiences were equally less likely to die from the disease compared with patients who did not have fatal clinical experience and those without clinical and subclinical experiences respectively. The odds of dying from the disease was found to increase as age increased beyond 25 years and was much higher for individuals with any underlying comorbidities.</p></sec><sec id="sec004"><title>Conclusion</title><p>Interventions to minimize mortality from the Middle East respiratory syndrome coronavirus should particularly focus individuals with comorbidity, non-health-care workers, patients with no clinical fatal experience, and patients without any clinical and subclinical experiences.</p></sec></abstract><funding-group><funding-statement>The authors recieved no specific funding for this work. All data analyzed in this study were publicly available.</funding-statement></funding-group><counts><fig-count count="4"></fig-count><table-count count="2"></table-count><page-count count="15"></page-count></counts><custom-meta-group><custom-meta id="data-availability"><meta-name>Data Availability</meta-name><meta-value>Data are made publicly available by Dr. Andrew Rambaut from <ext-link ext-link-type="uri" xlink:href="http://epidemic.bio.ed.ac.uk/coronavirus_background">http://epidemic.bio.ed.ac.uk/coronavirus_background</ext-link>.</meta-value></custom-meta></custom-meta-group></article-meta><notes><title>Data Availability</title><p>Data are made publicly available by Dr. Andrew Rambaut from <ext-link ext-link-type="uri" xlink:href="http://epidemic.bio.ed.ac.uk/coronavirus_background">http://epidemic.bio.ed.ac.uk/coronavirus_background</ext-link>.</p></notes></front><body><sec sec-type="intro" id="sec005"><title>Introduction</title><p>Middle East respiratory syndrome coronavirus (MERS-CoV) is a respiratory contagious pathogen that is contracted via close contact with an infected subject [<xref rid="pone.0181215.ref001" ref-type="bibr">1</xref>]. MERS-CoV transmission had been known to occur via animals-to-humans; however, subsequent cases of human-to-human transmission have resulted in households and health care facility outbreaks have been documented [<xref rid="pone.0181215.ref002" ref-type="bibr">2</xref>–<xref rid="pone.0181215.ref008" ref-type="bibr">8</xref>]. The disease appears to have been transmitted from camels to humans, and recent studies have revealed an association among the virus found in humans with that found in camels [<xref rid="pone.0181215.ref009" ref-type="bibr">9</xref>]. Additionally, some studies have found antibodies to the virus in camels located in Africa and the Middle East [<xref rid="pone.0181215.ref010" ref-type="bibr">10</xref>]. In 2012, dozens of cases of people infected with MERS-CoV were reported in the Kingdom of Saudi Arabia (KSA) [<xref rid="pone.0181215.ref009" ref-type="bibr">9</xref>]. It has been reported that the disease has been fatal in 40% of confirmed cases [<xref rid="pone.0181215.ref011" ref-type="bibr">11</xref>].</p><p>Outbreaks of the disease have exposed the general populace, particularly health-care workers, in different countries to a greater risk, especially in the Arabian Peninsula where most of the cases have been reported. The epidemic has serious public health implication. It is suspected that people with pre-existing chronic medical conditions (comorbidities) are more prone to being infected by the illness or to developing a severe case resulting in fatality [<xref rid="pone.0181215.ref012" ref-type="bibr">12</xref>]. Patients with chronic diseases such as diabetes, chronic lung disease and heart conditions especially older males are at higher risk [<xref rid="pone.0181215.ref013" ref-type="bibr">13</xref>, <xref rid="pone.0181215.ref014" ref-type="bibr">14</xref>]. Strong links between health-care facilities and the outbreak of the disease has also been found in Jeddah, where the majority of patients were in contact with other patients or health-care workers [<xref rid="pone.0181215.ref015" ref-type="bibr">15</xref>]. Elsewhere, the transmission of MERS-CoV in household contacts revealed that an outcome of approximately 5% as the rate of secondary transmission occurred at home [<xref rid="pone.0181215.ref012" ref-type="bibr">12</xref>].</p><p>The Centers for Disease Control and Prevention indicated that most index case-patients have either resided in, or have travelled to areas neighboring the Arabian Peninsula, specifically, Saudi Arabia, the United Arab Emirates, Qatar, Jordan, Oman and Kuwait [<xref rid="pone.0181215.ref016" ref-type="bibr">16</xref>, <xref rid="pone.0181215.ref017" ref-type="bibr">17</xref>]. Although the outbreak has been mostly within the Arabian Peninsula, and concentrated in major cities and towns, a few cases have been reported in western countries and more recently, in South Korea [<xref rid="pone.0181215.ref018" ref-type="bibr">18</xref>]. The epidemic has understandably caused serious travel panic among the general public as well as among health-care workers and policy makers worldwide. Going by the number of new cases of international occurrence of MERS-CoV outside the Arabian Peninsula [<xref rid="pone.0181215.ref019" ref-type="bibr">19</xref>], the question that should be on everyone’s mind right now is, especially since the disease was reported in South Korea, is what is the likelihood of similar outbreaks in countries in close proximity with the origin of the disease?</p><p>Most MERS-CoV cases reported have probably acquired infection through human-to-human transmission [<xref rid="pone.0181215.ref014" ref-type="bibr">14</xref>]. Among 144 confirmed and 17 probable cases analyzed by the MERS-CoV Research Group in November, 2013, 95 (59%) were classified as secondary cases with epidemiological links to other confirmed cases [<xref rid="pone.0181215.ref020" ref-type="bibr">20</xref>]. Among these, most acquired the infection in health-care settings (63.2%), followed by those infected in household settings (13.7%) [<xref rid="pone.0181215.ref020" ref-type="bibr">20</xref>].