Using paired serology and surveillance data to quantify dengue transmission and control during a large outbreak in Fiji
Dengue is a major health burden, but it can be challenging to examine transmission and evaluate control measures because outbreaks depend on multiple factors, including human population structure, prior immunity and climate. We combined population-representative paired sera collected before and afte...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2018-08-01
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| Series: | eLife |
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| Online Access: | https://elifesciences.org/articles/34848 |
| _version_ | 1811253201950736384 |
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| author | Adam J Kucharski Mike Kama Conall H Watson Maite Aubry Sebastian Funk Alasdair D Henderson Oliver J Brady Jessica Vanhomwegen Jean-Claude Manuguerra Colleen L Lau W John Edmunds John Aaskov Eric James Nilles Van-Mai Cao-Lormeau Stéphane Hué Martin L Hibberd |
| author_facet | Adam J Kucharski Mike Kama Conall H Watson Maite Aubry Sebastian Funk Alasdair D Henderson Oliver J Brady Jessica Vanhomwegen Jean-Claude Manuguerra Colleen L Lau W John Edmunds John Aaskov Eric James Nilles Van-Mai Cao-Lormeau Stéphane Hué Martin L Hibberd |
| author_sort | Adam J Kucharski |
| collection | DOAJ |
| description | Dengue is a major health burden, but it can be challenging to examine transmission and evaluate control measures because outbreaks depend on multiple factors, including human population structure, prior immunity and climate. We combined population-representative paired sera collected before and after the 2013/14 dengue-3 outbreak in Fiji with surveillance data to determine how such factors influence transmission and control in island settings. Our results suggested the 10–19 year-old age group had the highest risk of infection, but we did not find strong evidence that other demographic or environmental risk factors were linked to seroconversion. A mathematical model jointly fitted to surveillance and serological data suggested that herd immunity and seasonally varying transmission could not explain observed dynamics. However, the model showed evidence of an additional reduction in transmission coinciding with a vector clean-up campaign, which may have contributed to the decline in cases in the later stages of the outbreak. |
| first_indexed | 2024-04-12T16:47:21Z |
| format | Article |
| id | doaj.art-b97f49865fa845629dbe4c11ebb28480 |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-12T16:47:21Z |
| publishDate | 2018-08-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-b97f49865fa845629dbe4c11ebb284802022-12-22T03:24:32ZengeLife Sciences Publications LtdeLife2050-084X2018-08-01710.7554/eLife.34848Using paired serology and surveillance data to quantify dengue transmission and control during a large outbreak in FijiAdam J Kucharski0https://orcid.org/0000-0001-8814-9421Mike Kama1Conall H Watson2Maite Aubry3Sebastian Funk4https://orcid.org/0000-0002-2842-3406Alasdair D Henderson5Oliver J Brady6Jessica Vanhomwegen7Jean-Claude Manuguerra8https://orcid.org/0000-0002-5202-6531Colleen L Lau9W John Edmunds10John Aaskov11Eric James Nilles12https://orcid.org/0000-0001-7044-5257Van-Mai Cao-Lormeau13Stéphane Hué14Martin L Hibberd15Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United KingdomNational Centre for Communicable Disease Control, Suva, Fiji; University of the South Pacific, Suva, FijiCentre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United KingdomUnit of Emerging Infectious Diseases, Institut Louis Malardé, Tahiti, French PolynesiaCentre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United KingdomCentre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United KingdomCentre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United KingdomInstitut Pasteur, Paris, FranceInstitut Pasteur, Paris, FranceResearch School of Population Health, Australian National University, Canberra, AustraliaCentre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United KingdomQueensland University of Technology, Brisbane, AustraliaWorld Health Organization Division of Pacific Technical Support, Suva, FijiUnit of Emerging Infectious Diseases, Institut Louis Malardé, Tahiti, French PolynesiaCentre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United KingdomDepartment of Pathogen Molecular Biology, London School of Hygiene and Tropical Medicine, London, United KingdomDengue is a major health burden, but it can be challenging to examine transmission and evaluate control measures because outbreaks depend on multiple factors, including human population structure, prior immunity and climate. We combined population-representative paired sera collected before and after the 2013/14 dengue-3 outbreak in Fiji with surveillance data to determine how such factors influence transmission and control in island settings. Our results suggested the 10–19 year-old age group had the highest risk of infection, but we did not find strong evidence that other demographic or environmental risk factors were linked to seroconversion. A mathematical model jointly fitted to surveillance and serological data suggested that herd immunity and seasonally varying transmission could not explain observed dynamics. However, the model showed evidence of an additional reduction in transmission coinciding with a vector clean-up campaign, which may have contributed to the decline in cases in the later stages of the outbreak.https://elifesciences.org/articles/34848dengue virustransmission modelseroepidemiology |
| spellingShingle | Adam J Kucharski Mike Kama Conall H Watson Maite Aubry Sebastian Funk Alasdair D Henderson Oliver J Brady Jessica Vanhomwegen Jean-Claude Manuguerra Colleen L Lau W John Edmunds John Aaskov Eric James Nilles Van-Mai Cao-Lormeau Stéphane Hué Martin L Hibberd Using paired serology and surveillance data to quantify dengue transmission and control during a large outbreak in Fiji eLife dengue virus transmission model seroepidemiology |
| title | Using paired serology and surveillance data to quantify dengue transmission and control during a large outbreak in Fiji |
| title_full | Using paired serology and surveillance data to quantify dengue transmission and control during a large outbreak in Fiji |
| title_fullStr | Using paired serology and surveillance data to quantify dengue transmission and control during a large outbreak in Fiji |
| title_full_unstemmed | Using paired serology and surveillance data to quantify dengue transmission and control during a large outbreak in Fiji |
| title_short | Using paired serology and surveillance data to quantify dengue transmission and control during a large outbreak in Fiji |
| title_sort | using paired serology and surveillance data to quantify dengue transmission and control during a large outbreak in fiji |
| topic | dengue virus transmission model seroepidemiology |
| url | https://elifesciences.org/articles/34848 |
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