Show simple item record

dc.contributor.authorWhiteman, Ari
dc.contributor.authorDesjardins, Michael R.
dc.contributor.authorEskildsen, Gilberto A.
dc.contributor.authorLoaiza, Jose R.
dc.date.accessioned2020-06-30T06:13:00Z
dc.date.available2020-06-30T06:13:00Z
dc.date.issued2019-09-23
dc.identifier.otherhttps://doi.org/10.1371/journal.pntd.0007266
dc.identifier.urihttp://repositorio-indicasat.org.pa/handle/123456789/143
dc.descriptionLong term surveillance of vectors and arboviruses is an integral aspect of disease prevention and control systems in countries affected by increasing risk. Yet, little effort has been made to adjust space-time risk estimation by integrating disease case counts with vector surveillance data, which may result in inaccurate risk projection when several vector species are present, and when little is known about their likely role in local transmission. Here, we integrate 13 years of dengue case surveillance and associated Aedes occurrence data across 462 localities in 63 districts to estimate the risk of infection in the Republic of Panama. Our exploratory space-time modelling approach detected the presence of five clusters, which varied by duration, relative risk, and spatial extent after incorporating vector species as covariates. The Ae. aegypti model contained the highest number of districts with more dengue cases than would be expected given baseline population levels, followed by the model accounting for both Ae. aegypti and Ae. albopictus. This implies that arbovirus case surveillance coupled with entomological surveillance can affect cluster detection and risk estimation, potentially improving efforts to understand outbreak dynamics at national scales.en_US
dc.description.abstractLong term surveillance of vectors and arboviruses is an integral aspect of disease prevention and control systems in countries affected by increasing risk. Yet, little effort has been made to adjust space-time risk estimation by integrating disease case counts with vector surveillance data, which may result in inaccurate risk projection when several vector species are present, and when little is known about their likely role in local transmission. Here, we integrate 13 years of dengue case surveillance and associated Aedes occurrence data across 462 localities in 63 districts to estimate the risk of infection in the Republic of Panama. Our exploratory space-time modelling approach detected the presence of five clusters, which varied by duration, relative risk, and spatial extent after incorporating vector species as covariates. The Ae. aegypti model contained the highest number of districts with more dengue cases than would be expected given baseline population levels, followed by the model accounting for both Ae. aegypti and Ae. albopictus. This implies that arbovirus case surveillance coupled with entomological surveillance can affect cluster detection and risk estimation, potentially improving efforts to understand outbreak dynamics at national scales.en_US
dc.language.isoengen_US
dc.rightshttps://creativecommons.org/licenses/by/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectclustersen_US
dc.subjectdengueen_US
dc.subjectPanamaen_US
dc.subjectvector surveillance dataen_US
dc.titleDetecting space-time clusters of dengue fever in Panama after adjusting for vector surveillance dataen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.typeinfo:eu-repo/semantics/publishedVersion


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record