Assessment of yellow fever epidemic risk: an original multi-criteria modeling approach
Yellow fever (YF) virtually disappeared in francophone West African countries as a result of YF mass vaccination campaigns carried out between 1940 and 1953. However, because of the failure to continue mass vaccination campaigns, a resurgence of the deadly disease in many African countries began in...
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description | Yellow fever (YF) virtually disappeared in francophone West African countries as a result of YF mass vaccination campaigns carried out between 1940 and 1953. However, because of the failure to continue mass vaccination campaigns, a resurgence of the deadly disease in many African countries began in the early 1980s. We developed an original modeling approach to assess YF epidemic risk (vulnerability) and to prioritize the populations to be vaccinated.
We chose a two-step assessment of vulnerability at district level consisting of a quantitative and qualitative assessment per country. Quantitative assessment starts with data collection on six risk factors: five risk factors associated with "exposure" to virus/vector and one with "susceptibility" of a district to YF epidemics. The multiple correspondence analysis (MCA) modeling method was specifically adapted to reduce the five exposure variables to one aggregated exposure indicator. Health districts were then projected onto a two-dimensional graph to define different levels of vulnerability. Districts are presented on risk maps for qualitative analysis in consensus groups, allowing the addition of factors, such as population migrations or vector density, that could not be included in MCA. The example of rural districts in Burkina Faso show five distinct clusters of risk profiles. Based on this assessment, 32 of 55 districts comprising over 7 million people were prioritized for preventive vaccination campaigns.
This assessment of yellow fever epidemic risk at the district level includes MCA modeling and consensus group modification. MCA provides a standardized way to reduce complexity. It supports an informed public health decision-making process that empowers local stakeholders through the consensus group. This original approach can be applied to any disease with documented risk factors. |
doi_str_mv | 10.1371/journal.pntd.0000483 |
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We chose a two-step assessment of vulnerability at district level consisting of a quantitative and qualitative assessment per country. Quantitative assessment starts with data collection on six risk factors: five risk factors associated with "exposure" to virus/vector and one with "susceptibility" of a district to YF epidemics. The multiple correspondence analysis (MCA) modeling method was specifically adapted to reduce the five exposure variables to one aggregated exposure indicator. Health districts were then projected onto a two-dimensional graph to define different levels of vulnerability. Districts are presented on risk maps for qualitative analysis in consensus groups, allowing the addition of factors, such as population migrations or vector density, that could not be included in MCA. The example of rural districts in Burkina Faso show five distinct clusters of risk profiles. Based on this assessment, 32 of 55 districts comprising over 7 million people were prioritized for preventive vaccination campaigns.
This assessment of yellow fever epidemic risk at the district level includes MCA modeling and consensus group modification. MCA provides a standardized way to reduce complexity. It supports an informed public health decision-making process that empowers local stakeholders through the consensus group. This original approach can be applied to any disease with documented risk factors.</description><identifier>ISSN: 1935-2735</identifier><identifier>ISSN: 1935-2727</identifier><identifier>EISSN: 1935-2735</identifier><identifier>DOI: 10.1371/journal.pntd.0000483</identifier><identifier>PMID: 19597548</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Burkina Faso - epidemiology ; Data collection ; Decision making ; Disease prevention ; Epidemics ; Fever ; Humans ; Infectious Diseases ; Infectious Diseases/Epidemiology and Control of Infectious Diseases ; Infectious Diseases/Neglected Tropical Diseases ; Infectious Diseases/Tropical and Travel-Associated Diseases ; Infectious Diseases/Viral Infections ; Mathematics ; Mathematics/Mathematical Computing ; Public health ; Public Health and Epidemiology ; Public Health and Epidemiology/Epidemiology ; Public Health and Epidemiology/Immunization ; Public Health and Epidemiology/Infectious Diseases ; Risk Assessment - methods ; Risk Factors ; Tropical diseases ; Vaccines ; Yellow Fever - epidemiology ; Yellow Fever - prevention & control</subject><ispartof>PLoS neglected tropical diseases, 2009-07, Vol.3 (7), p.e483-e483</ispartof><rights>2009 Briand et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Briand S, Beresniak A, Nguyen T, Yonli T, Duru G, et