Spatiotemporal Patterns and Diffusion of the 1918 Influenza Pandemic in British India.
Identifieur interne : 000230 ( PubMed/Corpus ); précédent : 000229; suivant : 000231Spatiotemporal Patterns and Diffusion of the 1918 Influenza Pandemic in British India.
Auteurs : Olivia Reyes ; Elizabeth C. Lee ; Pratha Sah ; Cécile Viboud ; Siddharth Chandra ; Shweta BansalSource :
- American journal of epidemiology [ 1476-6256 ] ; 2018.
English descriptors
- KwdEn :
- Age Distribution, Cause of Death, Fever (mortality), History, 20th Century, Humans, India (epidemiology), Influenza Pandemic, 1918-1919 (history), Influenza Pandemic, 1918-1919 (mortality), Influenza, Human (epidemiology), Influenza, Human (history), Influenza, Human (mortality), Rainforest, Respiratory Tract Diseases (mortality), Seasons, Socioeconomic Factors, Spatio-Temporal Analysis, Travel.
- MESH :
- geographic , epidemiology : India.
- epidemiology : Influenza, Human.
- history : Influenza Pandemic, 1918-1919, Influenza, Human.
- mortality : Fever, Influenza Pandemic, 1918-1919, Influenza, Human, Respiratory Tract Diseases.
- Age Distribution, Cause of Death, History, 20th Century, Humans, Rainforest, Seasons, Socioeconomic Factors, Spatio-Temporal Analysis, Travel.
Abstract
The factors that drive spatial heterogeneity and diffusion of pandemic influenza remain debated. We characterized the spatiotemporal mortality patterns of the 1918 influenza pandemic in British India and studied the role of demographic factors, environmental variables, and mobility processes on the observed patterns of spread. Fever-related and all-cause excess mortality data across 206 districts in India from January 1916 to December 1920 were analyzed while controlling for variation in seasonality particular to India. Aspects of the 1918 autumn wave in India matched signature features of influenza pandemics, with high disease burden among young adults, (moderate) spatial heterogeneity in burden, and highly synchronized outbreaks across the country deviating from annual seasonality. Importantly, we found population density and rainfall explained the spatial variation in excess mortality, and long-distance travel via railroad was predictive of the observed spatial diffusion of disease. A spatiotemporal analysis of mortality patterns during the 1918 influenza pandemic in India was integrated in this study with data on underlying factors and processes to reveal transmission mechanisms in a large, intensely connected setting with significant climatic variability. The characterization of such heterogeneity during historical pandemics is crucial to prepare for future pandemics.
DOI: 10.1093/aje/kwy209
PubMed: 30252017
Links to Exploration step
pubmed:30252017Le document en format XML
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Lee</name><affiliation><nlm:affiliation>Department of Biology, Georgetown University, Washington, DC.</nlm:affiliation></affiliation></author><author><name sortKey="Sah, Pratha" sort="Sah, Pratha" uniqKey="Sah P" first="Pratha" last="Sah">Pratha Sah</name><affiliation><nlm:affiliation>Department of Biology, Georgetown University, Washington, DC.</nlm:affiliation></affiliation></author><author><name sortKey="Viboud, Cecile" sort="Viboud, Cecile" uniqKey="Viboud C" first="Cécile" last="Viboud">Cécile Viboud</name><affiliation><nlm:affiliation>Fogarty International Center, National Institutes of Health, Bethesda, Maryland.</nlm:affiliation></affiliation></author><author><name sortKey="Chandra, Siddharth" sort="Chandra, Siddharth" uniqKey="Chandra S" first="Siddharth" last="Chandra">Siddharth Chandra</name><affiliation><nlm:affiliation>Asian Studies Center, James Madison College, and the Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan.</nlm:affiliation></affiliation></author><author><name sortKey="Bansal, Shweta" sort="Bansal, Shweta" uniqKey="Bansal S" first="Shweta" last="Bansal">Shweta Bansal</name><affiliation><nlm:affiliation>Department of Biology, Georgetown University, Washington, DC.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2018">2018</date><idno type="RBID">pubmed:30252017</idno><idno type="pmid">30252017</idno><idno type="doi">10.1093/aje/kwy209</idno><idno type="wicri:Area/PubMed/Corpus">000230</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000230</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Spatiotemporal Patterns and Diffusion of the 1918 Influenza Pandemic in British India.</title><author><name sortKey="Reyes, Olivia" sort="Reyes, Olivia" uniqKey="Reyes O" first="Olivia" last="Reyes">Olivia Reyes</name><affiliation><nlm:affiliation>Department of Biology, Georgetown University, Washington, DC.</nlm:affiliation></affiliation></author><author><name sortKey="Lee, Elizabeth C" sort="Lee, Elizabeth C" uniqKey="Lee E" first="Elizabeth C" last="Lee">Elizabeth C. 