Probabilistic differential diagnosis of Middle East respiratory syndrome (MERS) using the time from immigration to illness onset among imported cases
Identifieur interne : 001B16 ( Main/Exploration ); précédent : 001B15; suivant : 001B17Probabilistic differential diagnosis of Middle East respiratory syndrome (MERS) using the time from immigration to illness onset among imported cases
Auteurs : Keisuke Ejima [Japon] ; Kazuyuki Aihara [Japon] ; Hiroshi Nishiura [Japon]Source :
- Journal of Theoretical Biology [ 0022-5193 ] ; 2014.
Descripteurs français
- KwdFr :
- Coronavirus, Diagnostic différentiel, Facteurs temps, Grippe humaine (épidémiologie), Humains, Infections de l'appareil respiratoire (diagnostic), Infections de l'appareil respiratoire (épidémiologie), Infections à coronavirus (diagnostic), Infections à coronavirus (épidémiologie), Modèles biologiques, Moyen Orient, Probabilité, Émigration et immigration ().
- MESH :
- diagnostic : Infections de l'appareil respiratoire, Infections à coronavirus.
- épidémiologie : Grippe humaine, Infections de l'appareil respiratoire, Infections à coronavirus.
- Coronavirus, Diagnostic différentiel, Facteurs temps, Humains, Modèles biologiques, Moyen Orient, Probabilité, Émigration et immigration.
English descriptors
- KwdEn :
- Coronavirus, Coronavirus Infections (diagnosis), Coronavirus Infections (epidemiology), Diagnosis, Differential, Emigration and Immigration (statistics & numerical data), Humans, Influenza, Human (epidemiology), Middle East, Models, Biological, Probability, Respiratory Tract Infections (diagnosis), Respiratory Tract Infections (epidemiology), Time Factors.
- MESH :
- geographic : Middle East.
- diagnosis : Coronavirus Infections, Respiratory Tract Infections.
- epidemiology : Coronavirus Infections, Influenza, Human, Respiratory Tract Infections.
- statistics & numerical data : Emigration and Immigration.
- Coronavirus, Diagnosis, Differential, Humans, Models, Biological, Probability, Time Factors.
Abstract
Middle East respiratory syndrome (MERS) has spread worldwide since 2012. As the clinical symptoms of MERS tend to be non-specific, the incubation period has been shown to complement differential diagnosis, especially to rule out influenza. However, because an infection event is seldom directly observable, the present study aims to construct a diagnostic model that predicts the probability of MERS diagnosis given the time from immigration to illness onset among imported cases which are suspected of MERS. Addressing censoring by considering the transmission dynamics in an exporting country, we demonstrate that the illness onset within 2 days from immigration is suggestive of influenza. Two exceptions to suspect MERS even for those with illness onset within 2 days since immigration are (i) when we observe substantial community transmissions of MERS and (ii) when the cases are at high risk of MERS (e.g. cases with close contact in hospital or household). It is vital to collect the information of the incubation period upon emergence of a novel infectious disease, and moreover, in our model, the fundamental transmission dynamics including the initial growth rate has to be explored to differentiate the disease diagnoses with non-specific symptoms.
Url:
DOI: 10.1016/j.jtbi.2013.12.024
PubMed: 24406808
PubMed Central: 7094128
Affiliations:
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Le document en format XML
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Bunkyo-ku, Tokyo 113-0033</wicri:regionArea><placeName><settlement type="city">Tokyo</settlement><region type="région">Région de Kantō</region></placeName><orgName type="university">Université de Tokyo</orgName><placeName><settlement type="city">Tokyo</settlement><region type="province">Région de Kantō</region></placeName></affiliation><affiliation wicri:level="1"><nlm:aff id="aff0020">PRESTO, Japan Science and Technology Agency, Saitama, Japan</nlm:aff><country xml:lang="fr">Japon</country><wicri:regionArea>PRESTO, Japan Science and Technology Agency, Saitama</wicri:regionArea><wicri:noRegion>Saitama</wicri:noRegion></affiliation></author></analytic><series><title level="j">Journal of Theoretical Biology</title><idno type="ISSN">0022-5193</idno><idno type="eISSN">1095-8541</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Coronavirus</term><term>Coronavirus Infections (diagnosis)</term><term>Coronavirus Infections (epidemiology)</term><term>Diagnosis, Differential</term><term>Emigration and Immigration (statistics & numerical data)</term><term>Humans</term><term>Influenza, Human (epidemiology)</term><term>Middle East</term><term>Models, Biological</term><term>Probability</term><term>Respiratory Tract Infections (diagnosis)</term><term>Respiratory Tract Infections (epidemiology)</term><term>Time Factors</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Coronavirus</term><term>Diagnostic