Routes of transmission of influenza A H1N1, SARS CoV, and norovirus in air cabin: Comparative analyses.
Identifieur interne : 000C45 ( Main/Exploration ); précédent : 000C44; suivant : 000C46Routes of transmission of influenza A H1N1, SARS CoV, and norovirus in air cabin: Comparative analyses.
Auteurs : H. Lei [République populaire de Chine] ; Y. Li [République populaire de Chine] ; S. Xiao [République populaire de Chine] ; C-H Lin [États-Unis] ; S L Norris [États-Unis] ; D. Wei [République populaire de Chine] ; Z. Hu [République populaire de Chine] ; S. Ji [République populaire de Chine]Source :
- Indoor air [ 1600-0668 ] ; 2018.
Descripteurs français
- KwdFr :
- Femelle, Flambées de maladies, Grippe humaine (transmission), Humains, Infections à Caliciviridae (transmission), Mâle, Norovirus, Pollution de l'air ambiant intérieur (analyse), Risque, Simulation numérique, Sous-type H1N1 du virus de la grippe A, Syndrome respiratoire aigu sévère (transmission), Virus du SRAS, Véhicules de transport aérien.
- MESH :
- analyse : Pollution de l'air ambiant intérieur.
- transmission : Femelle, Flambées de maladies, Grippe humaine, Humains, Infections à Caliciviridae, Mâle, Norovirus, Risque, Simulation numérique, Sous-type H1N1 du virus de la grippe A, Syndrome respiratoire aigu sévère, Virus du SRAS, Véhicules de transport aérien.
English descriptors
- KwdEn :
- MESH :
- analysis : Air Pollution, Indoor.
- transmission : Caliciviridae Infections, Influenza, Human, Severe Acute Respiratory Syndrome.
- Aircraft, Computer Simulation, Disease Outbreaks, Female, Humans, Influenza A Virus, H1N1 Subtype, Male, Norovirus, Risk, SARS Virus.
Abstract
Identifying the exact transmission route(s) of infectious diseases in indoor environments is a crucial step in developing effective intervention strategies. In this study, we proposed a comparative analysis approach and built a model to simulate outbreaks of 3 different in-flight infections in a similar cabin environment, that is, influenza A H1N1, severe acute respiratory syndrome (SARS) coronavirus (CoV), and norovirus. The simulation results seemed to suggest that the close contact route was probably the most significant route (contributes 70%, 95% confidence interval [CI]: 67%-72%) in the in-flight transmission of influenza A H1N1 transmission; as a result, passengers within 2 rows of the index case had a significantly higher infection risk than others in the outbreak (relative risk [RR]: 13.4, 95% CI: 1.5-121.2, P = .019). For SARS CoV, the airborne, close contact, and fomite routes contributed 21% (95% CI: 19%-23%), 29% (95% CI: 27%-31%), and 50% (95% CI: 48%-53%), respectively. For norovirus, the simulation results suggested that the fomite route played the dominant role (contributes 85%, 95% CI: 83%-87%) in most cases; as a result, passengers in aisle seats had a significantly higher infection risk than others (RR: 9.5, 95% CI: 1.2-77.4, P = .022). This work highlighted a method for using observed outbreak data to analyze the roles of different infection transmission routes.
DOI: 10.1111/ina.12445
PubMed: 29244221
Affiliations:
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Lei</name><affiliation wicri:level="1"><nlm:affiliation>Department of Mechanical Engineering, The University of Hong Kong, Pokfulam, Hong Kong, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Mechanical Engineering, The University of Hong Kong, Pokfulam, Hong Kong</wicri:regionArea><wicri:noRegion>Hong Kong</wicri:noRegion></affiliation></author><author><name sortKey="Li, Y" sort="Li, Y" uniqKey="Li Y" first="Y" last="Li">Y. Li</name><affiliation wicri:level="1"><nlm:affiliation>Department of Mechanical Engineering, The University of Hong Kong, Pokfulam, Hong Kong, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Mechanical Engineering, The University of Hong Kong, Pokfulam, Hong Kong</wicri:regionArea><wicri:noRegion>Hong Kong</wicri:noRegion></affiliation></author><author><name sortKey="Xiao, S" sort="Xiao, S" uniqKey="Xiao S" first="S" last="Xiao">S. 