Agent-Based Modeling for Super-Spreading Events: A Case Study of MERS-CoV Transmission Dynamics in the Republic of Korea
Identifieur interne : 000A71 ( Main/Exploration ); précédent : 000A70; suivant : 000A72Agent-Based Modeling for Super-Spreading Events: A Case Study of MERS-CoV Transmission Dynamics in the Republic of Korea
Auteurs : Yunhwan Kim ; Hohyung Ryu ; Sunmi Lee [Corée du Sud]Source :
- International Journal of Environmental Research and Public Health [ 1661-7827 ] ; 2018.
Descripteurs français
- KwdFr :
- Coronavirus du syndrome respiratoire du Moyen-Orient (physiologie), Incidence, Infections à coronavirus (transmission), Infections à coronavirus (épidémiologie), Modèles théoriques, Méthode de Monte-Carlo, Nombre de reproduction de base, Processus stochastiques, République de Corée (épidémiologie), Épidémies.
- MESH :
English descriptors
- KwdEn :
- MESH :
- geographic , epidemiology : Republic of Korea.
- epidemiology : Coronavirus Infections.
- physiology : Middle East Respiratory Syndrome Coronavirus.
- transmission : Coronavirus Infections.
- Basic Reproduction Number, Epidemics, Incidence, Models, Theoretical, Monte Carlo Method, Stochastic Processes.
Abstract
Super-spreading events have been observed in the transmission dynamics of many infectious diseases. The 2015 MERS-CoV outbreak in the Republic of Korea has also shown super-spreading events with a significantly high level of heterogeneity in generating secondary cases. It becomes critical to understand the mechanism for this high level of heterogeneity to develop effective intervention strategies and preventive plans for future emerging infectious diseases. In this regard, agent-based modeling is a useful tool for incorporating individual heterogeneity into the epidemic model. In the present work, a stochastic agent-based framework is developed in order to understand the underlying mechanism of heterogeneity. Clinical (i.e., an infectivity level) and social or environmental (i.e., a contact level) heterogeneity are modeled. These factors are incorporated in the transmission rate functions under assumptions that super-spreaders have stronger transmission and/or higher links. Our agent-based model has employed real MERS-CoV epidemic features based on the 2015 MERS-CoV epidemiological data. Monte Carlo simulations are carried out under various epidemic scenarios. Our findings highlight the roles of super-spreaders in a high level of heterogeneity, underscoring that the number of contacts combined with a higher level of infectivity are the most critical factors for substantial heterogeneity in generating secondary cases of the 2015 MERS-CoV transmission.
Url:
DOI: 10.3390/ijerph15112369
PubMed: 30373151
PubMed Central: 6265857
Affiliations:
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Korea;<email>kyinr108@gmail.com</email></nlm:aff></affiliation></author><author><name sortKey="Lee, Sunmi" sort="Lee, Sunmi" uniqKey="Lee S" first="Sunmi" last="Lee">Sunmi Lee</name><affiliation><nlm:aff id="af2-ijerph-15-02369">Department of Applied Mathematics, Kyung Hee University, Yongin 446-701, Korea;<email>kyinr108@gmail.com</email></nlm:aff></affiliation><affiliation wicri:level="1"><nlm:aff id="af3-ijerph-15-02369">Institute of Natural Sciences, Kyung Hee University, Yongin 446-701, Korea</nlm:aff><country xml:lang="fr">Corée du Sud</country><wicri:regionArea>Institute of Natural Sciences, Kyung Hee University, Yongin 446-701</wicri:regionArea><wicri:noRegion>Yongin 446-701</wicri:noRegion></affiliation></author></analytic><series><title level="j">International Journal of Environmental Research and Public Health</title><idno type="ISSN">1661-7827</idno><idno type="eISSN">1660-4601</idno><imprint><date when="2018">2018</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Basic Reproduction Number</term><term>Coronavirus Infections (epidemiology)</term><term>Coronavirus Infections (transmission)</term><term>Epidemics</term><term>Incidence</term><term>Middle East Respiratory Syndrome Coronavirus (physiology)</term><term>Models, Theoretical</term><term>Monte Carlo Method</term><term>Republic of Korea (epidemiology)</term><term>Stochastic Processes</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Coronavirus du syndrome respiratoire du Moyen-Orient (physiologie)</term><term>Incidence</term><term>Infections à coronavirus (transmission)</term><term>Infections à coronavirus (épidémiologie)</term><term>Modèles théoriques</term><term>Méthode de Monte-Carlo</term><term>Nombre de reproduction de base</term><term>Processus stochastiques</term><term>République de Corée (épidémiologie)</term><term>Épidémies</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Republic of Korea</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Coronavirus Infections</term></keywords><keywords scheme="MESH" qualifier="physiologie" xml:lang="fr"><term>Coronavirus du syndrome respiratoire du Moyen-Orient</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Middle East Respiratory Syndrome Coronavirus</term></keywords><keywords scheme="MESH" qualifier="transmission" xml:lang="en"><term>Coronavirus Infections</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Infections à coronavirus</term><term>République de Corée</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Basic Reproduction Number</term><term>Epidemics</term><term>Incidence</term><term>Models, Theoretical</term><term>Monte Carlo Method</term><term>Stochastic Processes</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Incidence</term><term>Modèles théoriques</term><term>Méthode de Monte-Carlo</term><term>Nombre de reproduction de base</term><term>Processus stochastiques</term><term>Épidémies</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Super-spreading events have been observed in the transmission dynamics of many infectious diseases. The 2015 MERS-CoV outbreak in the Republic of Korea has also shown super-spreading events with a significantly high level of heterogeneity in generating secondary cases. It becomes critical to understand the mechanism for this high level of heterogeneity to develop effective intervention strategies and preventive plans for future emerging infectious diseases. In this regard, agent-based modeling is a useful tool for incorporating individual heterogeneity into the epidemic model. In the present work, a stochastic agent-based framework is developed in order to understand the underlying mechanism of heterogeneity. Clinical (i.e., an infectivity level) and social or environmental (i.e., a contact level) heterogeneity are modeled. These factors are incorporated in the transmission rate functions under assumptions that super-spreaders have stronger transmission and/or higher links. Our agent-based model has employed real MERS-CoV epidemic features based on the 2015 MERS-CoV epidemiological data. Monte Carlo simulations are carried out under various epidemic scenarios. Our findings highlight the roles of super-spreaders in a high level of heterogeneity, underscoring that the number of contacts combined with a higher level of infectivity are the most critical factors for substantial heterogeneity in generating secondary cases of the 2015 MERS-CoV transmission.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Chowell, G" uniqKey="Chowell G">G. Chowell</name></author><author><name sortKey="Abdirizak, F" uniqKey="Abdirizak F">F. Abdirizak</name></author><author><name sortKey="Lee, S" uniqKey="Lee S">S. Lee</name></author><author><name sortKey="Lee, J" uniqKey="Lee J">J. Lee</name></author><author><name sortKey="Jung, E" uniqKey="Jung E">E. 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Quinnell</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>Corée du Sud</li></country></list><tree><noCountry><name sortKey="Kim, Yunhwan" sort="Kim, Yunhwan" uniqKey="Kim Y" first="Yunhwan" last="Kim">Yunhwan Kim</name><name sortKey="Ryu, Hohyung" sort="Ryu, Hohyung" uniqKey="Ryu H" first="Hohyung" last="Ryu">Hohyung Ryu</name></noCountry><country name="Corée du Sud"><noRegion><name sortKey="Lee, Sunmi" sort="Lee, Sunmi" uniqKey="Lee S" first="Sunmi" last="Lee">Sunmi Lee</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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