Latent-class analysis-derived brucellosis prevalence estimates by production system (pastoral and non-pastoral) and species (cattle, sheep, and goats).
<p>White dots indicate the median, black diamonds the mean, black boxes the 50% credible interval, and black lines the 95% credible interval. ‘n’ indicates the number of animals sampled and tested for brucellosis with both the Rose Bengal test (RBT) and competitive enzyme-linked immunosorbent...
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2025
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| _version_ | 1852022690841362432 |
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| author | Ângelo J. F. Mendes (20727333) |
| author2 | Daniel T. Haydon (7499906) William A. de Glanville (7431773) Rebecca F. Bodenham (11326851) AbdulHamid S. Lukambagire (8824208) Paul C. D. Johnson (9543637) Gabriel M. Shirima (7251362) Sarah Cleaveland (83792) Emma McIntosh (787413) Nick Hanley (591087) Jo E. B. Halliday (7499909) |
| author2_role | author author author author author author author author author author |
| author_facet | Ângelo J. F. Mendes (20727333) Daniel T. Haydon (7499906) William A. de Glanville (7431773) Rebecca F. Bodenham (11326851) AbdulHamid S. Lukambagire (8824208) Paul C. D. Johnson (9543637) Gabriel M. Shirima (7251362) Sarah Cleaveland (83792) Emma McIntosh (787413) Nick Hanley (591087) Jo E. B. Halliday (7499909) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ângelo J. F. Mendes (20727333) Daniel T. Haydon (7499906) William A. de Glanville (7431773) Rebecca F. Bodenham (11326851) AbdulHamid S. Lukambagire (8824208) Paul C. D. Johnson (9543637) Gabriel M. Shirima (7251362) Sarah Cleaveland (83792) Emma McIntosh (787413) Nick Hanley (591087) Jo E. B. Halliday (7499909) |
| dc.date.none.fl_str_mv | 2025-02-14T18:37:41Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pntd.0012814.g003 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Latent-class_analysis-derived_brucellosis_prevalence_estimates_by_production_system_pastoral_and_non-pastoral_and_species_cattle_sheep_and_goats_/28420497 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Medicine Biotechnology Ecology Cancer Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified per infected animal guide policy development economic modelling framework 2 – 211 2 – 17 economic impacts caused class analysis model 4 – 23 1 – 1 8 – 228 8 – 17 2 – 1 annual losses attributable 7 – 25 1 – 3 1 – 8 4 – 6 2 – 3 6 ), 1 div >< p 3 – 4 6 ), 6 9 ), 1 7 ), 9 pastoral production systems level brucellosis prevalence 8 ), level losses wide impacts pastoral systems growth model estimated losses livestock production 0 ), widespread distribution using tanzania uncertainty interval surveys conducted resource settings potential interventions many low international dollars inform cost human health derived income clustering algorithm classify households central tanzania brucella </ benefit analyses bayesian latent 541 households 384 classified |
| dc.title.none.fl_str_mv | Latent-class analysis-derived brucellosis prevalence estimates by production system (pastoral and non-pastoral) and species (cattle, sheep, and goats). |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>White dots indicate the median, black diamonds the mean, black boxes the 50% credible interval, and black lines the 95% credible interval. ‘n’ indicates the number of animals sampled and tested for brucellosis with both the Rose Bengal test (RBT) and competitive enzyme-linked immunosorbent assay (cELISA).</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_fa6956bb2c4e74f3efbc95ef07ec6df0 |
| identifier_str_mv | 10.1371/journal.pntd.0012814.g003 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28420497 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Latent-class analysis-derived brucellosis prevalence estimates by production system (pastoral and non-pastoral) and species (cattle, sheep, and goats).