Literature DB >> 32143028

Use of a generalized additive model for a spatial analysis of bovine brucellosis risk in the state of Mato Grosso in 2002 and 2014.

Isana Souza Silva1, Janice Elena Ioris Barddal2, Rísia Lopes Negreiros3, A C S Oliveira4, D M Aguiar5.   

Abstract

Diseases that affect cattle represent obstacles to the development of livestock activity. Brucellosis is a significant such disease because it is transmissible, has a chronic nature, and causes health and economic damages to the herd and rural producer. Data from surveys performed in 2002 and 2014 were compared to identify the spatial distribution of bovine brucellosis and to evaluate clusters of outbreaks and areas of greater risk to have infected cattle in the state of Mato Grosso, Brazil. The present study analyzed the data obtained in the aforementioned investigations with a statistical model based on a spatial point process called a generalized additive model (GAM). The analysis made it possible to identify the regions of highest and lowest risk in the state of Mato Grosso. Of the 1001 properties analyzed in 2002, 198 were in areas with high-odds ratio, and 121 were in a low-odds ratio area. Of the 1248 properties sampled in 2014, 119 were in a high-odds ratio area, and 162 were in a low-odds ratio area. Areas with high-odds ratio are more likely to have infected cattle and can be considered to be at higher risk for the disease. The results of the present study highlight the reduction in foci, prevalence, and its relationship with the spatial distribution of bovine brucellosis. The study results should help the official defense service of Mato Grosso direct its activities according to the profile of each region.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Brazil; Brucella abortus; Generalized additive model; Outbreak

Year:  2020        PMID: 32143028     DOI: 10.1016/j.prevetmed.2020.104938

Source DB:  PubMed          Journal:  Prev Vet Med        ISSN: 0167-5877            Impact factor:   2.670


  1 in total

1.  Predicting the Spatial-Temporal Distribution of Human Brucellosis in Europe Based on Convolutional Long Short-Term Memory Network.

Authors:  Li Shen; Chenghao Jiang; Minghao Sun; Xuan Qiu; Jiaqi Qian; Shuxuan Song; Qingwu Hu; Heilili Yelixiati; Kun Liu
Journal:  Can J Infect Dis Med Microbiol       Date:  2022-08-03       Impact factor: 2.585

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.