différentiel</term><term>Facteurs temps</term><term>Grippe humaine (épidémiologie)</term><term>Humains</term><term>Infections de l'appareil respiratoire (diagnostic)</term><term>Infections de l'appareil respiratoire (épidémiologie)</term><term>Infections à coronavirus (diagnostic)</term><term>Infections à coronavirus (épidémiologie)</term><term>Modèles biologiques</term><term>Moyen Orient</term><term>Probabilité</term><term>Émigration et immigration ()</term></keywords><keywords scheme="MESH" type="geographic" xml:lang="en"><term>Middle East</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Coronavirus Infections</term><term>Respiratory Tract Infections</term></keywords><keywords scheme="MESH" qualifier="diagnostic" xml:lang="fr"><term>Infections de l'appareil respiratoire</term><term>Infections à coronavirus</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Coronavirus Infections</term><term>Influenza, Human</term><term>Respiratory Tract Infections</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Emigration and Immigration</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Grippe humaine</term><term>Infections de l'appareil respiratoire</term><term>Infections à coronavirus</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Coronavirus</term><term>Diagnosis, Differential</term><term>Humans</term><term>Models, Biological</term><term>Probability</term><term>Time Factors</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Coronavirus</term><term>Diagnostic différentiel</term><term>Facteurs temps</term><term>Humains</term><term>Modèles biologiques</term><term>Moyen Orient</term><term>Probabilité</term><term>Émigration et immigration</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Middle East respiratory syndrome (MERS) has spread worldwide since 2012. As the clinical symptoms of MERS tend to be non-specific, the incubation period has been shown to complement differential diagnosis, especially to rule out influenza. However, because an infection event is seldom directly observable, the present study aims to construct a diagnostic model that predicts the probability of MERS diagnosis given the time from immigration to illness onset among imported cases which are suspected of MERS. Addressing censoring by considering the transmission dynamics in an exporting country, we demonstrate that the illness onset within 2 days from immigration is suggestive of influenza. Two exceptions to suspect MERS even for those with illness onset within 2 days since immigration are (i) when we observe substantial community transmissions of MERS and (ii) when the cases are at high risk of MERS (e.g. cases with close contact in hospital or household). It is vital to collect the information of the incubation period upon emergence of a novel infectious disease, and moreover, in our model, the fundamental transmission dynamics including the initial growth rate has to be explored to differentiate the disease diagnoses with non-specific symptoms.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Assiri, A" uniqKey="Assiri A">A. Assiri</name></author><author><name sortKey="Al Tawfiq, J A" uniqKey="Al Tawfiq J">J.A. Al-Tawfiq</name></author><author><name sortKey="Al Rabeeah, A A" uniqKey="Al Rabeeah A">A.A. Al-Rabeeah</name></author><author><name sortKey="Al Rabiah, F A" uniqKey="Al Rabiah F">F.A. Al-Rabiah</name></author><author><name sortKey="Al Hajjar, S" uniqKey="Al Hajjar S">S. Al-Hajjar</name></author><author><name sortKey="Al Barrak, A" uniqKey="Al Barrak A">A. Al-Barrak</name></author><author><name sortKey="Flemban, H" uniqKey="Flemban H">H. 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Lipsitch</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>Japon</li></country><region><li>Région de Kantō</li></region><settlement><li>Tokyo</li></settlement><orgName><li>Université de Tokyo</li></orgName></list><tree><country name="Japon"><region name="Région de Kantō"><name sortKey="Ejima, Keisuke" sort="Ejima, Keisuke" uniqKey="Ejima K" first="Keisuke" last="Ejima">Keisuke Ejima</name></region><name sortKey="Aihara, Kazuyuki" sort="Aihara, Kazuyuki" uniqKey="Aihara K" first="Kazuyuki" last="Aihara">Kazuyuki Aihara</name><name sortKey="Aihara, Kazuyuki" sort="Aihara, Kazuyuki" uniqKey="Aihara K" first="Kazuyuki" last="Aihara">Kazuyuki Aihara</name><name sortKey="Ejima, Keisuke" sort="Ejima, Keisuke" uniqKey="Ejima K" first="Keisuke" last="Ejima">Keisuke Ejima</name><name sortKey="Nishiura, Hiroshi" sort="Nishiura, Hiroshi" uniqKey="Nishiura H" first="Hiroshi" last="Nishiura">Hiroshi Nishiura</name><name sortKey="Nishiura, Hiroshi" sort="Nishiura, Hiroshi" uniqKey="Nishiura H" first="Hiroshi" last="Nishiura">Hiroshi Nishiura</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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