Temporal aggregation impacts on epidemiological simulations employing microcontact data.
Identifieur interne : 000425 ( Main/Exploration ); précédent : 000424; suivant : 000426Temporal aggregation impacts on epidemiological simulations employing microcontact data.
Auteurs : Mohammad Hashemian [Canada] ; Weicheng Qian ; Kevin G. Stanley ; Nathaniel D. OsgoodSource :
- BMC medical informatics and decision making [ 1472-6947 ] ; 2012.
Descripteurs français
- KwdFr :
- Grippe humaine (transmission), Grippe humaine (épidémiologie), Humains (MeSH), Modèles théoriques (MeSH), Projets pilotes (MeSH), Saskatchewan (épidémiologie), Sous-type H1N1 du virus de la grippe A (isolement et purification), Surveillance de la population (méthodes), Traçage des contacts (méthodes), Traçage des contacts (statistiques et données numériques), Études épidémiologiques (MeSH).
- MESH :
- isolement et purification : Sous-type H1N1 du virus de la grippe A.
- méthodes : Surveillance de la population, Traçage des contacts.
- statistiques et données numériques : Traçage des contacts.
- épidémiologie : Grippe humaine, Saskatchewan.
- Humains, Modèles théoriques, Projets pilotes, Études épidémiologiques.
English descriptors
- KwdEn :
- Contact Tracing (methods), Contact Tracing (statistics & numerical data), Epidemiologic Studies (MeSH), Humans (MeSH), Influenza A Virus, H1N1 Subtype (isolation & purification), Influenza, Human (epidemiology), Influenza, Human (transmission), Models, Theoretical (MeSH), Pilot Projects (MeSH), Population Surveillance (methods), Saskatchewan (epidemiology).
- MESH :
- geographic , epidemiology : Saskatchewan.
- epidemiology : Influenza, Human.
- isolation & purification : Influenza A Virus, H1N1 Subtype.
- methods : Contact Tracing, Population Surveillance.
- statistics & numerical data : Contact Tracing.
- transmission : Influenza, Human.
- Epidemiologic Studies, Humans, Models, Theoretical, Pilot Projects.
Abstract
BACKGROUND
Microcontact datasets gathered automatically by electronic devices have the potential augment the study of the spread of contagious disease by providing detailed representations of the study population's contact dynamics. However, the impact of data collection experimental design on the subsequent simulation studies has not been adequately addressed. In particular, the impact of study duration and contact dynamics data aggregation on the ultimate outcome of epidemiological models has not been studied in detail, leaving the potential for erroneous conclusions to be made based on simulation outcomes.
METHODS
We employ a previously published data set covering 36 participants for 92 days and a previously published agent-based H1N1 infection model to analyze the impact of contact dynamics representation on the simulated outcome of H1N1 transmission. We compared simulated attack rates resulting from the empirically recorded contact dynamics (ground truth), aggregated, typical day, and artificially generated synthetic networks.
RESULTS
No aggregation or sampling policy tested was able to reliably reproduce results from the ground-truth full dynamic network. For the population under study, typical day experimental designs - which extrapolate from data collected over a brief period - exhibited too high a variance to produce consistent results. Aggregated data representations systematically overestimated disease burden, and synthetic networks only reproduced the ground truth case when fitting errors systemically underestimated the total contact, compensating for the systemic overestimation from aggregation.
CONCLUSIONS
The interdepedendencies of contact dynamics and disease transmission require that detailed contact dynamics data be employed to secure high fidelity in simulation outcomes of disease burden in at least some populations. This finding serves as motivation for larger, longer and more socially diverse contact dynamics tracing experiments and as a caution to researchers employing calibrated aggregate synthetic representations of contact dynamics in simulation, as the calibration may underestimate disease parameters to compensate for the overestimation of disease burden imposed by the aggregate contact network representation.
DOI: 10.1186/1472-6947-12-132
PubMed: 23153380
PubMed Central: PMC3575321
Affiliations:
Links toward previous steps (curation, corpus...)