Ângelo J. F. Mendes (20727333)Daniel T. Haydon (7499906)William A. de Glanville (7431773)Rebecca F. Bodenham (11326851)AbdulHamid S. Lukambagire (8824208)Paul C. D. Johnson (9543637)Gabriel M. Shirima (7251362)Sarah Cleaveland (83792)Emma McIntosh (787413)Nick Hanley (591087)Jo E. B. Halliday (7499909)MedicineBiotechnologyEcologyCancerScience PolicyEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedper infected animalguide policy developmenteconomic modelling framework2 – 2112 – 17economic impacts causedclass analysis model4 – 231 – 18 – 2288 – 172 – 1annual losses attributable7 – 251 – 31 – 84 – 62 – 36 ), 1div >< p3 – 46 ), 69 ), 17 ), 9pastoral production systemslevel brucellosis prevalence8 ),level losseswide impactspastoral systemsgrowth modelestimated losseslivestock production0 ),widespread distributionusing tanzaniauncertainty intervalsurveys conductedresource settingspotential interventionsmany lowinternational dollarsinform costhuman healthderived incomeclustering algorithmclassify householdscentral tanzaniabrucella </benefit analysesbayesian latent541 households384 classified<p>White dots indicate the median, black diamonds the mean, black boxes the 50% credible interval, and black lines the 95% credible interval. ‘n’ indicates the number of animals sampled and tested for brucellosis with both the Rose Bengal test (RBT) and competitive enzyme-linked immunosorbent assay (cELISA).</p>2025-02-14T18:37:41ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pntd.0012814.g003https://figshare.com/articles/figure/Latent-class_analysis-derived_brucellosis_prevalence_estimates_by_production_system_pastoral_and_non-pastoral_and_species_cattle_sheep_and_goats_/28420497CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/284204972025-02-14T18:37:41Z |
| spellingShingle | Latent-class analysis-derived brucellosis prevalence estimates by production system (pastoral and non-pastoral) and species (cattle, sheep, and goats). Ângelo J. F. Mendes (20727333) Medicine Biotechnology Ecology Cancer Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified per infected animal guide policy development economic modelling framework 2 – 211 2 – 17 economic impacts caused class analysis model 4 – 23 1 – 1 8 – 228 8 – 17 2 – 1 annual losses attributable 7 – 25 1 – 3 1 – 8 4 – 6 2 – 3 6 ), 1 div >< p 3 – 4 6 ), 6 9 ), 1 7 ), 9 pastoral production systems level brucellosis prevalence 8 ), level losses wide impacts pastoral systems growth model estimated losses livestock production 0 ), widespread distribution using tanzania uncertainty interval surveys conducted resource settings potential interventions many low international dollars inform cost human health derived income clustering algorithm classify households central tanzania brucella </ benefit analyses bayesian latent 541 households 384 classified |
| status_str | publishedVersion |
| title | Latent-class analysis-derived brucellosis prevalence estimates by production system (pastoral and non-pastoral) and species (cattle, sheep, and goats). |
| title_full | Latent-class analysis-derived brucellosis prevalence estimates by production system (pastoral and non-pastoral) and species (cattle, sheep, and goats). |
| title_fullStr | Latent-class analysis-derived brucellosis prevalence estimates by production system (pastoral and non-pastoral) and species (cattle, sheep, and goats). |
| title_full_unstemmed | Latent-class analysis-derived brucellosis prevalence estimates by production system (pastoral and non-pastoral) and species (cattle, sheep, and goats). |
| title_short | Latent-class analysis-derived brucellosis prevalence estimates by production system (pastoral and non-pastoral) and species (cattle, sheep, and goats). |
| title_sort | Latent-class analysis-derived brucellosis prevalence estimates by production system (pastoral and non-pastoral) and species (cattle, sheep, and goats). |
| topic | Medicine Biotechnology Ecology Cancer Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified per infected animal guide policy development economic modelling framework 2 – 211 2 – 17 economic impacts caused class analysis model 4 – 23 1 – 1 8 – 228 8 – 17 2 – 1 annual losses attributable 7 – 25 1 – 3 1 – 8 4 – 6 2 – 3 6 ), 1 div >< p 3 – 4 6 ), 6 9 ), 1 7 ), 9 pastoral production systems level brucellosis prevalence 8 ), level losses wide impacts pastoral systems growth model estimated losses livestock production 0 ), widespread distribution using tanzania uncertainty interval surveys conducted resource settings potential interventions many low international dollars inform cost human health derived income clustering algorithm classify households central tanzania brucella </ benefit analyses bayesian latent 541 households 384 classified |