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Ji</name><affiliation wicri:level="3"><nlm:affiliation>Beijing Aeronautical Science & Technology Research Institute of COMAC, Beijing, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Beijing Aeronautical Science & Technology Research Institute of COMAC, Beijing</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author></analytic><series><title level="j">Indoor air</title><idno type="eISSN">1600-0668</idno><imprint><date when="2018" type="published">2018</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Air Pollution, Indoor (analysis)</term><term>Aircraft</term><term>Caliciviridae Infections (transmission)</term><term>Computer Simulation</term><term>Disease Outbreaks</term><term>Female</term><term>Humans</term><term>Influenza A Virus, H1N1 Subtype</term><term>Influenza, Human (transmission)</term><term>Male</term><term>Norovirus</term><term>Risk</term><term>SARS Virus</term><term>Severe Acute Respiratory Syndrome (transmission)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Femelle</term><term>Flambées de maladies</term><term>Grippe humaine (transmission)</term><term>Humains</term><term>Infections à Caliciviridae (transmission)</term><term>Mâle</term><term>Norovirus</term><term>Pollution de l'air ambiant intérieur (analyse)</term><term>Risque</term><term>Simulation numérique</term><term>Sous-type H1N1 du virus de la grippe A</term><term>Syndrome respiratoire aigu sévère (transmission)</term><term>Virus du SRAS</term><term>Véhicules de transport aérien</term></keywords><keywords scheme="MESH" qualifier="analyse" xml:lang="fr"><term>Pollution de l'air ambiant intérieur</term></keywords><keywords scheme="MESH" qualifier="analysis" xml:lang="en"><term>Air Pollution, Indoor</term></keywords><keywords scheme="MESH" qualifier="transmission" xml:lang="en"><term>Caliciviridae Infections</term><term>Influenza, Human</term><term>Severe Acute Respiratory Syndrome</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Aircraft</term><term>Computer Simulation</term><term>Disease Outbreaks</term><term>Female</term><term>Humans</term><term>Influenza A Virus, H1N1 Subtype</term><term>Male</term><term>Norovirus</term><term>Risk</term><term>SARS Virus</term></keywords><keywords scheme="MESH" qualifier="transmission" xml:lang="fr"><term>Femelle</term><term>Flambées de maladies</term><term>Grippe humaine</term><term>Humains</term><term>Infections à Caliciviridae</term><term>Mâle</term><term>Norovirus</term><term>Risque</term><term>Simulation numérique</term><term>Sous-type H1N1 du virus de la grippe A</term><term>Syndrome respiratoire aigu sévère</term><term>Virus du SRAS</term><term>Véhicules de transport aérien</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Identifying the exact transmission route(s) of infectious diseases in indoor environments is a crucial step in developing effective intervention strategies. In this study, we proposed a comparative analysis approach and built a model to simulate outbreaks of 3 different in-flight infections in a similar cabin environment, that is, influenza A H1N1, severe acute respiratory syndrome (SARS) coronavirus (CoV), and norovirus. The simulation results seemed to suggest that the close contact route was probably the most significant route (contributes 70%, 95% confidence interval [CI]: 67%-72%) in the in-flight transmission of influenza A H1N1 transmission; as a result, passengers within 2 rows of the index case had a significantly higher infection risk than others in the outbreak (relative risk [RR]: 13.4, 95% CI: 1.5-121.2, P = .019). For SARS CoV, the airborne, close contact, and fomite routes contributed 21% (95% CI: 19%-23%), 29% (95% CI: 27%-31%), and 50% (95% CI: 48%-53%), respectively. For norovirus, the simulation results suggested that the fomite route played the dominant role (contributes 85%, 95% CI: 83%-87%) in most cases; as a result, passengers in aisle seats had a significantly higher infection risk than others (RR: 9.5, 95% CI: 1.2-77.4, P = .022). This work highlighted a method for using observed outbreak data to analyze the roles of different infection transmission routes.</div></front></TEI><affiliations><list><country><li>République populaire de Chine</li><li>États-Unis</li></country><region><li>Washington (État)</li></region><settlement><li>Pékin</li></settlement></list><tree><country name="République populaire de Chine"><noRegion><name sortKey="Lei, H" sort="Lei, H" uniqKey="Lei H" first="H" last="Lei">H. Lei</name></noRegion><name sortKey="Hu, Z" sort="Hu, Z" uniqKey="Hu Z" first="Z" last="Hu">Z. Hu</name><name sortKey="Ji, S" sort="Ji, S" uniqKey="Ji S" first="S" last="Ji">S. Ji</name><name sortKey="Li, Y" sort="Li, Y" uniqKey="Li Y" first="Y" last="Li">Y. Li</name><name sortKey="Wei, D" sort="Wei, D" uniqKey="Wei D" first="D" last="Wei">D. Wei</name><name sortKey="Xiao, S" sort="Xiao, S" uniqKey="Xiao S" first="S" last="Xiao">S. Xiao</name></country><country name="États-Unis"><region name="Washington (État)"><name sortKey="Lin, C H" sort="Lin, C H" uniqKey="Lin C" first="C-H" last="Lin">C-H Lin</name></region><name sortKey="Norris, S L" sort="Norris, S L" uniqKey="Norris S" first="S L" last="Norris">S L Norris</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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