Abstract:
The paper is the first to use principal component analysis (PCA) in veterinary
epidemiology and aims to identify the most significant agents of diarrhoea in
calves. Data were collected from 245 calves on 32 farms within 13 districts of
Northern Kazakhstan. Epidemiological data were simulated in R, using
standard PCA modules. The data clustering was carried out based on the
prevalence of seven enteropathogens for an age group dataset (represented by
four animal groups) and a farm type dataset (by three farm types depending
on herd size). The simulation revealed that the components identified here for
one and two-week calves explained 91.31% of the variance. The first two
components of large and middle-sized farms covered 90.3% of the variance.
The coordinates corresponding to pathogens were approximately visualised in
directions of eigenvectors for each age group and farm type. The coordinate
for Cryptosporidium parvum was in directions of eigenvectors for one-to
three-week calves and large farms. The coordinate for rotaviruses was in the
direction of eigenvectors for four-week calves and medium-sized and small
farms. So, PCA can potentially be useful in the clustering of epidemiological
datasets and making decisions on control of infectious diseases with a multipathogenic nature.