</p><p>The epidemiologic features of the disease are difficult to determine with the currently available information. The analyses of the disease outbreaks will be a versatile tool for studying and understanding transmission and spread of the disease. It will be useful in cubing its upsurge, and possibly its containment or eradication. Yesterday, it was AIDS, today Ebola, MERS-CoV and Zika. What will it be tomorrow? It is, therefore, a matter of urgency to examine the likelihood of fatality as a result of MERS, keeping in mind the associations of individual- and work-related risk factors with the disease. The present paper aims to use geoadditive regression model [<xref rid="pone.0181215.ref021" ref-type="bibr">21</xref>] to elucidate the epidemiological risk factors and geographical distribution of the transmission and severity of the outbreak. Specifically, we investigated the effect of comorbidity and other individual- and work related- level risk factors including the geographical spread of mortality from MERS-CoV across the regions of KSA.</p><p>The motivating dataset for this study is introduced in section 2, while Section 3 presents the modeling technique. The results and discussion of the findings are presented in section 4 and 5 respectively. Findings from this study will help public health practitioners, policy makers and program managers monitor and design intervention strategies aimed at minimizing deaths due to the Middle East Respiratory Syndrome Coronavirus in the Arabian Peninsula.</p></sec><sec sec-type="materials|methods" id="sec006"><title>Materials and methods</title><sec id="sec007"><title>Data sources</title><p>This study was based on a retrospective data on the Middle East respiratory syndrome coronavirus (MERS-CoV) outbreak in the Kingdom of Saudi Arabia (KSA) between June 6, 2012 and July 17, 2015. The data set was the case-by-case data list compiled and regularly maintained by Dr. Andrew Rambaut [<xref rid="pone.0181215.ref022" ref-type="bibr">22</xref>] from various sources including World Health Organization(WHO) bulletins, Ministry of Health of the Kingdom of Saudi Arabia and media reports. MERS-CoV cases were confirmed via real-time RNA-positive using Reverse transcription polymerase chain reaction (RT-PCR) showing positive PCR on at least two specific genomic targets upstream E protein (upE) and ORF1a or a single positive target (upE) with sequencing of a second target (RdRpSeq assay) or N gene (NSeq assay) [<xref rid="pone.0181215.ref023" ref-type="bibr">23</xref>]. See <xref ref-type="fig" rid="pone.0181215.g001">Fig 1</xref> for the map of the crude rates and counts of infected MERS cases across the KSA created from case-by-case data.</p><fig id="pone.0181215.g001" orientation="portrait" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0181215.g001</object-id><label>Fig 1</label><caption><title>Map of Kingdom of Saudi Arabia showing the distribution of (a) the number of MERS cases in the 13 regions of Saudi Arabia, (b) the crude mortality rate for MERS-CoV disease.</title><p>The maps are based on regional aggregated counts of MERS cases over the study period.</p></caption><graphic xlink:href="pone.0181215.g001"></graphic></fig><p>The outcome of interest in this study is the survival status of the infected individual (dead/alive). The survival status of an infected individual is determined by whether the individual is dead or alive at the time of reporting [<xref rid="pone.0181215.ref022" ref-type="bibr">22</xref>]. Based on available data and recent literature [<xref rid="pone.0181215.ref024" ref-type="bibr">24</xref>], the following characteristics were used as individual level risk: age (in years) and gender, clinical outcome, region of infection, history of contact with animal, history contact with camels, whether the patient is a health-care worker (including all personnel that work in a health-care facility), presence or absence of any comorbidities in a patient, where or through who the patient contracted the disease (if known) and whether the patient is a primary contact (the first case within a defined group) or a secondary contact (individual infected by primary contact). The region of residence of the respondents was geo-referenced and used for the spatial analysis. <xref rid="pone.0181215.t001" ref-type="table">Table 1</xref> presents the frequency distribution of the recorded cases based on the variables considered.</p><table-wrap id="pone.0181215.t001" orientation="portrait" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0181215.t001</object-id><label>Table 1</label><caption><title>Risk factors analyzed in MERS-CoV mortality data with chi-square test.</title></caption><alternatives><graphic id="pone.0181215.t001g" xlink:href="pone.0181215.t001"></graphic><table frame="box" rules="all" border="0"><colgroup span="1"><col align="left" valign="middle" span="1"></col><col align="left" valign="middle" span="1"></col><col align="left" valign="middle" span="1"></col><col align="left" valign="middle" span="1"></col></colgroup><thead><tr><th align="left" style="border-top:thick" rowspan="1" colspan="1">Risk factors</th><th align="left" style="border-top:thick" rowspan="1" colspan="1">Category<xref ref-type="table-fn" rid="t001fn001"><sup>‡</sup></xref></th><th align="left" style="border-top:thick" rowspan="1" colspan="1">% Dead<xref ref-type="table-fn" rid="t001fn002"><sup>†</sup></xref></th><th align="right" style="border-top:thick" rowspan="1" colspan="1"><italic>χ</italic><sup>2</sup><italic>p</italic> − <italic>value</italic></th></tr></thead><tbody><tr><td align="left" rowspan="3" colspan="1">Gender</td><td align="left" rowspan="1" colspan="1">Male (65.7%)</td><td align="left" rowspan="1" colspan="1">28%</td><td align="char" char="." rowspan="1" colspan="1">0.0259</td></tr><tr><td align="left" rowspan="1" colspan="1">Female (32.6%)</td><td align="left" rowspan="1" colspan="1">36%</td><td align="right" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">NA (1.8%)</td><td align="left" rowspan="1" colspan="1"></td><td align="right" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="2" colspan="1">Comorbidity</td><td align="left" rowspan="1" colspan="1">Present (52.7%)</td><td align="left" rowspan="1" colspan="1">46.7%</td><td align="char" char="." rowspan="1" colspan="1">< 