Stochastic modelling of infectious diseases for heterogeneous populations
Identifieur interne : 000F41 ( Main/Exploration ); précédent : 000F40; suivant : 000F42Stochastic modelling of infectious diseases for heterogeneous populations
Auteurs : Rui-Xing Ming [République populaire de Chine] ; Jiming Liu [Hong Kong] ; William K. W. Cheung [Hong Kong] ; Xiang Wan [Hong Kong]Source :
- Infectious Diseases of Poverty [ 2095-5162 ] ; 2016.
Descripteurs français
- KwdFr :
- Flambées de maladies, Grippe humaine (transmission), Grippe humaine (virologie), Grippe humaine (épidémiologie), Hong Kong, Humains, Maladies transmissibles (transmission), Maladies transmissibles (épidémiologie), Maladies transmissibles (étiologie), Modèles théoriques, Processus stochastiques, Sous-type H1N1 du virus de la grippe A (physiologie).
- MESH :
- physiologie : Sous-type H1N1 du virus de la grippe A.
- virologie : Grippe humaine.
- épidémiologie : Grippe humaine, Maladies transmissibles.
- étiologie : Maladies transmissibles.
- Flambées de maladies, Hong Kong, Humains, Modèles théoriques, Processus stochastiques.
English descriptors
- KwdEn :
- Communicable Diseases (epidemiology), Communicable Diseases (etiology), Communicable Diseases (transmission), Disease Outbreaks, Hong Kong, Humans, Influenza A Virus, H1N1 Subtype (physiology), Influenza, Human (epidemiology), Influenza, Human (transmission), Influenza, Human (virology), Models, Theoretical, Stochastic Processes.
- MESH :
- epidemiology : Communicable Diseases, Influenza, Human.
- etiology : Communicable Diseases.
- physiology : Influenza A Virus, H1N1 Subtype.
- transmission : Communicable Diseases, Influenza, Human.
- virology : Influenza, Human.
- Disease Outbreaks, Hong Kong, Humans, Models, Theoretical, Stochastic Processes.
Abstract
Infectious diseases such as SARS and H1N1 can significantly impact people’s lives and cause severe social and economic damages. Recent outbreaks have stressed the urgency of effective research on the dynamics of infectious disease spread. However, it is difficult to predict when and where outbreaks may emerge and how infectious diseases spread because many factors affect their transmission, and some of them may be unknown.
One feasible means to promptly detect an outbreak and track the progress of disease spread is to implement surveillance systems in regional or national health and medical centres. The accumulated surveillance data, including temporal, spatial, clinical, and demographic information can provide valuable information that can be exploited to better understand and model the dynamics of infectious disease spread. The aim of this work is to develop and empirically evaluate a stochastic model that allows the investigation of transmission patterns of infectious diseases in heterogeneous populations.
We test the proposed model on simulation data and apply it to the surveillance data from the 2009 H1N1 pandemic in Hong Kong. In the simulation experiment, our model achieves high accuracy in parameter estimation (less than 10.0
We propose a stochastic model to study the dynamics of infectious disease spread in heterogeneous populations from temporal-spatial surveillance data. The proposed model is evaluated using both simulated data and the real data from the 2009 H1N1 epidemic in Hong Kong and achieves acceptable prediction accuracy. We believe that our model can provide valuable insights for public health authorities to predict the effect of disease spread and analyse its underlying factors and to guide new control efforts.
The online version of this article (doi:10.1186/s40249-016-0199-5) contains supplementary material, which is available to authorized users.
Url:
DOI: 10.1186/s40249-016-0199-5
PubMed: 28003016
PubMed Central: 5178099
Affiliations:
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W." last="Cheung">William K. W. Cheung</name><affiliation wicri:level="1"><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="GRID">grid.221309.b</institution-id><institution-id institution-id-type="ISNI">0000000417645980</institution-id><institution>Department of Computer Science,</institution><institution>Hong Kong Baptist University,</institution></institution-wrap>Kowloon Tong, Hong Kong</nlm:aff><country xml:lang="fr">Hong Kong</country><wicri:regionArea>Kowloon Tong</wicri:regionArea></affiliation></author><author><name sortKey="Wan, Xiang" sort="Wan, Xiang" uniqKey="Wan X" first="Xiang" last="Wan">Xiang Wan</name><affiliation wicri:level="1"><nlm:aff id="Aff3"><institution-wrap><institution-id institution-id-type="GRID">grid.221309.b</institution-id><institution-id institution-id-type="ISNI">0000000417645980</institution-id><institution>Department of Computer Science & Institute of Theoretical and Computational Study,</institution><institution>Hong Kong Baptist University,</institution></institution-wrap>Kowloon Tong, Hong Kong</nlm:aff><country xml:lang="fr">Hong Kong</country><wicri:regionArea>Kowloon Tong</wicri:regionArea></affiliation></author></analytic><series><title level="j">Infectious Diseases of Poverty</title><idno type="ISSN">2095-5162</idno><idno type="eISSN">2049-9957</idno><imprint><date when="2016">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Communicable Diseases (epidemiology)</term><term>Communicable Diseases (etiology)</term><term>Communicable Diseases (transmission)</term><term>Disease Outbreaks</term><term>Hong Kong</term><term>Humans</term><term>Influenza A Virus, H1N1 Subtype (physiology)</term><term>Influenza, Human (epidemiology)</term><term>Influenza, Human (transmission)</term><term>Influenza, Human (virology)</term><term>Models, Theoretical</term><term>Stochastic