Early Characterization of the Severity and Transmissibility of Pandemic Influenza Using Clinical Episode Data from Multiple Populations
Identifieur interne : 000392 ( Pmc/Checkpoint ); précédent : 000391; suivant : 000393Early Characterization of the Severity and Transmissibility of Pandemic Influenza Using Clinical Episode Data from Multiple Populations
Auteurs : Pete Riley [États-Unis] ; Michal Ben-Nun [États-Unis] ; Jon A. Linker [États-Unis] ; Angelia A. Cost [États-Unis] ; Jose L. Sanchez [États-Unis] ; Dylan George [États-Unis] ; David P. Bacon [États-Unis] ; Steven Riley [États-Unis, Royaume-Uni]Source :
- PLoS Computational Biology [ 1553-734X ] ; 2015.
Abstract
The potential rapid availability of large-scale clinical episode data during the next influenza pandemic suggests an opportunity for increasing the speed with which novel respiratory pathogens can be characterized. Key intervention decisions will be determined by both the transmissibility of the novel strain (measured by the basic reproductive number
Url:
DOI: 10.1371/journal.pcbi.1004392
PubMed: 26402446
PubMed Central: 4581836
Affiliations:
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PMC:4581836Le document en format XML
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<front><div type="abstract" xml:lang="en"><p>The potential rapid availability of large-scale clinical episode data during the next influenza pandemic suggests an opportunity for increasing the speed with which novel respiratory pathogens can be characterized. Key intervention decisions will be determined by both the transmissibility of the novel strain (measured by the basic reproductive number <italic>R</italic>
<sub>0</sub>
) and its individual-level severity. The 2009 pandemic illustrated that estimating individual-level severity, as described by the proportion <italic>p</italic>
<sub><italic>C</italic>
</sub>
of infections that result in clinical cases, can remain uncertain for a prolonged period of time. Here, we use 50 distinct US military populations during 2009 as a retrospective cohort to test the hypothesis that real-time encounter data combined with disease dynamic models can be used to bridge this uncertainty gap. Effectively, we estimated the total number of infections in multiple early-affected communities using the model and divided that number by the known number of clinical cases. Joint estimates of severity and transmissibility clustered within a relatively small region of parameter space, with 40 of the 50 populations bounded by: <italic>p</italic>
<sub><italic>C</italic>
</sub>
, 0.0133–0.150 and <italic>R</italic>
<sub>0</sub>
, 1.09–2.16. These fits were obtained despite widely varying incidence profiles: some with spring waves, some with fall waves and some with both. To illustrate the benefit of specific pairing of rapidly available data and infectious disease models, we simulated a future moderate pandemic strain with <italic>p</italic>
<sub><italic>C</italic>
</sub>
approximately ×10 that of 2009; the results demonstrating that even before the peak had passed in the first affected population, <italic>R</italic>
<sub>0</sub>
and <italic>p</italic>
<sub><italic>C</italic>
</sub>
could be well estimated. This study provides a clear reference in this two-dimensional space against which future novel respiratory pathogens can be rapidly assessed and compared with previous pandemics.</p>
</div>
</front>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">PLoS Comput Biol</journal-id>
<journal-id journal-id-type="iso-abbrev">PLoS Comput. Biol</journal-id>
<journal-id journal-id-type="publisher-id">plos</journal-id>
<journal-id journal-id-type="pmc">ploscomp</journal-id>
<journal-title-group><journal-title>PLoS Computational Biology</journal-title>
</journal-title-group>
<issn pub-type="ppub">1553-734X</issn>
<issn pub-type="epub">1553-7358</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">26402446</article-id>
<article-id pub-id-type="pmc">4581836</article-id>
<article-id pub-id-type="publisher-id">PCOMPBIOL-D-15-00065</article-id>
<article-id pub-id-type="doi">10.1371/journal.pcbi.1004392</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject>
</subj-group>
</article-categories>
<title-group><article-title>Early Characterization of the Severity and Transmissibility of Pandemic Influenza Using Clinical Episode Data from Multiple Populations</article-title>
<alt-title alt-title-type="running-head">Severity and Transmissibility of Pandemic Influenza</alt-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Riley</surname>
<given-names>Pete</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
<xref ref-type="corresp" rid="cor001">*</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Ben-Nun</surname>
