Effects of reactive social distancing on the 1918 influenza pandemic
Identifieur interne : 001C91 ( Ncbi/Curation ); précédent : 001C90; suivant : 001C92Effects of reactive social distancing on the 1918 influenza pandemic
Auteurs : Duo Yu [République populaire de Chine, États-Unis] ; Qianying Lin [République populaire de Chine] ; Alice Py Chiu [République populaire de Chine] ; Daihai He [République populaire de Chine]Source :
- PLoS ONE [ 1932-6203 ] ; 2017.
Descripteurs français
- KwdFr :
- MESH :
- psychologie : Grippe humaine.
- épidémiologie : Grippe humaine.
- Distance sociale, Grippe humaine, Humains, Modèles statistiques, Pandémie de grippe de 1918-1919, Royaume-Uni.
English descriptors
- KwdEn :
- MESH :
- epidemiology : Influenza, Human.
- prevention & control : Influenza Pandemic, 1918-1919, Influenza, Human.
- psychology : Influenza, Human.
- statistics & numerical data : Influenza Pandemic, 1918-1919.
- Humans, Models, Statistical, Social Distance, United Kingdom.
Abstract
The 1918 influenza pandemic was characterized by multiple epidemic waves. We investigated reactive social distancing, a form of behavioral response where individuals avoid potentially infectious contacts in response to available information on an ongoing epidemic or pandemic. We modelled its effects on the three influenza waves in the United Kingdom. In previous studies, human behavioral response was modelled by a Power function of the proportion of recent influenza mortality in a population, and by a Hill function, which is a function of the number of recent influenza mortality. Using a simple epidemic model with a Power function and one common set of parameters, we provided a good model fit for the observed multiple epidemic waves in London boroughs, Birmingham and Liverpool. We further applied the model parameters from these three cities to all 334 administrative units in England and Wales and including the population sizes of individual administrative units. We computed the Pearson’s correlation between the observed and simulated for each administrative unit. We found a median correlation of 0.636, indicating that our model predictions are performing reasonably well. Our modelling approach is an improvement from previous studies where separate models are fitted to each city. With the reduced number of model parameters used, we achieved computational efficiency gain without over-fitting the model. We also showed the importance of reactive behavioral distancing as a potential non-pharmaceutical intervention during an influenza pandemic. Our work has both scientific and public health significance.
Url:
DOI: 10.1371/journal.pone.0180545
PubMed: 28704460
PubMed Central: 5507503
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PMC:5507503Le document en format XML
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Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong (SAR)</wicri:regionArea><wicri:noRegion>Hong Kong (SAR)</wicri:noRegion></affiliation><affiliation wicri:level="3"><nlm:aff id="aff002"><addr-line>Department of Biostatistics, School of Public Health, University of Texas, Health Science Center at Houston, Houston, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biostatistics, School of Public Health, University of Texas, Health Science Center at Houston, Houston</wicri:regionArea><placeName><settlement type="city">Houston</settlement><region type="state">Texas</region></placeName></affiliation></author><author><name sortKey="Lin, Qianying" sort="Lin, Qianying" uniqKey="Lin Q" first="Qianying" last="Lin">Qianying Lin</name><affiliation wicri:level="1"><nlm:aff id="aff001"><addr-line>Department of Applied Mathematics, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong (SAR), China</addr-line></nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Applied Mathematics, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong (SAR)</wicri:regionArea><wicri:noRegion>Hong Kong (SAR)</wicri:noRegion></affiliation></author><author><name sortKey="Chiu, Alice Py" sort="Chiu, Alice Py" uniqKey="Chiu A" first="Alice Py" last="Chiu">Alice Py Chiu</name><affiliation wicri:level="1"><nlm:aff id="aff001"><addr-line>Department of Applied Mathematics, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong (SAR), China</addr-line></nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Applied Mathematics, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong (SAR)</wicri:regionArea><wicri:noRegion>Hong Kong (SAR)</wicri:noRegion></affiliation></author><author><name sortKey="He, Daihai" sort="He, Daihai" uniqKey="He D" first="Daihai" last="He">Daihai He</name><affiliation wicri:level="1"><nlm:aff id="aff001"><addr-line>Department of Applied Mathematics, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong (SAR), China</addr-line></nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Applied Mathematics, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong (SAR)</wicri:regionArea><wicri:noRegion>Hong Kong (SAR)</wicri:noRegion></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2017">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Humans</term><term>Influenza Pandemic, 1918-1919 (prevention & control)</term><term>Influenza Pandemic, 1918-1919 (statistics & numerical data)</term><term>Influenza, Human (epidemiology)</term><term>Influenza, Human (prevention & control)</term><term>Influenza, Human (psychology)</term><term>Models, Statistical</term><term>Social Distance</term><term>United Kingdom</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Distance sociale</term><term>Grippe humaine ()</term><term>Grippe humaine (psychologie)</term><term>Grippe humaine (épidémiologie)</term><term>Humains</term><term>Modèles statistiques</term><term>Pandémie de grippe de 1918-1919 ()</term><term>Royaume-Uni</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="prevention & control" xml:lang="en"><term>Influenza Pandemic, 1918-1919</term><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="psychologie" xml:lang="fr"><term>Grippe humaine</term></keywords><keywords scheme="MESH" qualifier="psychology" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Influenza Pandemic, 1918-1919</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Grippe humaine</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Humans</term><term>Models, Statistical</term><term>Social Distance</term><term>United Kingdom</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Distance sociale</term><term>Grippe humaine</term><term>Humains</term><term>Modèles statistiques</term><term>Pandémie de grippe de 1918-1919</term><term>Royaume-Uni</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>The 1918 influenza pandemic was characterized by multiple epidemic waves. We investigated reactive social distancing, a form of behavioral response where individuals avoid potentially infectious contacts in response to available information on an ongoing epidemic or pandemic. We modelled its effects on the three influenza waves in the United Kingdom. In previous studies, human behavioral response was modelled by a Power function of the proportion of recent influenza mortality in a population, and by a Hill function, which is a function of the number of recent influenza mortality. Using a simple epidemic model with a Power function and one common set of parameters, we provided a good model fit for the observed multiple epidemic waves in London boroughs, Birmingham and Liverpool. We further applied the model parameters from these three cities to all 334 administrative units in England and Wales and including the population sizes of individual administrative units. We computed the Pearson’s correlation between the observed and simulated for each administrative unit. We found a median correlation of 0.636, indicating that our model predictions are performing reasonably well. Our modelling approach is an improvement from previous studies where separate models are fitted to each city. With the reduced number of model parameters used, we achieved computational efficiency gain without over-fitting the model. We also showed the importance of reactive behavioral distancing as a potential non-pharmaceutical intervention during an influenza pandemic. Our work has both scientific and public health significance.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Johnson, Np" uniqKey="Johnson N">NP Johnson</name></author><author><name sortKey="Mueller, J" uniqKey="Mueller J">J Mueller</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Barry, Jm" uniqKey="Barry J">JM Barry</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bauch, C" uniqKey="Bauch C">C Bauch</name></author><author><name sortKey="D Nofrio, A" uniqKey="D Nofrio A">A d’Onofrio</name></author><author><name sortKey="Manfredi, P" uniqKey="Manfredi P">P Manfredi</name></author><author><name sortKey="Manfredi, P" uniqKey="Manfredi P">P Manfredi</name></author><author><name sortKey="D Nofrio, A" uniqKey="D Nofrio A">A d’Onofrio</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="D Nofrio, A" uniqKey="D Nofrio A">A 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Earn</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Caley, P" uniqKey="Caley P">P Caley</name></author><author><name sortKey="Philp, Dj" uniqKey="Philp D">DJ Philp</name></author><author><name sortKey="Mccracken, K" uniqKey="Mccracken K">K McCracken</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Mathews, Jd" uniqKey="Mathews J">JD Mathews</name></author><author><name sortKey="Mccaw, Ct" uniqKey="Mccaw C">CT McCaw</name></author><author><name sortKey="Mcvernon, J" uniqKey="Mcvernon J">J McVernon</name></author><author><name sortKey="Mcbryde, Es" uniqKey="Mcbryde E">ES McBryde</name></author><author><name sortKey="Mccaw, Jm" uniqKey="Mccaw J">JM McCaw</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bolton, Kj" uniqKey="Bolton K">KJ Bolton</name></author><author><name sortKey="Mccaw, Jm" uniqKey="Mccaw J">JM McCaw</name></author><author><name sortKey="Mcvernon, J" uniqKey="Mcvernon J">J McVernon</name></author><author><name sortKey="Mathews, Jd" uniqKey="Mathews J">JD Mathews</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Gani, R" uniqKey="Gani R">R Gani</name></author><author><name sortKey="Hughes, H" uniqKey="Hughes H">H Hughes</name></author><author><name sortKey="Fleming, D" uniqKey="Fleming D">D Fleming</name></author><author><name sortKey="Griffin, T" uniqKey="Griffin T">T Griffin</name></author><author><name sortKey="Medlock, J" uniqKey="Medlock J">J Medlock</name></author><author><name sortKey="Leach, S" uniqKey="Leach S">S Leach</name></author></analytic></biblStruct></listBibl></div1></back></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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