A new framework and software to estimate time-varying reproduction numbers during epidemics.
Identifieur interne : 001126 ( PubMed/Curation ); précédent : 001125; suivant : 001127A new framework and software to estimate time-varying reproduction numbers during epidemics.
Auteurs : Anne Cori ; Neil M. Ferguson ; Christophe Fraser ; Simon CauchemezSource :
- American journal of epidemiology [ 1476-6256 ] ; 2013.
Descripteurs français
- KwdFr :
- MESH :
- épidémiologie : Maladies virales.
- Facteurs temps, Humains, Incidence, Logiciel, Modèles statistiques, Épidémies.
English descriptors
- KwdEn :
- MESH :
- epidemiology : Virus Diseases.
- statistics & numerical data : Epidemics.
- transmission : Virus Diseases.
- Humans, Incidence, Models, Statistical, Software, Time Factors.
Abstract
The quantification of transmissibility during epidemics is essential to designing and adjusting public health responses. Transmissibility can be measured by the reproduction number R, the average number of secondary cases caused by an infected individual. Several methods have been proposed to estimate R over the course of an epidemic; however, they are usually difficult to implement for people without a strong background in statistical modeling. Here, we present a ready-to-use tool for estimating R from incidence time series, which is implemented in popular software including Microsoft Excel (Microsoft Corporation, Redmond, Washington). This tool produces novel, statistically robust analytical estimates of R and incorporates uncertainty in the distribution of the serial interval (the time between the onset of symptoms in a primary case and the onset of symptoms in secondary cases). We applied the method to 5 historical outbreaks; the resulting estimates of R are consistent with those presented in the literature. This tool should help epidemiologists quantify temporal changes in the transmission intensity of future epidemics by using surveillance data.
DOI: 10.1093/aje/kwt133
PubMed: 24043437
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Ferguson</name></author><author><name sortKey="Fraser, Christophe" sort="Fraser, Christophe" uniqKey="Fraser C" first="Christophe" last="Fraser">Christophe Fraser</name></author><author><name sortKey="Cauchemez, Simon" sort="Cauchemez, Simon" uniqKey="Cauchemez S" first="Simon" last="Cauchemez">Simon Cauchemez</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2013">2013</date><idno type="RBID">pubmed:24043437</idno><idno type="pmid">24043437</idno><idno type="doi">10.1093/aje/kwt133</idno><idno type="wicri:Area/PubMed/Corpus">001126</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">001126</idno><idno type="wicri:Area/PubMed/Curation">001126</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">001126</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">A new framework and software to estimate time-varying reproduction numbers during epidemics.</title><author><name sortKey="Cori, Anne" sort="Cori, Anne" uniqKey="Cori A" first="Anne" last="Cori">Anne Cori</name></author><author><name sortKey="Ferguson, Neil M" sort="Ferguson, Neil M" uniqKey="Ferguson N" first="Neil M" last="Ferguson">Neil M. Ferguson</name></author><author><name sortKey="Fraser, Christophe" sort="Fraser, Christophe" uniqKey="Fraser C" first="Christophe" last="Fraser">Christophe Fraser</name></author><author><name sortKey="Cauchemez, Simon" sort="Cauchemez, Simon" uniqKey="Cauchemez S" first="Simon" last="Cauchemez">Simon Cauchemez</name></author></analytic><series><title level="j">American journal of epidemiology</title><idno type="eISSN">1476-6256</idno><imprint><date when="2013" type="published">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Epidemics (statistics & numerical data)</term><term>Humans</term><term>Incidence</term><term>Models, Statistical</term><term>Software</term><term>Time Factors</term><term>Virus Diseases (epidemiology)</term><term>Virus Diseases (transmission)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Facteurs temps</term><term>Humains</term><term>Incidence</term><term>Logiciel</term><term>Maladies virales (transmission)</term><term>Maladies virales (épidémiologie)</term><term>Modèles statistiques</term><term>Épidémies ()</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Virus Diseases</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Epidemics</term></keywords><keywords scheme="MESH" qualifier="transmission" xml:lang="en"><term>Virus Diseases</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Maladies virales</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Humans</term><term>Incidence</term><term>Models, Statistical</term><term>Software</term><term>Time Factors</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Facteurs temps</term><term>Humains</term><term>Incidence</term><term>Logiciel</term><term>Modèles statistiques</term><term>Épidémies</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The quantification of transmissibility during epidemics is essential to designing and adjusting public health responses. Transmissibility can be measured by the reproduction number R, the average number of secondary cases caused by an infected individual. Several methods have been proposed to estimate R over the course of an epidemic; however, they are usually difficult to implement for people without a strong background in statistical modeling. Here, we present a ready-to-use tool for estimating R from incidence time series, which is implemented in popular software including Microsoft Excel (Microsoft Corporation, Redmond, Washington). This tool produces novel, statistically robust analytical estimates of R and incorporates uncertainty in the distribution of the serial interval (the time between the onset of symptoms in a primary case and the onset of symptoms in secondary cases). We applied the method to 5 historical outbreaks; the resulting estimates of R are consistent with those presented in the literature. This tool should help epidemiologists quantify temporal changes in the transmission intensity of future epidemics by using surveillance data.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">24043437</PMID><DateCompleted><Year>2013</Year><Month>12</Month><Day>23</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1476-6256</ISSN><JournalIssue CitedMedium="Internet"><Volume>178</Volume><Issue>9</Issue><PubDate><Year>2013</Year><Month>Nov</Month><Day>01</Day></PubDate></JournalIssue><Title>American journal of epidemiology</Title><ISOAbbreviation>Am. J. 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This tool produces novel, statistically robust analytical estimates of R and incorporates uncertainty in the distribution of the serial interval (the time between the onset of symptoms in a primary case and the onset of symptoms in secondary cases). We applied the method to 5 historical outbreaks; the resulting estimates of R are consistent with those presented in the literature. 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