<given-names>Michal</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Linker</surname>
<given-names>Jon A.</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Cost</surname>
<given-names>Angelia A.</given-names>
</name>
<xref ref-type="aff" rid="aff002"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Sanchez</surname>
<given-names>Jose L.</given-names>
</name>
<xref ref-type="aff" rid="aff002"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>George</surname>
<given-names>Dylan</given-names>
</name>
<xref ref-type="aff" rid="aff003"><sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Bacon</surname>
<given-names>David P.</given-names>
</name>
<xref ref-type="aff" rid="aff004"><sup>4</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Riley</surname>
<given-names>Steven</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff005"><sup>5</sup>
</xref>
</contrib>
</contrib-group>
<aff id="aff001"><label>1</label>
<addr-line>Predictive Science Inc., San Diego, California, United States of America</addr-line>
</aff>
<aff id="aff002"><label>2</label>
<addr-line>Armed Forces Health Surveillance Center, Silver Spring, Maryland, United States of America</addr-line>
</aff>
<aff id="aff003"><label>3</label>
<addr-line>Biomedical Advanced Research and Development Authority (BARDA), Assistant Secretary for Preparedness and Response (ASPR), Department of Health and Human Services (HHS), Washington, D.C., United States of America</addr-line>
</aff>
<aff id="aff004"><label>4</label>
<addr-line>Leidos, McLean, Virginia, United States of America</addr-line>
</aff>
<aff id="aff005"><label>5</label>
<addr-line>MRC Centre for Outbreak Analysis and Modelling, Imperial College London, United Kingdom</addr-line>
</aff>
<contrib-group><contrib contrib-type="editor"><name><surname>Salathé</surname>
<given-names>Marcel</given-names>
</name>
<role>Editor</role>
<xref ref-type="aff" rid="edit1"></xref>
</contrib>
</contrib-group>
<aff id="edit1"><addr-line>Pennsylvania State University, UNITED STATES</addr-line>
</aff>
<author-notes><fn fn-type="COI-statement" id="coi001"><p>PR, MBN, and JAL are paid employees of Predictive Science Inc. David P. Bacon is a paid employee of Leidos. The authors have declared that no other competing interests exist.</p>
</fn>
<fn fn-type="con" id="contrib001"><p>Conceived and designed the experiments: PR SR DG. Performed the experiments: PR SR MBN. Analyzed the data: PR SR MBN DPB. Contributed reagents/materials/analysis tools: AAC JLS. Wrote the paper: PR SR MBN JAL.</p>
</fn>
<corresp id="cor001">* E-mail: <email>pete@predsci.com</email>
</corresp>
</author-notes>
<pub-date pub-type="collection"><month>9</month>
<year>2015</year>
</pub-date>
<pub-date pub-type="epub"><day>24</day>
<month>9</month>
<year>2015</year>
</pub-date>
<volume>11</volume>
<issue>9</issue>
<elocation-id>e1004392</elocation-id>
<history><date date-type="received"><day>20</day>
<month>1</month>
<year>2015</year>
</date>
<date date-type="accepted"><day>9</day>
<month>6</month>
<year>2015</year>
</date>
</history>
<permissions><license xlink:href="https://creativecommons.org/publicdomain/zero/1.0/"><license-p>This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.</license-p>
</license>
</permissions>
<self-uri content-type="pdf" xlink:type="simple" xlink:href="pcbi.1004392.pdf"></self-uri>
<abstract><p>The potential rapid availability of large-scale clinical episode data during the next influenza pandemic suggests an opportunity for increasing the speed with which novel respiratory pathogens can be characterized. Key intervention decisions will be determined by both the transmissibility of the novel strain (measured by the basic reproductive number <italic>R</italic>
<sub>0</sub>
) and its individual-level severity. The 2009 pandemic illustrated that estimating individual-level severity, as described by the proportion <italic>p</italic>
<sub><italic>C</italic>
</sub>
of infections that result in clinical cases, can remain uncertain for a prolonged period of time. Here, we use 50 distinct US military populations during 2009 as a retrospective cohort to test the hypothesis that real-time encounter data combined with disease dynamic models can be used to bridge this uncertainty gap. Effectively, we estimated the total number of infections in multiple early-affected communities using the model and divided that number by the known number of clinical cases. Joint estimates of severity and transmissibility clustered within a relatively small region of parameter space, with 40 of the 50 populations bounded by: <italic>p</italic>
<sub><italic>C</italic>
</sub>
, 0.0133–0.150 and <italic>R</italic>
<sub>0</sub>
