Supporting information from Complex responses to movement-based disease control: when livestock trading helps

Livestock disease controls are often linked to movements between farms, for example, via quarantine and pre- or post-movement testing. Designing effective controls, therefore, benefits from accurate assessment of herd-to-herd transmission. Household models of human infections make use of <i>R</i>_*, the number of groups infected by an initial infected group, which is a metapopulation level analogue of the basic reproduction number <i>R</i>₀ that provides a better characterization of disease spread in a metapopulation. However, existing approaches to calculate <i>R</i>_* do not account for individual movements between locations which means we lack suitable tools for livestock systems. We address this gap using next-generation matrix approaches to capture movements explicitly and introduce novel tools to calculate <i>R</i>_* in any populations coupled by individual movements. We show that depletion of infectives in the source group, which hastens its recovery, is a phenomenon with important implications for design and efficacy of movement-based controls. Underpinning our results is the observation that <i>R</i>_* peaks at intermediate livestock movement rates. Consequently, under movement-based controls, infection could be controlled at high movement rates but persist at intermediate rates. Thus, once control schemes are present in a livestock system, a reduction in movements can counterintuitively lead to increased disease prevalence. We illustrate our results using four important livestock diseases (bovine viral diarrhoea, bovine herpes, Johne's disease and <i>Escherichia coli</i> O157) that each persist across different movement rate ranges with the consequence that a change in livestock movements could help control one disease, but exacerbate another

Details of mouse monoclonal antibodies (mAb) used for flow cytometry.

1<p>AbD Serotec, Kidlington, UK;</p>2<p>VMRD Inc., Pullman, WA;</p>3<p>eBioscience, San Diego, CA;</p>4<p>Moredun Research Institute; n/a = not applicable.</p

Health event traits: descriptive statistics.

<p>Frequency refers to the proportion of cows with a condition on the week of the immunological analysis; mean, standard deviation and maximum refer to number of distinct episodes per lactation.</p

Immune traits: descriptive statistics, estimates of variance due to the animal effect and proportion of the total phenotypic variance due to the animal effect (between animal repeatability estimates).

1<p>Coefficient of Variation;</p>2<p>Between animal repeatability of the trait;</p>3<p>Natural Antibodies;</p>4<p>Tumour Necrosis Factor-α;</p>5<p>%Peripheral Blood Mononuclear Cells;</p>6%<p>of PBMC that were CD3, CD4, CD8, CD14, CD21, CD335 and γδ TCR positive;</p>7%<p>of total leukocytes that were lymphocytes, monocytes, neutrophils or eosinophils.</p>*<p>Significantly greater than 0 estimates (P<0.05).</p

Estimated intra-assay repeatability and inter-assay precision for cellular immune trait measurements.

1<p>Coefficient of Variation;</p>2<p>Peripheral Blood Mononuclear Cells;</p>3<p>% of PBMC that were CD3, CD4, CD8, CD14, CD21, CD335 and γδ TCR positive;</p>4<p>% of total leukocytes that were lymphocytes, monocytes, neutrophils or eosinophils.</p

Lactation traits: descriptive statistics, estimates of variance due to the animal effect and proportion of the total phenotypic variance due to the animal effect (between animal repeatability estimates); data were weekly averaged daily records.

1<p>Coefficient of Variation;</p>2<p>Between animal repeatability of the trait;</p>*<p>Significantly greater than 0 estimates (P<0.05).</p

Effect of genetic and diet group on serological immune traits.

<p>Levels of natural antibodies (A), TNFα (B) and haptoglobin (C) in the serum of cows from control or select genetic groups and high concentrate (High Conc.) or low concentrate (Low Conc) diet groups. Data represents values recorded over the whole 8 month study period.</p

Effect genetic group on cellular immune traits.

<p>Differential leukocyte counts (A), CD4:CD8 ratios (B) and PBMC leukocyte subpopulations (C) in cows from either control (C) or select (S) genetic groups. Data represents values recorded over the whole 8 month study period.</p

Statistically significant (<i>P</i><0.05) post Bonferroni correction animal and phenotypic correlations between immune, health event, reproductive performance and lactation traits.

1<p>expressed as 0/1 on the week of the immune analysis;</p>2<p>expressed as number of distinct episodes in a lactation;</p>3<p>% of PBMC that were CD335, CD8, CD4, CD3, CD14 or γδ TCR positive;</p>4<p>% of total leukocytes that were PBMC or lymphocytes;</p>5<p>Natural Antibodies.</p>a<p>phenotypic correlations between immune and health events traits;</p>b<p>phenotypic correlations between immune and reproductive traits;</p>c<p>animal correlations between immune and lactation traits measured on the same week; ^danimal correlations between immune and lactation traits measured throughout lactation; ^ephenotypic correlations between immune profile and lactation traits measured on the same week;</p>f<p>phenotypic correlations between immune and lactation traits measured throughout lactation.</p

Nussey 2014 - ProcRSocB - Dataframe+Readme

Dataframe used in analyses and readme file in Excel forma