Processes</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Flambées de maladies</term><term>Grippe humaine (transmission)</term><term>Grippe humaine (virologie)</term><term>Grippe humaine (épidémiologie)</term><term>Hong Kong</term><term>Humains</term><term>Maladies transmissibles (transmission)</term><term>Maladies transmissibles (épidémiologie)</term><term>Maladies transmissibles (étiologie)</term><term>Modèles théoriques</term><term>Processus stochastiques</term><term>Sous-type H1N1 du virus de la grippe A (physiologie)</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Communicable Diseases</term><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="etiology" xml:lang="en"><term>Communicable Diseases</term></keywords><keywords scheme="MESH" qualifier="physiologie" xml:lang="fr"><term>Sous-type H1N1 du virus de la grippe A</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Influenza A Virus, H1N1 Subtype</term></keywords><keywords scheme="MESH" qualifier="transmission" xml:lang="en"><term>Communicable Diseases</term><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="virologie" xml:lang="fr"><term>Grippe humaine</term></keywords><keywords scheme="MESH" qualifier="virology" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Grippe humaine</term><term>Maladies transmissibles</term></keywords><keywords scheme="MESH" qualifier="étiologie" xml:lang="fr"><term>Maladies transmissibles</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Disease Outbreaks</term><term>Hong Kong</term><term>Humans</term><term>Models, Theoretical</term><term>Stochastic Processes</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Flambées de maladies</term><term>Hong Kong</term><term>Humains</term><term>Modèles théoriques</term><term>Processus stochastiques</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>Infectious diseases such as SARS and H1N1 can significantly impact people’s lives and cause severe social and economic damages. Recent outbreaks have stressed the urgency of effective research on the dynamics of infectious disease spread. However, it is difficult to predict when and where outbreaks may emerge and how infectious diseases spread because many factors affect their transmission, and some of them may be unknown.</p></sec><sec><title>Methods</title><p>One feasible means to promptly detect an outbreak and track the progress of disease spread is to implement surveillance systems in regional or national health and medical centres. The accumulated surveillance data, including temporal, spatial, clinical, and demographic information can provide valuable information that can be exploited to better understand and model the dynamics of infectious disease spread. The aim of this work is to develop and empirically evaluate a stochastic model that allows the investigation of transmission patterns of infectious diseases in heterogeneous populations.</p></sec><sec><title>Results</title><p>We test the proposed model on simulation data and apply it to the surveillance data from the 2009 H1N1 pandemic in Hong Kong. In the simulation experiment, our model achieves high accuracy in parameter estimation (less than 10.0 <italic>%</italic> mean absolute percentage error). In terms of the forward prediction of case incidence, the mean absolute percentage errors are 17.3 <italic>%</italic> for the simulation experiment and 20.0 <italic>%</italic> for the experiment on the real surveillance data.</p></sec><sec><title>Conclusion</title><p>We propose a stochastic model to study the dynamics of infectious disease spread in heterogeneous populations from temporal-spatial surveillance data. The proposed model is evaluated using both simulated data and the real data from the 2009 H1N1 epidemic in Hong Kong and achieves acceptable prediction accuracy. We believe that our model can provide valuable insights for public health authorities to predict the effect of disease spread and analyse its underlying factors and to guide new control efforts.</p></sec><sec><title>Electronic supplementary material</title><p>The online version of this article (doi:10.1186/s40249-016-0199-5) contains supplementary material, which is available to authorized users.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Cohen, J" uniqKey="Cohen J">J Cohen</name></author><author><name sortKey="Enserink, M" uniqKey="Enserink M">M Enserink</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ferguson, Nm" uniqKey="Ferguson N">NM Ferguson</name></author><author><name sortKey="Cummings, Da" uniqKey="Cummings D">DA Cummings</name></author><author><name sortKey="Fraser, C" uniqKey="Fraser C">C Fraser</name></author><author><name sortKey="Cajka, Jc" 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uniqKey="Aramini J">J Aramini</name></author><author><name sortKey="Edge, G" uniqKey="Edge G">G Edge</name></author><author><name sortKey="Moore, Km" uniqKey="Moore K">KM Moore</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>Hong Kong</li><li>République populaire de Chine</li></country></list><tree><country name="République populaire de Chine"><noRegion><name sortKey="Ming, Rui Xing" sort="Ming, Rui Xing" uniqKey="Ming R" first="Rui-Xing" last="Ming">Rui-Xing Ming</name></noRegion></country><country name="Hong Kong"><noRegion><name sortKey="Liu, Jiming" sort="Liu, Jiming" uniqKey="Liu J" first="Jiming" last="Liu">Jiming Liu</name></noRegion><name sortKey="Cheung, William K W" sort="Cheung, William K W" uniqKey="Cheung W" first="William K. W." last="Cheung">William K. W. Cheung</name><name sortKey="Wan, Xiang" sort="Wan, Xiang" uniqKey="Wan X" first="Xiang" last="Wan">Xiang Wan</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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