, 1.09–2.16. These fits were obtained despite widely varying incidence profiles: some with spring waves, some with fall waves and some with both. To illustrate the benefit of specific pairing of rapidly available data and infectious disease models, we simulated a future moderate pandemic strain with <italic>p</italic>
<sub><italic>C</italic>
</sub>
approximately ×10 that of 2009; the results demonstrating that even before the peak had passed in the first affected population, <italic>R</italic>
<sub>0</sub>
and <italic>p</italic>
<sub><italic>C</italic>
</sub>
could be well estimated. This study provides a clear reference in this two-dimensional space against which future novel respiratory pathogens can be rapidly assessed and compared with previous pandemics.</p>
</abstract>
<abstract abstract-type="summary"><title>Author Summary</title>
<p>The ever-increasing availability of timely, large-scale clinical episode data can, in principle, dramatically shorten the time required to characterize the transmission and severity of novel respiratory pathogens, which, in turn, can be used to inform key intervention decisions. We investigated 50 distinct military populations during the 2009 influenza pandemic to test the hypothesis that real-time encounter data combined with disease dynamic models can be used to jointly determine the transmissibility of the novel strain (described by the basic reproductive number <italic>R</italic>
<sub>0</sub>
) and its individual-level severity (described by the proportion <italic>p</italic>
<sub><italic>C</italic>
</sub>
of infections that result in clinical cases). To illustrate the use of such a procedure, we simulated a future moderate pandemic strain with <italic>p</italic>
<sub><italic>C</italic>
</sub>
approximately ×10 that of 2009, which demonstrated that even before the peak had passed in the first affected population, <italic>R</italic>
<sub>0</sub>
and <italic>p</italic>
<sub><italic>C</italic>
</sub>
could be well estimated. These results provide a clear reference in this two-dimensional space against which future novel respiratory pathogens can be rapidly compared, establishing a firm baseline for describing the relative severity of future emerging respiratory pathogens.</p>
</abstract>
<funding-group><funding-statement>Work performed by PR, MBN, JAL, DPB, and SR was supported by the Defense Threat Reduction Agency (DTRA), contract number: HDTRA1-14-C-0031. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</funding-statement>
</funding-group>
<counts><fig-count count="3"></fig-count>
<table-count count="0"></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>All data used in this study are available directly from <ext-link ext-link-type="uri" xlink:href="http://www.predsci.com/~pete/research/plos-comp-biol-flu-severity/">http://www.predsci.com/~pete/research/plos-comp-biol-flu-severity/</ext-link>
</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
<notes><title>Data Availability</title>
<p>All data used in this study are available directly from <ext-link ext-link-type="uri" xlink:href="http://www.predsci.com/~pete/research/plos-comp-biol-flu-severity/">http://www.predsci.com/~pete/research/plos-comp-biol-flu-severity/</ext-link>
</p>
</notes>
</front>
</pmc>
<affiliations><list><country><li>Royaume-Uni</li>
<li>États-Unis</li>
</country>
<region><li>Californie</li>
<li>Maryland</li>
<li>Virginie</li>
</region>
</list>
<tree><country name="États-Unis"><region name="Californie"><name sortKey="Riley, Pete" sort="Riley, Pete" uniqKey="Riley P" first="Pete" last="Riley">Pete Riley</name>
</region>
<name sortKey="Bacon, David P" sort="Bacon, David P" uniqKey="Bacon D" first="David P." last="Bacon">David P. Bacon</name>
<name sortKey="Ben Nun, Michal" sort="Ben Nun, Michal" uniqKey="Ben Nun M" first="Michal" last="Ben-Nun">Michal Ben-Nun</name>
<name sortKey="Cost, Angelia A" sort="Cost, Angelia A" uniqKey="Cost A" first="Angelia A." last="Cost">Angelia A. Cost</name>
<name sortKey="George, Dylan" sort="George, Dylan" uniqKey="George D" first="Dylan" last="George">Dylan George</name>
<name sortKey="Linker, Jon A" sort="Linker, Jon A" uniqKey="Linker J" first="Jon A." last="Linker">Jon A. Linker</name>
<name sortKey="Riley, Steven" sort="Riley, Steven" uniqKey="Riley S" first="Steven" last="Riley">Steven Riley</name>
<name sortKey="Sanchez, Jose L" sort="Sanchez, Jose L" uniqKey="Sanchez J" first="Jose L." last="Sanchez">Jose L. Sanchez</name>
</country>
<country name="Royaume-Uni"><noRegion><name sortKey="Riley, Steven" sort="Riley, Steven" uniqKey="Riley S" first="Steven" last="Riley">Steven Riley</name>
</noRegion>
</country>
</tree>
</affiliations>
</record>
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