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Stanley</name></author><author><name sortKey="Osgood, Nathaniel D" sort="Osgood, Nathaniel D" uniqKey="Osgood N" first="Nathaniel D" last="Osgood">Nathaniel D. Osgood</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2012">2012</date><idno type="RBID">pubmed:23153380</idno><idno type="pmid">23153380</idno><idno type="doi">10.1186/1472-6947-12-132</idno><idno type="pmc">PMC3575321</idno><idno type="wicri:Area/Main/Corpus">000406</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000406</idno><idno type="wicri:Area/Main/Curation">000406</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">000406</idno><idno type="wicri:Area/Main/Exploration">000406</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Temporal aggregation impacts on epidemiological simulations employing microcontact data.</title><author><name sortKey="Hashemian, Mohammad" sort="Hashemian, Mohammad" uniqKey="Hashemian M" first="Mohammad" last="Hashemian">Mohammad Hashemian</name><affiliation wicri:level="1"><nlm:affiliation>Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan</wicri:regionArea><wicri:noRegion>Saskatchewan</wicri:noRegion></affiliation></author><author><name sortKey="Qian, Weicheng" sort="Qian, Weicheng" uniqKey="Qian W" first="Weicheng" last="Qian">Weicheng Qian</name></author><author><name sortKey="Stanley, Kevin G" sort="Stanley, Kevin G" uniqKey="Stanley K" first="Kevin G" last="Stanley">Kevin G. Stanley</name></author><author><name sortKey="Osgood, Nathaniel D" sort="Osgood, Nathaniel D" uniqKey="Osgood N" first="Nathaniel D" last="Osgood">Nathaniel D. Osgood</name></author></analytic><series><title level="j">BMC medical informatics and decision making</title><idno type="eISSN">1472-6947</idno><imprint><date when="2012" type="published">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Contact Tracing (methods)</term><term>Contact Tracing (statistics & numerical data)</term><term>Epidemiologic Studies (MeSH)</term><term>Humans (MeSH)</term><term>Influenza A Virus, H1N1 Subtype (isolation & purification)</term><term>Influenza, Human (epidemiology)</term><term>Influenza, Human (transmission)</term><term>Models, Theoretical (MeSH)</term><term>Pilot Projects (MeSH)</term><term>Population Surveillance (methods)</term><term>Saskatchewan (epidemiology)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Grippe humaine (transmission)</term><term>Grippe humaine (épidémiologie)</term><term>Humains (MeSH)</term><term>Modèles théoriques (MeSH)</term><term>Projets pilotes (MeSH)</term><term>Saskatchewan (épidémiologie)</term><term>Sous-type H1N1 du virus de la grippe A (isolement et purification)</term><term>Surveillance de la population (méthodes)</term><term>Traçage des contacts (méthodes)</term><term>Traçage des contacts (statistiques et données numériques)</term><term>Études épidémiologiques (MeSH)</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Saskatchewan</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="isolation & purification" xml:lang="en"><term>Influenza A Virus, H1N1 Subtype</term></keywords><keywords scheme="MESH" qualifier="isolement et purification" xml:lang="fr"><term>Sous-type H1N1 du virus de la grippe A</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Contact Tracing</term><term>Population Surveillance</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Surveillance de la population</term><term>Traçage des contacts</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Contact Tracing</term></keywords><keywords scheme="MESH" qualifier="statistiques et données numériques" xml:lang="fr"><term>Traçage des contacts</term></keywords><keywords scheme="MESH" qualifier="transmission" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Grippe humaine</term><term>Saskatchewan</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Epidemiologic Studies</term><term>Humans</term><term>Models, Theoretical</term><term>Pilot Projects</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Humains</term><term>Modèles théoriques</term><term>Projets pilotes</term><term>Études épidémiologiques</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b></p><p>Microcontact datasets gathered automatically by electronic devices have the potential augment the study of the spread of contagious disease by providing detailed representations of the study population's contact dynamics. However, the impact of data collection experimental design on the subsequent simulation studies has not been adequately addressed. In particular, the impact of study duration and contact dynamics data aggregation on the ultimate outcome of epidemiological models has not been studied in detail, leaving the potential for erroneous conclusions to be made based on simulation outcomes.</p></div><div type="abstract" xml:lang="en"><p><b>METHODS</b></p><p>We employ a previously published data set covering 36 participants for 92 days and a previously published agent-based H1N1 infection model to analyze the impact of contact dynamics representation on the simulated outcome of H1N1 transmission. We compared simulated attack rates resulting from the empirically recorded contact dynamics (ground truth), aggregated, typical day, and artificially generated synthetic networks.