6533b85afe1ef96bd12b8d7c
RESEARCH PRODUCT
Integration of animal health and public health surveillance sources to exhaustively inform the risk of zoonosis: An application to echinococcosis in Rio Negro, Argentina
Raymond BoazMarcos ArezoAndrew B. LawsonVictor J. Del Rio VilasEdmundo LarrieuMarco Antonio Natal VigilatoAna Corberán-valletsubject
0301 basic medicineEpidemiologyRC955-962Animal DiseasesBayes' theoremMedical Conditions0302 clinical medicinePublic health surveillanceZoonosesArctic medicine. Tropical medicineEpidemiologyMedicine and Health SciencesPublic Health SurveillanceDog DiseasesChildEchinococcus granulosusMammalsCiencias Médicas y de la SaludDisease surveillanceSurveillancebiologyZoonosisEukaryotaEchinococcosisInfectious DiseasesGeographyHelminth InfectionsVertebratesPublic aspects of medicineRA1-1270Research ArticleNeglected Tropical Diseasesmedicine.medical_specialtyInfectious Disease ControlAdolescent030231 tropical medicineArgentinaDisease SurveillanceModels Biological03 medical and health sciencesDogsEchinococcosisEnvironmental healthControlParasitic DiseasesmedicineAnimalsHumansEchinococcus granulosusOrganismsPublic Health Environmental and Occupational HealthBiology and Life SciencesBayes TheoremTropical Diseasesmedicine.diseasebiology.organism_classification030104 developmental biologyEchinococosisMedical Risk FactorsInfectious Disease SurveillanceData qualityAmniotesZoologydescription
The analysis of zoonotic disease risk requires the consideration of both human and animal geo-referenced disease incidence data. Here we show an application of joint Bayesian analyses to the study of echinococcosis granulosus (EG) in the province of Rio Negro, Argentina. We focus on merging passive and active surveillance data sources of animal and human EG cases using joint Bayesian spatial and spatio-temporal models. While similar spatial clustering and temporal trending was apparent, there appears to be limited lagged dependence between animal and human outcomes. Beyond the data quality issues relating to missingness at different times, we were able to identify relations between dog and human data and the highest ‘at risk’ areas for echinococcosis within the province.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-08-01 |