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We characterized the spatiotemporal mortality patterns of the 1918 influenza pandemic in British India and studied the role of demographic factors, environmental variables, and mobility processes on the observed patterns of spread. Fever-related and all-cause excess mortality data across 206 districts in India from January 1916 to December 1920 were analyzed while controlling for variation in seasonality particular to India. Aspects of the 1918 autumn wave in India matched signature features of influenza pandemics, with high disease burden among young adults, (moderate) spatial heterogeneity in burden, and highly synchronized outbreaks across the country deviating from annual seasonality. Importantly, we found population density and rainfall explained the spatial variation in excess mortality, and long-distance travel via railroad was predictive of the observed spatial diffusion of disease. A spatiotemporal analysis of mortality patterns during the 1918 influenza pandemic in India was integrated in this study with data on underlying factors and processes to reveal transmission mechanisms in a large, intensely connected setting with significant climatic variability. The characterization of such heterogeneity during historical pandemics is crucial to prepare for future pandemics.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">30252017</PMID><DateCompleted><Year>2019</Year><Month>09</Month><Day>16</Day></DateCompleted><DateRevised><Year>2019</Year><Month>12</Month><Day>01</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1476-6256</ISSN><JournalIssue CitedMedium="Internet"><Volume>187</Volume><Issue>12</Issue><PubDate><Year>2018</Year><Month>12</Month><Day>01</Day></PubDate></JournalIssue><Title>American journal of epidemiology</Title><ISOAbbreviation>Am. J. Epidemiol.</ISOAbbreviation></Journal><ArticleTitle>Spatiotemporal Patterns and Diffusion of the 1918 Influenza Pandemic in British India.</ArticleTitle><Pagination><MedlinePgn>2550-2560</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/aje/kwy209</ELocationID><Abstract><AbstractText>The factors that drive spatial heterogeneity and diffusion of pandemic influenza remain debated. We characterized the spatiotemporal mortality patterns of the 1918 influenza pandemic in British India and studied the role of demographic factors, environmental variables, and mobility processes on the observed patterns of spread. Fever-related and all-cause excess mortality data across 206 districts in India from January 1916 to December 1920 were analyzed while controlling for variation in seasonality particular to India. Aspects of the 1918 autumn wave in India matched signature features of influenza pandemics, with high disease burden among young adults, (moderate) spatial heterogeneity in burden, and highly synchronized outbreaks across the country deviating from annual seasonality. Importantly, we found population density and rainfall explained the spatial variation in excess mortality, and long-distance travel via railroad was predictive of the observed spatial diffusion of disease. A spatiotemporal analysis of mortality patterns during the 1918 influenza pandemic in India was integrated in this study with data on underlying factors and processes to reveal transmission mechanisms in a large, intensely connected setting with significant climatic variability. The characterization of such heterogeneity during historical pandemics is crucial to prepare for future pandemics.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Reyes</LastName><ForeName>Olivia</ForeName><Initials>O</Initials><AffiliationInfo><Affiliation>Department of Biology, Georgetown University, Washington, DC.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lee</LastName><ForeName>Elizabeth C</ForeName><Initials>EC</Initials><AffiliationInfo><Affiliation>Department of Biology, Georgetown University, Washington, DC.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sah</LastName><ForeName>Pratha</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>Department of Biology, Georgetown University, Washington, DC.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Viboud</LastName><ForeName>Cécile</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>Fogarty International Center, National Institutes of Health, Bethesda, Maryland.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Chandra</LastName><ForeName>Siddharth</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Asian Studies Center, James Madison College, and the Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bansal</LastName><ForeName>Shweta</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Department of Biology, Georgetown University, Washington, DC.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Fogarty International Center, National Institutes of Health, Bethesda, Maryland.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>R21 DA025917</GrantID><Acronym>DA</Acronym><Agency>NIDA NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016456">Historical Article</PublicationType><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType><PublicationType UI="D013486">Research Support, U.S. Gov't, Non-P.H.S.</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Am J Epidemiol</MedlineTA><NlmUniqueID>7910653</NlmUniqueID><ISSNLinking>0002-9262</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D017677" MajorTopicYN="N">Age Distribution</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002423" MajorTopicYN="N">Cause of Death</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005334" MajorTopicYN="N">Fever</DescriptorName><QualifierName UI="Q000401" 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