Investigation of nonlinear epidemiological models for analyzing and controlling the MERS outbreak in Korea
Identifieur interne : 000D30 ( Main/Exploration ); précédent : 000D29; suivant : 000D31Investigation of nonlinear epidemiological models for analyzing and controlling the MERS outbreak in Korea
Auteurs : Inkyung Ahn [Corée du Sud] ; Seongman Heo [Corée du Sud] ; Seunghyun Ji [Corée du Sud] ; Kyung Hyun Kim [Corée du Sud] ; Taehwan Kim [Corée du Sud] ; Eun Joo Lee [Corée du Sud] ; Jooyoung Park [Corée du Sud] ; Keehoon Sung [Corée du Sud]Source :
- Journal of Theoretical Biology [ 0022-5193 ] ; 2017.
Descripteurs français
- KwdFr :
- Algorithmes, Coronavirus du syndrome respiratoire du Moyen-Orient, Dynamique non linéaire, Flambées de maladies (), Humains, Infections à coronavirus (mortalité), Infections à coronavirus (transmission), Infections à coronavirus (épidémiologie), Quarantaine (), République de Corée (épidémiologie), Santé publique (), Santé publique (économie), Simulation numérique, Taux de survie.
- MESH :
- mortalité : Infections à coronavirus.
- économie : Infections à coronavirus, Santé publique.
- épidémiologie : Infections à coronavirus, République de Corée.
- Algorithmes, Coronavirus du syndrome respiratoire du Moyen-Orient, Dynamique non linéaire, Flambées de maladies, Humains, Quarantaine, Santé publique, Simulation numérique, Taux de survie.
English descriptors
- KwdEn :
- Algorithms, Computer Simulation, Coronavirus Infections (epidemiology), Coronavirus Infections (mortality), Coronavirus Infections (transmission), Disease Outbreaks (prevention & control), Humans, Middle East Respiratory Syndrome Coronavirus, Nonlinear Dynamics, Public Health (economics), Public Health (methods), Quarantine (statistics & numerical data), Republic of Korea (epidemiology), Survival Rate.
- MESH :
- economics : Public Health.
- epidemiology : Coronavirus Infections, Republic of Korea.
- methods : Public Health.
- mortality : Coronavirus Infections.
- prevention & control : Disease Outbreaks.
- statistics & numerical data : Quarantine.
- transmission : Coronavirus Infections.
- Algorithms, Computer Simulation, Humans, Middle East Respiratory Syndrome Coronavirus, Nonlinear Dynamics, Survival Rate.
Abstract
By fitting the SIQ model, which employs a squashing-type function for As a practical guideline to avoid another similar unexpected outbreak, we draw the conclusion that combined efforts in the early stage are critically important, and sharing information including the names of affected hospitals or countries, clinical situations, and prevention methods might be important for global public health control. We applied optimal control theory to the controlled SIQ model with the goal of minimizing the infectious compartment population and the cost of implementing the quarantine and isolation strategies. Simulation results show that the number of the MERS cases can be controlled reasonably well via the optimal control approach.
Url:
DOI: 10.1016/j.jtbi.2017.10.004
PubMed: 29031518
PubMed Central: 7094113
Affiliations:
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Le document en format XML
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<term>Coronavirus Infections (transmission)</term>
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<term>Infections à coronavirus (mortalité)</term>
<term>Infections à coronavirus (transmission)</term>
<term>Infections à coronavirus (épidémiologie)</term>
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<front><div type="abstract" xml:lang="en"><title>Highlights</title>
<p><list list-type="simple" id="celist0001"><list-item id="celistitem0001"><label>•</label>
<p id="para0001">By fitting the SIQ model, which employs a squashing-type function for <italic>β</italic>
(t), to the real data on the confirmed cases and the quarantined cases, we obtained reasonable performance. Also, it turned out that the resultant estimated parameters belonged to plausible ranges. By comparison, the conventional SIQ model utilizing a constant transmission probability could not explain the observed data well.</p>
</list-item>
<list-item id="celistitem0002"><label>•</label>
<p id="para0002">As a practical guideline to avoid another similar unexpected outbreak, we draw the conclusion that combined efforts in the early stage are critically important, and sharing information including the names of affected hospitals or countries, clinical situations, and prevention methods might be important for global public health control.</p>
</list-item>
<list-item id="celistitem0003"><label>•</label>
<p id="para0003">We applied optimal control theory to the controlled SIQ model with the goal of minimizing the infectious compartment population and the cost of implementing the quarantine and isolation strategies. Simulation results show that the number of the MERS cases can be controlled reasonably well via the optimal control approach.</p>
</list-item>
</list>
</p>
</div>
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</TEI>
<affiliations><list><country><li>Corée du Sud</li>
</country>
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<tree><country name="Corée du Sud"><noRegion><name sortKey="Ahn, Inkyung" sort="Ahn, Inkyung" uniqKey="Ahn I" first="Inkyung" last="Ahn">Inkyung Ahn</name>
</noRegion>
<name sortKey="Heo, Seongman" sort="Heo, Seongman" uniqKey="Heo S" first="Seongman" last="Heo">Seongman Heo</name>
<name sortKey="Ji, Seunghyun" sort="Ji, Seunghyun" uniqKey="Ji S" first="Seunghyun" last="Ji">Seunghyun Ji</name>
<name sortKey="Kim, Kyung Hyun" sort="Kim, Kyung Hyun" uniqKey="Kim K" first="Kyung Hyun" last="Kim">Kyung Hyun Kim</name>
<name sortKey="Kim, Taehwan" sort="Kim, Taehwan" uniqKey="Kim T" first="Taehwan" last="Kim">Taehwan Kim</name>
<name sortKey="Lee, Eun Joo" sort="Lee, Eun Joo" uniqKey="Lee E" first="Eun Joo" last="Lee">Eun Joo Lee</name>
<name sortKey="Park, Jooyoung" sort="Park, Jooyoung" uniqKey="Park J" first="Jooyoung" last="Park">Jooyoung Park</name>
<name sortKey="Sung, Keehoon" sort="Sung, Keehoon" uniqKey="Sung K" first="Keehoon" last="Sung">Keehoon Sung</name>
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