Open AccessPredictive risk model of livestock rabies occurrence in Peru
Author(s) -
Ricardo Augusto Dias,
Francisco Miroslav Ulloa-Stanojlović
Publication year - 2021
Publication title -
brazilian journal of veterinary research and animal science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.187
H-Index - 17
eISSN - 1678-4456
pISSN - 1413-9596
DOI - 10.11606/issn.1678-4456.bjvras.2021.183270
Subject(s) - livestock , rabies , geography , transmission (telecommunications) , vulnerability (computing) , environmental health , veterinary medicine , biology , medicine , virology , forestry , computer science , telecommunications , computer security
Livestock rabies is endemic in Peru. Hence, its persistence and annual dissemination represent an important economic impact, especially for impoverished farming communities. The disease is mostly transmitted by the hematophagous bat Desmodus rotundus. The present study aimed to adapt an existing predictive model of the occurrence of livestock rabies to Peru, in which the risk of rabies transmission from bats to livestock was estimated using decision-tree models of receptivity and vulnerability. Official rabies surveillance data between 2010 and 2015 were used along with possible risk factors, such as livestock biomass, environmental changes, and geomorphological characteristics. Several scenarios were established to evaluate the prediction of the occurrence of livestock rabies cases by determining more than one cut-off point of the receptivity variables. During the study period, the precision of the model was estimated through the sensitivity (39.46%) and specificity (98.64%) by using confusion matrices. Targeting control efforts, especially in districts with a high estimated risk, could represent the prevention of a significant proportion of livestock rabies cases, which would optimize the human and economic resources of the Peruvian surveillance service. However, the quality of data produced by the surveillance should be improved not only to obtain higher model precision but also to allow the adequate planning of control actions.