0.0001</td></tr><tr><td align="left" rowspan="1" colspan="1">Absent(47.3%)</td><td align="left" rowspan="1" colspan="1">17.1%</td><td align="right" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="3" colspan="1">Comorb-cat</td><td align="left" rowspan="1" colspan="1">None (47.3%)</td><td align="left" rowspan="1" colspan="1">17.1%</td><td align="char" char="." rowspan="1" colspan="1">< 0.0001</td></tr><tr><td align="left" rowspan="1" colspan="1">One (14.8%)</td><td align="left" rowspan="1" colspan="1">14.8%</td><td align="right" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">More than one (37.9%)</td><td align="left" rowspan="1" colspan="1">49.0%</td><td align="right" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="2" colspan="1">Animal Contact</td><td align="left" rowspan="1" colspan="1">Yes (8.9%)</td><td align="left" rowspan="1" colspan="1">38.8%</td><td align="char" char="." rowspan="1" colspan="1">0.2215</td></tr><tr><td align="left" rowspan="1" colspan="1">No (91.1%)</td><td align="left" rowspan="1" colspan="1">32.3%</td><td align="right" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="2" colspan="1">Camel Contact</td><td align="left" rowspan="1" colspan="1">Yes (7%)</td><td align="left" rowspan="1" colspan="1">37.3%</td><td align="char" char="." rowspan="1" colspan="1">0.4228</td></tr><tr><td align="left" rowspan="1" colspan="1">No (93%)</td><td align="left" rowspan="1" colspan="1">32.5%</td><td align="right" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="2" colspan="1">Health Care Worker</td><td align="left" rowspan="1" colspan="1">Yes (13%)</td><td align="left" rowspan="1" colspan="1">8.6%</td><td align="char" char="." rowspan="1" colspan="1">< 0.0001</td></tr><tr><td align="left" rowspan="1" colspan="1">No (87%)</td><td align="left" rowspan="1" colspan="1">36.6%</td><td align="right" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="4" colspan="1">Clinical</td><td align="left" rowspan="1" colspan="1">Fatal (21.2%)</td><td align="left" rowspan="1" colspan="1">94.6%</td><td align="char" char="." rowspan="1" colspan="1">< 0.0001</td></tr><tr><td align="left" rowspan="1" colspan="1">Clinical (60.6%)</td><td align="left" rowspan="1" colspan="1">20.6%</td><td align="right" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Subclinical (15.8%)</td><td align="left" rowspan="1" colspan="1">1.3%</td><td align="right" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">NA (2.2%)</td><td align="left" rowspan="1" colspan="1"></td><td align="right" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="5" colspan="1">Where-contracted</td><td align="left" rowspan="1" colspan="1">Family or Community 19.1%</td><td align="left" rowspan="1" colspan="1">40.4%</td><td align="char" char="." rowspan="1" colspan="1">< 0.0001</td></tr><tr><td align="left" rowspan="1" colspan="1">Hospital (11.6%)</td><td align="left" rowspan="1" colspan="1">44.6%</td><td align="right" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">HCW (2.7%)</td><td align="left" rowspan="1" colspan="1">7.7%</td><td align="right" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">No Contact (23.4%)</td><td align="left" rowspan="1" colspan="1">18.8%</td><td align="right" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">NA (38.2%)</td><td align="left" rowspan="1" colspan="1"></td><td align="right" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="2" colspan="1">Secondary Contact</td><td align="left" rowspan="1" colspan="1">Yes (40%)</td><td align="left" rowspan="1" colspan="1">25.7%</td><td align="char" char="." rowspan="1" colspan="1">0.0001</td></tr><tr><td align="left" rowspan="1" colspan="1">No (60%)</td><td align="left" rowspan="1" colspan="1">37.6%</td><td align="right" rowspan="1" colspan="1"></td></tr><tr><td align="left" style="border-bottom:thick" rowspan="1" colspan="1">Ageat onset</td><td align="left" style="border-bottom:thick" rowspan="1" colspan="1">Median = 52years (IQR = 37- 65)</td><td align="left" style="border-bottom:thick" rowspan="1" colspan="1"></td><td align="char" char="." style="border-bottom:thick" rowspan="1" colspan="1">< 0.0001</td></tr></tbody></table></alternatives><table-wrap-foot><fn id="t001fn001"><p><sup>‡</sup>: Percentage based on number of infected individuals within each category.</p></fn><fn id="t001fn002"><p><sup>†</sup>: The percentages were calculated based on the number of deaths within each category.</p></fn><fn id="t001fn003"><p>(For example, 36% of infected females died of the disease).</p></fn></table-wrap-foot></table-wrap></sec><sec id="sec008"><title>Exploratory analysis</title><p>Firstly, univariate analyses were carried out to explore the relationship between the patient survival status and several risks and demographical factors using SAS 9.3 [<xref rid="pone.0181215.ref025" ref-type="bibr">25</xref>]. We present the frequency of risk factors and survival status as percentages of deaths within each category (<xref rid="pone.0181215.t001" ref-type="table">Table 1</xref>). To identify associations between categorical risk factors and survival status of MERS-CoV disease, we used Pearson’s chi-square statistics for testing independence in contingency tables [<xref rid="pone.0181215.ref026" ref-type="bibr">26</xref>]. The chi-square test measures how “close” the observed values are to those which would be expected under the fitted model.