al. (2009) Assessment of Yellow Fever Epidemic Risk: An Original Multi-criteria Modeling Approach. PLoS Negl Trop Dis 3(7): e483. doi:10.1371/journal.pntd.0000483</rights><rights>Briand et al. 2009</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c556t-9a8776cca89fd60a225f9be379219762ecdbee855569e233c807d930981b40c13</citedby><cites>FETCH-LOGICAL-c556t-9a8776cca89fd60a225f9be379219762ecdbee855569e233c807d930981b40c13</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC2704869/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC2704869/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,725,778,782,862,883,2098,2917,23849,27907,27908,53774,53776,79351,79352</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/19597548$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Lopes da Fonseca, Benedito A.</contributor><creatorcontrib>Briand, Sylvie</creatorcontrib><creatorcontrib>Beresniak, Ariel</creatorcontrib><creatorcontrib>Nguyen, Tim</creatorcontrib><creatorcontrib>Yonli, Tajoua</creatorcontrib><creatorcontrib>Duru, Gerard</creatorcontrib><creatorcontrib>Kambire, Chantal</creatorcontrib><creatorcontrib>Perea, William</creatorcontrib><creatorcontrib>Yellow Fever Risk Assessment Group (YF-RAG)</creatorcontrib><creatorcontrib>The Yellow Fever Risk Assessment Group (YF-RAG)</creatorcontrib><title>Assessment of yellow fever epidemic risk: an original multi-criteria modeling approach</title><title>PLoS neglected tropical diseases</title><addtitle>PLoS Negl Trop Dis</addtitle><description>Yellow fever (YF) virtually disappeared in francophone West African countries as a result of YF mass vaccination campaigns carried out between 1940 and 1953. However, because of the failure to continue mass vaccination campaigns, a resurgence of the deadly disease in many African countries began in the early 1980s. We developed an original modeling approach to assess YF epidemic risk (vulnerability) and to prioritize the populations to be vaccinated.
We chose a two-step assessment of vulnerability at district level consisting of a quantitative and qualitative assessment per country. Quantitative assessment starts with data collection on six risk factors: five risk factors associated with "exposure" to virus/vector and one with "susceptibility" of a district to YF epidemics. The multiple correspondence analysis (MCA) modeling method was specifically adapted to reduce the five exposure variables to one aggregated exposure indicator. Health districts were then projected onto a two-dimensional graph to define different levels of vulnerability. Districts are presented on risk maps for qualitative analysis in consensus groups, allowing the addition of factors, such as population migrations or vector density, that could not be included in MCA. The example of rural districts in Burkina Faso show five distinct clusters of risk profiles. Based on this assessment, 32 of 55 districts comprising over 7 million people were prioritized for preventive vaccination campaigns.
This assessment of yellow fever epidemic risk at the district level includes MCA modeling and consensus group modification. MCA provides a standardized way to reduce complexity. It supports an informed public health decision-making process that empowers local stakeholders through the consensus group. This original approach can be applied to any disease with documented risk factors.</description><subject>Burkina Faso - epidemiology</subject><subject>Data collection</subject><subject>Decision making</subject><subject>Disease prevention</subject><subject>Epidemics</subject><subject>Fever</subject><subject>Humans</subject><subject>Infectious Diseases</subject><subject>Infectious Diseases/Epidemiology and Control of Infectious Diseases</subject><subject>Infectious Diseases/Neglected Tropical Diseases</subject><subject>Infectious Diseases/Tropical and Travel-Associated Diseases</subject><subject>Infectious Diseases/Viral Infections</subject><subject>Mathematics</subject><subject>Mathematics/Mathematical Computing</subject><subject>Public health</subject><subject>Public Health and Epidemiology</subject><subject>Public Health and Epidemiology/Epidemiology</subject><subject>Public Health and Epidemiology/Immunization</subject><subject>Public Health and Epidemiology/Infectious Diseases</subject><subject>Risk Assessment - methods</subject><subject>Risk Factors</subject><subject>Tropical diseases</subject><subject>Vaccines</subject><subject>Yellow Fever - epidemiology</subject><subject>Yellow Fever - prevention & control</subject><issn>1935-2735</issn><issn>1935-2727</issn><issn>1935-2735</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>DOA</sourceid><recordid>eNp9ks1u1DAUhS0Eou3AGyCIhASrDP6JY18WSFVVoFIlNsDWcuybqYckDnamqG9PphOgRQhvbNnfPdc-PoQ8Y3TNhGJvtnGXBtutx2HyazqPSosH5JiBkCVXQj68sz4iJzlvKZUgNXtMjhhIULLSx-Trac6Yc4_DVMS2uMGuiz-KFq8xFTgGj31wRQr529vCDkVMYRPmpkW_66ZQuhQmTMEWffTYhWFT2HFM0bqrJ-RRa7uMT5d5Rb68P_989rG8_PTh4uz0snRS1lMJVitVO2c1tL6mlnPZQoNCAWegao7ON4hazjAgF8JpqjwICpo1FXVMrMiLg-7YxWwWS7JhXGsKUM8GrMjFgfDRbs2YQm_TjYk2mNuNmDbGpim4Do1n3IFTVrbcVhwQQHGPdSuUakTD6Kz1bum2a3r0bjYt2e6e6P2TIVyZTbw2XM2_U-8v83oRSPH7DvNk-pDd7LkdMO6yUaLitOK35Kv_kpzWALLagy__Av_tQnWgXIo5J2x_X5pRs4_Tryqzj5NZ4jSXPb_74D9FS37ET69_yQI</recordid><startdate>20090701</startdate><enddate>20090701</enddate><creator>Briand, Sylvie</creator><creator>Beresniak, Ariel</creator><creator>Nguyen, Tim</creator><creator>Yonli, Tajoua</creator><creator>Duru, Gerard</creator><creator>Kambire, Chantal</creator><creator>Perea, William</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QL</scope><scope>7SS</scope><scope>7T2</scope><scope>7T7</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8C1</scope><scope>8FD</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>H94</scope><scope>H95</scope><scope>H97</scope><scope>K9.