Babesiosis (also known as tick fever or cattle fever) is caused by intraerythrocytic protozoan parasites of the genus Babesia that infect a wide range of domestic and wild animals, and occasionally humans. The disease is tick-transmitted and distributed worldwide. Economically, tick fever is the most important arthropod-borne disease of cattle, with vast areas of Australia, Africa, South and Central America and the United States continuously under threat. Tick fever was the first disease for which transmission by an arthropod to a mammal was implicated at the turn of the twentieth century, and is the first disease to be eradicated from a continent (North America). This review describes the biology of Babesia spp. in the host and the tick, the scale of the problem to the cattle industry, the various components of control programmes, epidemiology, pathogenesis, immunity, vaccination and future research. The emphasis is on Babesia bovis and Babesia bigemina, the two most important species infecting cattle.
Babes (1888) investigated disease outbreaks causing haemoglobinuria in cattle in Romania in 1888 and was the first to describe piroplasms in the blood of cattle. He believed it to be a bacterium and named it Haematococcus bovis (Angus, 1996). Shortly afterwards investigations by Smith and Kilborne (1893) in the United States of America demonstrated the causative organism of ‘Texas Fever’ (babesiosis), which they called Pyrosoma bigeminum (= Babesia bigemina).
Longitudinal measurements of human growth present special difficulties for statistical analysis, both in the fitting of parametric models for individual growth and when comparing growth in various populations. The main objective of parametric analysis is to describe or predict growth, or differences in growth, as a function of chronological age. Although it would be convenient in this connection to assume complete records of measurements at the same time points for all cases, that condition is almost impossible to fulfil even in carefully conducted longitudinal studies. For this reason, conventional multivariate statistical methods that assume measurement records of fixed dimensionality do not apply.
Further difficulties arise from the fact that growth is not a simple deterministic process that can be represented by a continuous function of time with a manageable number of parameters. Even well-fitting functional models will exhibit residual variation attributable to so-called ‘equation error’. To the extent that the residuals are unbiased (i.e. tend to sum to zero), the fitted growth curve will pass through the data points in such a way that the points in successive intervals will lie on one side of the curve and then on the other. This introduces autocorrelation among the residuals and violates the assumption of independent error required in most curve-fitting procedures – although it does not bias the shape of curves appreciably if the growth measurements are regularly spaced in time.
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