</p><p>Similarly, local spatial heterogeneity of MERS disease was evaluated in SaTScan [<xref rid="pone.0181215.ref027" ref-type="bibr">27</xref>]. SaTScan is widely used for local cluster detection, which is good for detecting large clusters as well as to evaluate outliers when the outlier pattern is very strong or a small maximum search window is used [<xref rid="pone.0181215.ref028" ref-type="bibr">28</xref>]. The idea of Poisson model based SaTScan circular version is to recognize sets of regions where the disease count is significantly larger than expected [<xref rid="pone.0181215.ref029" ref-type="bibr">29</xref>]. SaTScan’s Poisson log likelihood ratio statistics was applied to regional aggregated MERS counts in circular windows of increasing radius centered at each region centroid with a maximum cluster size of provinces covering 50% of the national population. Clusters with the largest test statistics were tested for statistical significance. This significance was assessed using the default 999 Monte Carlo trials drawn under the null hypothesis that the observed case count represents the census distribution. If the p-value derived by ranking a test statistic calculated from observed data against the 999 statistics calculated similarly for the Monte Carlo trials was below our alpha level of 5%, then the observed cluster was considered significant [<xref rid="pone.0181215.ref027" ref-type="bibr">27</xref>–<xref rid="pone.0181215.ref029" ref-type="bibr">29</xref>]. Additionally, the Wang’s <italic>q</italic>-Statistics [<xref rid="pone.0181215.ref030" ref-type="bibr">30</xref>, <xref rid="pone.0181215.ref031" ref-type="bibr">31</xref>] was used to test the global stratified spatial heterogeneity of occurrence of MERS disease. In all analyses, p-values of less than 0.05 were considered statistically significant in all tests.</p></sec><sec id="sec009"><title>Statistical analysis</title><p>Our approach to spatial analysis is based on the framework of structured additive regression model [<xref rid="pone.0181215.ref021" ref-type="bibr">21</xref>]. Geoadditive Bayesian models have been used and described in details in several studies [<xref rid="pone.0181215.ref032" ref-type="bibr">32</xref>–<xref rid="pone.0181215.ref034" ref-type="bibr">34</xref>]. In brevity, suppose <italic>y</italic><sub><italic>i</italic></sub> is the survival status of an infected individual <italic>i</italic> at location <italic>s</italic><sub><italic>i</italic></sub> and <italic>υ</italic> is a vector of observed covariates, which could be categorical or continuous. We define <italic>y</italic><sub><italic>i</italic></sub> = 1 indicating the individual die of MERS disease or <italic>y</italic><sub><italic>i</italic></sub> = 0 otherwise. <italic>y</italic><sub><italic>i</italic></sub> is assumed to have a binomial distribution given as:
<disp-formula id="pone.0181215.e001"><alternatives><graphic xlink:href="pone.0181215.e001.jpg" id="pone.0181215.e001g" mimetype="image" position="anchor" orientation="portrait"></graphic><mml:math id="M1"><mml:mtable displaystyle="true"><mml:mtr><mml:mtd columnalign="right"><mml:mrow><mml:msub><mml:mi>y</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mi>B</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>p</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:math></alternatives><label>(1)</label></disp-formula>where the probability “<italic>p</italic><sub><italic>i</italic></sub>” of dying from the infectious disease is given as:
<disp-formula id="pone.0181215.e002"><alternatives><graphic xlink:href="pone.0181215.e002.jpg" id="pone.0181215.e002g" mimetype="image" position="anchor" orientation="portrait"></graphic><mml:math id="M2"><mml:mtable displaystyle="true"><mml:mtr><mml:mtd columnalign="right"><mml:mrow><mml:msub><mml:mi>p</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mi>P</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:msub><mml:mi>y</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>e</mml:mi><mml:mi>x</mml:mi><mml:mi>p</mml:mi><mml:mo>(</mml:mo><mml:msub><mml:mi>η</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>+</mml:mo><mml:mi>e</mml:mi><mml:mi>x</mml:mi><mml:mi>p</mml:mi><mml:mo>(</mml:mo><mml:msub><mml:mi>η</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:mfrac></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:math></alternatives><label>(2)</label></disp-formula></p><p>The predictor indicator “<italic>η</italic><sub><italic>i</italic></sub>”, is a known response function with a logit link function as specified in <xref ref-type="disp-formula" rid="pone.0181215.e003">Eq 3</xref> [<xref rid="pone.0181215.ref032" ref-type="bibr">32</xref>]. The influence of the covariates can be modelled assuming a logit link function on the proportion.</p><p>To be able to incorporate spatial covariate and to model the continuous variable, age using smooth function, we adopt the logistic model with structured additive predictors defined as:
<disp-formula id="pone.0181215.e003"><alternatives><graphic xlink:href="pone.0181215.e003.jpg" id="pone.0181215.e003g" mimetype="image" position="anchor" orientation="portrait"></graphic><mml:math id="M3"><mml:mtable displaystyle="true"><mml:mtr><mml:mtd columnalign="right"><mml:mrow><mml:msub><mml:mi>η</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>g</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:msub><mml:mi>p</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>g</mml:mi><mml:mo>{</mml:mo><mml:mfrac><mml:msub><mml:mi>p</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mrow><mml:mn>1</mml:mn><mml:mo>-</mml:mo><mml:msub><mml:mi>p</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mfrac><mml:mo>}</mml:mo><mml:mo>=</mml:mo><mml:mi>f</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mi>x</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:msub><mml:mi>f</mml:mi><mml:mrow><mml:mi>g</mml:mi><mml:mi>e</mml:mi><mml:mi>o</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:msub><mml:mi>s</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:msup><mml:mi>υ</mml:mi><mml:mo>′</mml:mo></mml:msup><mml:mi>β</mml:mi></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:math></alternatives><label>(3)</label></disp-formula>where <italic>f</italic>(<italic>x</italic>) is a nonlinear effect smooth function assumed for age, <italic>f</italic><sub><italic>geo</italic></sub>(<italic>s</italic><sub><italic>i</italic></sub>) is the geographical effect, and <italic>β</italic> is a vector of fixed effect parameters for the categorical covariates. The predictor will be of the form <italic>η</italic><sub><italic>i</italic></sub> = <italic>β</italic><sub>1</sub> ⋅ Comorbidity + … + <italic>β</italic><sub>7</sub> ⋅ Clinical + <italic>f</italic><sub>1</sub>(<italic>age</italic>) + <italic>f</italic><sub><italic>geo</italic></sub>(<italic>region</italic>). We also included an interaction term between comorbidity and age and modeled that using smooth function. The aim was to examine how comorbidity varies smoothly across age (The results of this model are presented in <xref rid="pone.0181215.t002" ref-type="table">Table 2</xref>).