</scope><scope>L.G</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20090701</creationdate><title>Assessment of yellow fever epidemic risk: an original multi-criteria modeling approach</title><author>Briand, Sylvie ; Beresniak, Ariel ; Nguyen, Tim ; Yonli, Tajoua ; Duru, Gerard ; Kambire, Chantal ; Perea, William</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c556t-9a8776cca89fd60a225f9be379219762ecdbee855569e233c807d930981b40c13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Burkina Faso - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PLoS neglected tropical diseases</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Briand, Sylvie</au><au>Beresniak, Ariel</au><au>Nguyen, Tim</au><au>Yonli, Tajoua</au><au>Duru, Gerard</au><au>Kambire, Chantal</au><au>Perea, William</au><au>Lopes da Fonseca, Benedito A.</au><aucorp>Yellow Fever Risk Assessment Group (YF-RAG)</aucorp><aucorp>The Yellow Fever Risk Assessment Group (YF-RAG)</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assessment of yellow fever epidemic risk: an original multi-criteria modeling approach</atitle><jtitle>PLoS neglected tropical diseases</jtitle><addtitle>PLoS Negl Trop Dis</addtitle><date>2009-07-01</date><risdate>2009</risdate><volume>3</volume><issue>7</issue><spage>e483</spage><epage>e483</epage><pages>e483-e483</pages><issn>1935-2735</issn><issn>1935-2727</issn><eissn>1935-2735</eissn><abstract>Yellow fever (YF) virtually disappeared in francophone West African countries as a result of YF mass vaccination campaigns carried out between 1940 and 1953. However, because of the failure to continue mass vaccination campaigns, a resurgence of the deadly disease in many African countries began in the early 1980s. We developed an original modeling approach to assess YF epidemic risk (vulnerability) and to prioritize the populations to be vaccinated.
We chose a two-step assessment of vulnerability at district level consisting of a quantitative and qualitative assessment per country. Quantitative assessment starts with data collection on six risk factors: five risk factors associated with "exposure" to virus/vector and one with "susceptibility" of a district to YF epidemics. The multiple correspondence analysis (MCA) modeling method was specifically adapted to reduce the five exposure variables to one aggregated exposure indicator. Health districts were then projected onto a two-dimensional graph to define different levels of vulnerability. Districts are presented on risk maps for qualitative analysis in consensus groups, allowing the addition of factors, such as population migrations or vector density, that could not be included in MCA. The example of rural districts in Burkina Faso show five distinct clusters of risk profiles. Based on this assessment, 32 of 55 districts comprising over 7 million people were prioritized for preventive vaccination campaigns.
This assessment of yellow fever epidemic risk at the district level includes MCA modeling and consensus group modification. MCA provides a standardized way to reduce complexity. It supports an informed public health decision-making process that empowers local stakeholders through the consensus group. This original approach can be applied to any disease with documented risk factors.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>19597548</pmid><doi>10.1371/journal.pntd.0000483</doi><oa>free_for_read</oa></addata></record> |
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subjects | Burkina Faso - epidemiology Data collection Decision making Disease prevention Epidemics Fever Humans Infectious Diseases Infectious Diseases/Epidemiology and Control of Infectious Diseases Infectious Diseases/Neglected Tropical Diseases Infectious Diseases/Tropical and Travel-Associated Diseases Infectious Diseases/Viral Infections Mathematics Mathematics/Mathematical Computing Public health Public Health and Epidemiology Public Health and Epidemiology/Epidemiology Public Health and Epidemiology/Immunization Public Health and Epidemiology/Infectious Diseases Risk Assessment - methods Risk Factors Tropical diseases Vaccines Yellow Fever - epidemiology Yellow Fever - prevention & control |
title | Assessment of yellow fever epidemic risk: an original multi-criteria modeling approach |
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