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>No aggregation or sampling policy tested was able to reliably reproduce results from the ground-truth full dynamic network. For the population under study, typical day experimental designs - which extrapolate from data collected over a brief period - exhibited too high a variance to produce consistent results. Aggregated data representations systematically overestimated disease burden, and synthetic networks only reproduced the ground truth case when fitting errors systemically underestimated the total contact, compensating for the systemic overestimation from aggregation.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b></p><p>The interdepedendencies of contact dynamics and disease transmission require that detailed contact dynamics data be employed to secure high fidelity in simulation outcomes of disease burden in at least some populations. This finding serves as motivation for larger, longer and more socially diverse contact dynamics tracing experiments and as a caution to researchers employing calibrated aggregate synthetic representations of contact dynamics in simulation, as the calibration may underestimate disease parameters to compensate for the overestimation of disease burden imposed by the aggregate contact network representation.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">23153380</PMID><DateCompleted><Year>2013</Year><Month>05</Month><Day>24</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1472-6947</ISSN><JournalIssue CitedMedium="Internet"><Volume>12</Volume><PubDate><Year>2012</Year><Month>Nov</Month><Day>15</Day></PubDate></JournalIssue><Title>BMC medical informatics and decision making</Title><ISOAbbreviation>BMC Med Inform Decis Mak</ISOAbbreviation></Journal><ArticleTitle>Temporal aggregation impacts on epidemiological simulations employing microcontact data.</ArticleTitle><Pagination><MedlinePgn>132</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/1472-6947-12-132</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Microcontact datasets gathered automatically by electronic devices have the potential augment the study of the spread of contagious disease by providing detailed representations of the study population's contact dynamics. However, the impact of data collection experimental design on the subsequent simulation studies has not been adequately addressed. In particular, the impact of study duration and contact dynamics data aggregation on the ultimate outcome of epidemiological models has not been studied in detail, leaving the potential for erroneous conclusions to be made based on simulation outcomes.</AbstractText><AbstractText Label="METHODS" NlmCategory="METHODS">We employ a previously published data set covering 36 participants for 92 days and a previously published agent-based H1N1 infection model to analyze the impact of contact dynamics representation on the simulated outcome of H1N1 transmission. We compared simulated attack rates resulting from the empirically recorded contact dynamics (ground truth), aggregated, typical day, and artificially generated synthetic networks.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">No aggregation or sampling policy tested was able to reliably reproduce results from the ground-truth full dynamic network. For the population under study, typical day experimental designs - which extrapolate from data collected over a brief period - exhibited too high a variance to produce consistent results. Aggregated data representations systematically overestimated disease burden, and synthetic networks only reproduced the ground truth case when fitting errors systemically underestimated the total contact, compensating for the systemic overestimation from aggregation.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">The interdepedendencies of contact dynamics and disease transmission require that detailed contact dynamics data be employed to secure high fidelity in simulation outcomes of disease burden in at least some populations. This finding serves as motivation for larger, longer and more socially diverse contact dynamics tracing experiments and as a caution to researchers employing calibrated aggregate synthetic representations of contact dynamics in simulation, as the calibration may underestimate disease parameters to compensate for the overestimation of disease burden imposed by the aggregate contact network representation.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Hashemian</LastName><ForeName>Mohammad</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Qian</LastName><ForeName>Weicheng</ForeName><Initials>W</Initials></Author><Author ValidYN="Y"><LastName>Stanley</LastName><ForeName>Kevin G</ForeName><Initials>KG</Initials></Author><Author 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Osgood</name><name sortKey="Qian, Weicheng" sort="Qian, Weicheng" uniqKey="Qian W" first="Weicheng" last="Qian">Weicheng Qian</name><name sortKey="Stanley, Kevin G" sort="Stanley, Kevin G" uniqKey="Stanley K" first="Kevin G" last="Stanley">Kevin G. Stanley</name></noCountry><country name="Canada"><noRegion><name sortKey="Hashemian, Mohammad" sort="Hashemian, Mohammad" uniqKey="Hashemian M" first="Mohammad" last="Hashemian">Mohammad Hashemian</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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