</p><table-wrap id="pone.0181215.t002" orientation="portrait" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0181215.t002</object-id><label>Table 2</label><caption><title>Posterior odds ratio and 95% credible interval of the effect of various categorical variables on mortality due to MERS-CoV.</title></caption><alternatives><graphic id="pone.0181215.t002g" xlink:href="pone.0181215.t002"></graphic><table frame="box" rules="all" border="0"><colgroup span="1"><col align="left" valign="middle" span="1"></col><col align="left" valign="middle" span="1"></col><col align="left" valign="middle" span="1"></col></colgroup><thead><tr><th align="left" style="border-top:thick" rowspan="1" colspan="1">Variable</th><th align="left" style="border-top:thick" rowspan="1" colspan="1">Posterior odds ratio</th><th align="left" style="border-top:thick" rowspan="1" colspan="1">Credible interval</th></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">Comorbidity (Present vs Absent)</td><td align="char" char="." rowspan="1" colspan="1">2.071</td><td align="left" rowspan="1" colspan="1">1.307, 3.263</td></tr><tr><td align="left" rowspan="1" colspan="1">Animal contact (Yes vs No)</td><td align="char" char="." rowspan="1" colspan="1">1.634</td><td align="left" rowspan="1" colspan="1">0.527, 4.847</td></tr><tr><td align="left" rowspan="1" colspan="1">Camel contact (Yes vs No)</td><td align="char" char="." rowspan="1" colspan="1">0.741</td><td align="left" rowspan="1" colspan="1">0.229, 2.534</td></tr><tr><td align="left" rowspan="1" colspan="1">Health Care Worker (Yes vs No)</td><td align="char" char="." rowspan="1" colspan="1">0.372</td><td align="left" rowspan="1" colspan="1">0.151, 0.827</td></tr><tr><td align="left" rowspan="1" colspan="1">Secondary Contact (Yes vs No)</td><td align="char" char="." rowspan="1" colspan="1">1.089</td><td align="left" rowspan="1" colspan="1">0.696, 1.658</td></tr><tr><td align="left" rowspan="1" colspan="1">Sex (Male vs Female)</td><td align="char" char="." rowspan="1" colspan="1">1.197</td><td align="left" rowspan="1" colspan="1">0.787, 1.821</td></tr><tr><td align="left" rowspan="1" colspan="1">Clinical</td><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Clinical vs Fatal</td><td align="char" char="." rowspan="1" colspan="1">0.040</td><td align="left" rowspan="1" colspan="1">0.025, 0.063</td></tr><tr><td align="left" style="border-bottom:thick" rowspan="1" colspan="1">Subclinical vs Fatal</td><td align="char" char="." style="border-bottom:thick" rowspan="1" colspan="1">0.004</td><td align="left" style="border-bottom:thick" rowspan="1" colspan="1">0.001, 0.016</td></tr></tbody></table></alternatives></table-wrap><p>Parameters estimation follow from the Bayesian context whereby all parameters and functions are considered as random variables and appropriate priors are assumed. Independent diffuse priors are assumed to estimate the categorical covariates. For the smooth function for the nonlinear effects of age, Bayesian P-splines prior was assumed [<xref rid="pone.0181215.ref035" ref-type="bibr">35</xref>, <xref rid="pone.0181215.ref036" ref-type="bibr">36</xref>]. Following [<xref rid="pone.0181215.ref035" ref-type="bibr">35</xref>–<xref rid="pone.0181215.ref037" ref-type="bibr">37</xref>], the P-spline assumes that the spline can be written as a linear combination of basis functions (B-spline: <italic>B</italic><sub><italic>j</italic></sub>), denoted by:
<disp-formula id="pone.0181215.e004"><alternatives><graphic xlink:href="pone.0181215.e004.jpg" id="pone.0181215.e004g" mimetype="image" position="anchor" orientation="portrait"></graphic><mml:math id="M4"><mml:mtable displaystyle="true"><mml:mtr><mml:mtd columnalign="right"><mml:mrow><mml:msub><mml:mi>f</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>j</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>j</mml:mi></mml:munderover><mml:msub><mml:mi>β</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:msub><mml:mi>B</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:math></alternatives></disp-formula>The <italic>β</italic><sub><italic>j</italic></sub> are unknown regression coefficients that can be defined to follow a first or second order random walks smoothness <italic>β</italic><sub><italic>j</italic></sub> = 2<italic>β</italic><sub><italic>j</italic>−1</sub> − <italic>β</italic><sub><italic>j</italic>−2</sub> + <italic>u</italic><sub><italic>j</italic></sub> with Gaussian errors <inline-formula id="pone.0181215.e005"><alternatives><graphic xlink:href="pone.0181215.e005.jpg" id="pone.0181215.e005g" mimetype="image" position="anchor" orientation="portrait"></graphic><mml:math id="M5"><mml:mrow><mml:msub><mml:mi>u</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>∼</mml:mo><mml:mi>N</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:msubsup><mml:mi>τ</mml:mi><mml:mi>j</mml:mi><mml:mn>2</mml:mn></mml:msubsup><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>. The smoothness of <italic>f</italic> is control by the variance parameter <inline-formula id="pone.0181215.e006"><alternatives><graphic xlink:href="pone.0181215.e006.jpg" id="pone.0181215.e006g" mimetype="image" position="anchor" orientation="portrait"></graphic><mml:math id="M6"><mml:msubsup><mml:mi>τ</mml:mi><mml:mi>j</mml:mi><mml:mn>2</mml:mn></mml:msubsup></mml:math></alternatives></inline-formula>, which is also considered as a random variable and a highly dispersed inverse gamma prior assumed for the variance, <inline-formula id="pone.0181215.e007"><alternatives><graphic xlink:href="pone.0181215.e007.jpg" id="pone.0181215.e007g" mimetype="image" position="anchor" orientation="portrait"></graphic><mml:math id="M7"><mml:mrow><mml:msubsup><mml:mi>τ</mml:mi><mml:mi>j</mml:mi><mml:mn>2</mml:mn></mml:msubsup><mml:mo>∼</mml:mo><mml:mi>I</mml:mi><mml:mi>G</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:msub><mml:mi>a</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>b</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>. This way, it is jointly estimated with the regression coefficients [<xref rid="pone.0181215.ref036" ref-type="bibr">36</xref>].</p><p>The spatial effects <italic>f</italic><sub><italic>geo</italic></sub>(<italic>s</italic><sub><italic>i</italic></sub>) = <italic>β</italic><sub><italic>geo</italic>,<italic>s</italic></sub> was modeled assuming a Gaussian Markov random field prior [<xref rid="pone.0181215.ref036" ref-type="bibr">36</xref>, <xref rid="pone.0181215.ref038" ref-type="bibr">38</xref>] defined by:
