AN EPIDEMIOLOGICAL SITUATION OF CATTLE’S CRYPTOSPORIDIOSIS IN CENTRAL KAZAKHSTAN (AKMOLA OBLAST)

Authors

  • A.E Ussenbayev. , D.T.Kurenkeyeva, А.А. Zhanabayev, L.А Lider., R. Bissengaliyev.

Keywords:

Cryptosporidiosis, cattle, modelling, epidemiology, maximum likelihoodestimation, Bayesian statistics.

Abstract

In the modern world cryptosporidiosis is a serious human and animal health problem, presenting an epidemiological threat. This infection causes significant economic losses for breeding enterprises and livestock production farms.However, in Central Asia research of the parasitosis in livestock have not been carried out to date. This work examines the prevalence of infection of Cryptosporidium spp. of cattle in Central Kazakhstan. The research was carried out at 39 agricultural enterprises in five administrative regions of Akmola Oblast. Here 589 calves of Kazakh Whitehead, Holstein-Friesian, Aberdeen-Angus and outbreed calves aged from one to 12 months have been studied for cryptosporidiosis. Faecal specimens were investigated by Hein (1982). Bayesian inference of observed data was studied with maximum likelihood techniques to define of their parameters and
confidences. Binomial distribution was chosen for likelihood to get a conjugate beta-posterior. A computer package R was used to numerically find the values forthe mean rates, credible intervals. There were infected 26 of investigated animals and maximum likelihood estimation was 0.044. The 95% confidence interval for prevalence probability was [0.048, 0.082]. There were simulated a posterior distributions in females and males for 100,000 random samples. These computer experiments demonstrated that the sex of animals did not affect the infection with cryptosporidiosis.In common with other similar studies, digital simulations indicated significantly that the first month live calves were infected in high mean prevalence rate than animals of older groups. Obtained data would be useful to develop simulation models for the control of cryptosporidiosis in cattle.

Published

2021-07-03