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Review
. 2021 Apr 3;11(4):1014.
doi: 10.3390/ani11041014.

Prevalence of Worldwide Neonatal Calf Diarrhoea Caused by Bovine Rotavirus in Combination with Bovine Coronavirus, Escherichia coli K99 and Cryptosporidium spp.: A Meta-Analysis

Michael Brunauer  1 , Franz-Ferdinand Roch  1 , Beate Conrady  1   2   3
Affiliations
Review

Prevalence of Worldwide Neonatal Calf Diarrhoea Caused by Bovine Rotavirus in Combination with Bovine Coronavirus, Escherichia coli K99 and Cryptosporidium spp.: A Meta-Analysis

Michael Brunauer et al. Animals (Basel). .

Abstract

Multiple enteropathogens such as bovine rotavirus (BRV), bovine coronavirus (BCoV), Escherichia coli K99 (ETEC) and Cryptosporidium spp. (Crypto) are the most common causes of calf diarrhoea during the first 30 days of animal age. Three weighted-stratified random-effects meta-analyses were performed to calculate the worldwide prevalence of mixed infections of the causative agents (i.e., BRV-BCoV, BRV-ETEC, BRV-Crypto) and their potential influencing factors. The meta-analysis covered 41 studies (94 sub-studies) in 21 countries that determined the presence or absence of mixed infections in global calf populations. The highest worldwide estimated pooled prevalence was identified for BRV-Crypto (6.69%), followed by BRV-BCoV (2.84%), and BRV-ETEC (1.64%). The chance of detecting BCoV in calves with diarrhoea was 1.83 higher in the presence of BRV compared to calves without BRV, whereby an inhibition effect (odds ratio: 0.77) was determined between BRV and Crypto infections. The diagnostic methods were identified as a significant influencing factor in the detection of all considered mixed infections, while the other analysed factors differed in relation to their effect on prevalence. In contrast to BRV-BCoV, the prevalence of BRV-ETEC and BRV-Crypto mixed infections followed the course of individual ETEC and Crypto prevalence related to the age class of the sampled animals.

Keywords: Cryptosporidium spp.; Escherichia coli K99; bovine coronavirus; bovine rotavirus; concurrent-infection; epidemiology; mixed-infection; pathogens; systematic review.

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Conflict of interest statement

The authors declare no conflict of interest. None of the authors of this paper has a financial or personal relationship with other people or organisations that could inappropriately influence or bias the content of the paper.

Figures

Figure 1
Figure 1
Flow chart of studies incorporated in the systematic review and meta-analysis.
Figure 2
Figure 2
Temporal analysis of the individual (a) grey: bovine rotavirus; (b) orange: bovine coronavirus; (c) green: Escherichia coli K99; purple: Cryptosporidium spp.) and mixed prevalences stratified by age class of sampled animals until 30 days. The lines represent the mean prevalence estimates of all considered studies with the corresponding 95% CI (area) and individual prevalence points of studies (dots) during the period observed. The more prevalence estimates available at a certain age class of sampled animals, the wider the dots. N.B. To avoid imprecise model predictions, studies in the age groups (28–49 days) were excluded from the temporal curve fitting due to the small number of available studies (see number of available studies in Table 2, Table 3 and Table 4).
Figure 3
Figure 3
Comparison of the detected prevalence of mixed infections (dots) and the expected prevalence of infections (arrowheads) in calves with diarrhoea under the assumption of independency between both pathogens (e.g., P (BRV ∩ BCoV) = P(BRV) × P(BCoV)). Dots with blue arrows represent data with a lower prevalence than we would expect in an independent co-infection, while dots with red arrows represent data with a higher prevalence, as expected.
See this image and copyright information in PMC

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