<disp-formula id="pone.0181215.e008"><alternatives><graphic xlink:href="pone.0181215.e008.jpg" id="pone.0181215.e008g" mimetype="image" position="anchor" orientation="portrait"></graphic><mml:math id="M8"><mml:mtable displaystyle="true"><mml:mtr><mml:mtd columnalign="right"><mml:mrow><mml:msub><mml:mi>β</mml:mi><mml:mrow><mml:mi>g</mml:mi><mml:mi>e</mml:mi><mml:mi>o</mml:mi><mml:mo>,</mml:mo><mml:mi>s</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:mo>|</mml:mo></mml:mrow><mml:msub><mml:mi>β</mml:mi><mml:mrow><mml:mi>g</mml:mi><mml:mi>e</mml:mi><mml:mi>o</mml:mi><mml:mo>,</mml:mo><mml:mi>u</mml:mi></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:mi>u</mml:mi><mml:mo>≠</mml:mo><mml:mi>s</mml:mi><mml:mo>∼</mml:mo><mml:mi>N</mml:mi><mml:mo>(</mml:mo><mml:munder><mml:mo>∑</mml:mo><mml:mrow><mml:mi>u</mml:mi><mml:mo>∈</mml:mo><mml:msub><mml:mi>∂</mml:mi><mml:mi>s</mml:mi></mml:msub></mml:mrow></mml:munder><mml:mfrac><mml:mn>1</mml:mn><mml:msub><mml:mi>N</mml:mi><mml:mi>s</mml:mi></mml:msub></mml:mfrac><mml:msub><mml:mi>β</mml:mi><mml:mrow><mml:mi>g</mml:mi><mml:mi>e</mml:mi><mml:mi>o</mml:mi><mml:mo>,</mml:mo><mml:mi>u</mml:mi></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:mfrac><mml:msup><mml:mi>τ</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:msub><mml:mi>N</mml:mi><mml:mi>s</mml:mi></mml:msub></mml:mfrac><mml:mo>)</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:math></alternatives><label>(4)</label></disp-formula>where <italic>N</italic><sub><italic>s</italic></sub> is the number of adjacent regions, and ∂<sub><italic>s</italic></sub> denotes the regions which are neighbors of region <italic>s</italic>. This defines areas as neighbours if they share a common boundary. The spatial variance was also assigned an inverse Gamma prior.</p><p>Sensitivity to the choice of hyper-priors was investigated by varying the values of <italic>a</italic><sub><italic>j</italic></sub> and <italic>b</italic><sub><italic>j</italic></sub>. The results turned out to be indistinguishable. Findings reported are based on <italic>a</italic><sub><italic>j</italic></sub> = <italic>b</italic><sub><italic>j</italic></sub> = 0.001. The posterior distribution is intractable so, Markov chain Monte Carlo (MCMC) algorithm was adopted to generate sample from the posterior distributions, which allows for estimation and inference to be made for all parameters. The posterior odds ratios (OR) and their 95% confidence intervals (95% CI) were calculated using BayesX version 2.1 [<xref rid="pone.0181215.ref039" ref-type="bibr">39</xref>, <xref rid="pone.0181215.ref040" ref-type="bibr">40</xref>].</p></sec></sec><sec sec-type="results" id="sec010"><title>Results</title><sec id="sec011"><title>Exploratory data analysis</title><p><xref rid="pone.0181215.t001" ref-type="table">Table 1</xref> presents the summary profile characteristics and univariate analysis of the categorical variables in the dataset and age. 959 MERS cases were recorded in KSA during the study period with 317 (33%) deaths while 67 (7%) had contact with camels or camel products, 126 (13%) were health-care workers and 52.7% had some kind of comorbidity (<xref rid="pone.0181215.t001" ref-type="table">Table 1</xref>). Similarly, out of the 630 male patients, 28% died as a result of MERS-CoV while only 36% of the females died from the disease (<xref rid="pone.0181215.t001" ref-type="table">Table 1</xref>). The median age for males was 53.5 years (interquartile range 39-66) while the median age for females was 48 years (interquartile range 32-63).</p><p>Not all of the comorbidities were equally prevalent. While most of the patients in this study had some kind of underlying comorbidities (52.7% have at least one comorbidities), around 38% of all patients had more than one comorbidities with the most common being obesity, diabetes and hypertension (which occurred in more than 50% of those with any underlying comorbidity) (<xref rid="pone.0181215.t001" ref-type="table">Table 1</xref>). Others comorbidities were heart disease, respiratory disease, pneumonia, renal/kidney disease and asthma.</p><p>Pearson’s chi-square test of health outcomes between subgroups shows significant difference in gender, comorbidity, health-care worker, clinical outcome, contact type and secondary contact (<xref rid="pone.0181215.t001" ref-type="table">Table 1</xref>). About 3 out of every 10 males died of MERS disease, compared to 28% of the females. The percentage of health-care workers that died of MERS (8.73%) were much less than non-health care workers (36.5%), while 46.14% of persons with comorbidity died of MERS compared with 17.05% of those without comorbidity. Similarly, there effect of comorbidity on mortality from MERS-CoV was significant; patients who died of the disease were more likely to have one or more comorbidities with an odd ratios of 3.4 and 4.7 respectively.</p><p><xref ref-type="fig" rid="pone.0181215.g001">Fig 1</xref> shows the study area and the distribution of the number of infected people and the number of people who died of the disease in the 13 provinces of the KSA. Most of the MERS cases occurred in Ar Riyad (38%) and Makkah (34%) provinces. <xref ref-type="fig" rid="pone.0181215.g002">Fig 2</xref> shows the pyramids of the distribution of the mortality status for the 13 regions based on comorbidity status (upper part) and whether or not the individual was a health worker (lower part). From the pyramids, it is clear that the highest number of cases occurred in Ar Riyad followed by Makkah. The incidence of comorbidities was significantly higher among patients in Ar Riyad, Makkah and Ash Sharqiyah (about half of the cases of comorbidities occurred in these three regions). Al Bahah had the least cases of infected individuals. Similarly, Ar Riyad, Makkah and Ash Sharqiyah recorded the highest number of infected health-care works (<xref ref-type="fig" rid="pone.0181215.g002">Fig 2</xref> bottom). The proportion of health-care workers who died of MERS-CoV were smaller than the proportion of non health-care works who died of the disease.</p><fig id="pone.0181215.g002" orientation="portrait" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0181215.g002</object-id><label>Fig 2</label><caption><title>Pyramids showing the distribution of mortality among infected individuals with some kind of comorbidity (top) and health-care workers (HCW, bottom), in the 13 regions of Saudi Arabia.</title><p>The horizontal axis represents the number of MERS cases.</p></caption><graphic xlink:href="pone.0181215.g002"></graphic></fig></sec><sec id="sec012"><title>Spatial analysis</title><p>SaTScan for local cluster detection detects the area of Al Qasim as primary cluster with high rates after adjusting for all explanatory variables (Relative risk(RR) = 1.83, <italic>p</italic> − <italic>value</italic> < 0.0001) and the area of Aseer and Jizan as primary cluster for low rates (RR = 0.093, <italic>p</italic> − <italic>value</italic> < 0.0001) while Al Jawf, Riyadh and Hail were secondary cluster for low rates (RR = 0.51, <italic>p</italic> − <italic>value</italic> < 0.0001). The Wang’s <italic>q</italic>-statistics for global stratified spatial heterogeneity was 0.2285 using the geographical detector method [<xref rid="pone.0181215.ref030" ref-type="bibr">30</xref>, <xref rid="pone.0181215.ref031" ref-type="bibr">31</xref>]. The spatial stratified heterogeneity analysis indicated no significant stratified spatial heterogeneity of the district MERS incidence (<italic>q</italic> = 0.2285, <italic>p</italic> − <italic>value</italic> = 0.9444).</p><p>The estimated posterior odds ratio of mortality from MERS disease and corresponding 95% credibility intervals are shown in <xref rid="pone.0181215.t002" ref-type="table">Table 2</xref>. The results reveal that individuals with comorbidities were twice as likely to have died from MERS-CoV compared with those without comorbidities (OR = 2.071; CI: 1.307, 3.263). Estimates for those individuals that had animal or camel contact, those with secondary contact and results based on gender were not significant. However, individuals who were health-care workers were significantly less likely to have died from the disease compared with non-health workers (OR = 0.372, CI: 0.151, 0.827). Compared with patients who had fatal clinical experience, those with clinical and subclinical experiences were equally less likely to have died from the disease.</p><p><xref ref-type="fig" rid="pone.0181215.g003">Fig 3</xref> shows the estimated effects of age (a) and the estimated effects of comorbidity as it varies smoothly over age (interaction between comorbidity and age). Individuals aged 25 years or younger who suffered from MERS-CoV were less likely to have suffered mortality. Nevertheless, the odds of dying from the disease tended to increase as age increased beyond 25 years and was much higher for individuals with any underlying comorbidities.</p><fig id="pone.0181215.g003" orientation="portrait" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0181215.g003</object-id><label>Fig 3</label><caption><title>Posterior means of nonlinear effects of age on mortality due to MERS-CoV patients (a) without any underlying comorbidities, (b) with some kind of underlying comorbidities, together with 95% credible interval, adjusted for other covariates.</title></caption><graphic xlink:href="pone.0181215.g003"></graphic></fig><p>Results of the estimated total spatial variation in mortality due to MERS-CoV are presented in <xref ref-type="fig" rid="pone.0181215.g004">Fig 4</xref>. From <xref ref-type="fig" rid="pone.0181215.g004">Fig 4</xref>, individuals from provinces with red shading were less likely to have suffered mortality due to MERS-CoV but mortality was higher as the shading moves towards green colour. This implies evidence of significant geographical variation and clustering of mortality from MERS-CoV with lower risk (after adjusting for other variables) occurring in Riyadh, Ar’ar, Al Jawf and Jizan, and higher risk in Al Qasim.</p><fig id="pone.0181215.g004" orientation="portrait" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0181215.g004</object-id><label>Fig 4</label><caption><title>Map showing the posterior means of spatial effects in mortality due to MERS-CoV.</title></caption><graphic xlink:href="pone.0181215.g004"></graphic></fig></sec></sec><sec sec-type="conclusions" id="sec013"><title>Discussion</title><p>This study that was based on retrospective data of MERS-CoV outbreak in the KSA had 4 main findings. First, patients with comorbidities who were infected with MERS-CoV were as twice likely to die from it than those without comorbidities, after adjusting for confounders. Second, patients with 2 or more comorbidities were more likely to die from MERS-CoV than those with only one comorbidity. Third, health-care workers were 37% less likely to die from the viral infection when compared to non-health care workers. Fourth, our large study sample confirms that individuals under the age of 25, irrespective of comorbidity, and who suffer from MERS-CoV are less likely to die from it, in comparison to the older age groups and that the odds of dying from the disease increased with age.</p><p>A number of studies have looked at the epidemiological pattern of the MERS-CoV infection among the community; however, very few have looked at the pattern of deaths among those who are afflicted by the viral disease. The majority of previous studies were limited by the small sample size [<xref rid="pone.0181215.ref012" ref-type="bibr">12</xref>] except two recent ones [<xref rid="pone.0181215.ref014" ref-type="bibr">14</xref>, <xref rid="pone.0181215.ref024" ref-type="bibr">24</xref>].</p><p>Our findings are collectively consistent with the most recent studies on MERS-CoV published by Rivers at al., [<xref rid="pone.0181215.ref024" ref-type="bibr">24</xref>] and Alraddadi et al., [<xref rid="pone.0181215.ref014" ref-type="bibr">14</xref>]. While the work of Rivers and colleagues was practically impeccable, their analysis adopted Poisson regression models using a robust variance estimator without accounting for area-specific geographical effects to capture extra variation in the model. Ignoring spatial pattern in infectious disease may be inadequate to explain the variation in the occurrence of the disease due to space as it has been found that most diseases are location related [<xref rid="pone.0181215.ref041" ref-type="bibr">41</xref>, <xref rid="pone.0181215.ref042" ref-type="bibr">42</xref>]. Similarly, Alraddadi et al., [<xref rid="pone.0181215.ref014" ref-type="bibr">14</xref>] considered only primary MERS-CoV cases reported in Saudi Arabia during March–November 2014 in their study. They exclude cases with exposure to other cases of MERS-CoV, acute respiratory illness of unknown cause and those exposed to health-care settings within 14 days before illness onset [<xref rid="pone.0181215.ref014" ref-type="bibr">14</xref>].</p><p>In our study we adopted the Bayesian spatial modeling to allow for the exact analysis of random effects and coefficient models as well as assess the area-specific spatial effects associated with MERS disease. Assiri’s et al., [<xref rid="pone.0181215.ref012" ref-type="bibr">12</xref>] findings that those with existing health issues are more likely to die from the infection of MERS are also consistent with our findings; nevertheless, the above study only looked at 47 patients with MERS-CoV (28 deaths).</p><p>To further strengthen our investigation, we performed a sub-analysis using the “number of comorbidities”, to explore the dose response relationship between comorbidity and mortality from MERS-CoV. The subsequent analysis (univariate) showed that patients who died of MERS-CoV were three times more likely to have one or more comorbidities (OR: 3.4) and almost 5 times more likely to have 2 or more comorbidities (OR: 4.7), compared to patients without any underlying comorbidities. This is a significant finding as it further exposes the negative combined influence of comorbidities on survival, particularly when considering the rise in prevalence of non-communicable disease and the ageing population.</p><p>A joint and coordinated worldwide response is unquestionably crucial to tackle new infectious diseases and the threats posed by emerging new strands of viruses that have been able to cause fatal respiratory tract infections over the past decade. These coordinated efforts will optimistically fill major gaps in the understanding of the epidemiology and transmission of the disease. However, these efforts should take place in parallel with the efforts to reduce chronic-non-communicable diseases in our aging population.</p><p>The issue of comorbidity is posing further health threats in our time, with chronic and lifestyle diseases on the rise, particularly obesity, diabetes and heart disease. Our research findings and those similar, further warrant the need for effective and successful campaigns to combat chronic illness in the ageing population, not only to reduce mortality and defenselessness against novel and emerging infections, but also to improve the quality of life of these individuals. This is yet another reminder that older and sicker patients are the most vulnerable of all and, thus, require that extra care and watchfulness. Additionally, what makes the situation even more serious is that with today’s unhealthy routine, including occupational and sedentary lifestyle and the abundance of processed and fast foods, more and more people are prone to develop serious comorbidities at a younger age. The Gulf region is indeed not immune to all that as childhood obesity, diabetes and other non-communicable diseases are showing no signs of slowing down. A study from Saudi Arabia showed that more than 50% of Saudi people older than 50 years have diabetes [<xref rid="pone.0181215.ref043" ref-type="bibr">43</xref>]. However, in the studies by Assiri et al., [<xref rid="pone.0181215.ref012" ref-type="bibr">12</xref>] and Mackay et al., [<xref rid="pone.0181215.ref013" ref-type="bibr">13</xref>] the large number of people with MERS-CoV infection and chronic disease might have been due to the hospital outbreak where patients were first admitted. Our results are inline with a recent case-control study where previous medical conditions such as diabetes mellitus, heart disease, and smoking, were each independently associated with MERS-CoV disease [<xref rid="pone.0181215.ref014" ref-type="bibr">14</xref>]. Further case-control studies are needed to define the effect of comorbidities on susceptibility to, and associated mortality from, MERS-CoV infection.</p><p>One of the limitations of our study is that it is retrospective rather than prospective. Also, in some cases, infected persons that were admitted for unrelated medical conditions were not considered as having comorbidity and the disease that they were admitted for was not mentioned, although this was not common. There is also the possibility that some patients might have died after discharge; however, this is quite unlikely as patients released from hospital as recovered would have been unlikely to die from the disease without reporting back to the hospital and medical team when health deteriorated. Lastly, because in some cases patients history and contacts with animals or camel cannot be verified, there is a possibility of patients giving false or inaccurate information.</p></sec><sec sec-type="conclusions" id="sec014"><title>Conclusion</title><p>This study has revealed that individuals with comorbidity, non-health-care workers, patients with no clinical fatal experience, and patients without any clinical and subclinical experiences significantly increased the odds of death from MERS-CoV in the Arabian Peninsula. It is therefore imperative for public health practitioners, policy makers and program managers to principally target these individuals when they are formulating and implementing strategies to minimize deaths from this syndrome. 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