Social contact matrices.

<p>Values and colours show the mean number of contacts per day reported between each age group. In each panel, the participant's age group is shown on the vertical axis, that of their contacts on the horizontal axis. The four panels show patterns of A: conversational contacts during school term time; B: conversational contacts during school holidays; C: physical contacts during school term time; D: physical contacts during school holidays.</p

The impact of school holidays on epidemic growth rate.

<p>The impact of school holidays and prior immunity on initial epidemic growth rate predicted using the best-fitting model (using patterns of conversational contacts fitted to HPA incidence estimates) considering an epidemic that began during term time or during the school holidays, with and without measured levels of prior immunity. Comparable results from the other models can be found in Table S5 in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002425#pcbi.1002425.s004" target="_blank">Text S1</a>. Lines show the range of model predictions using the low-difference and high-difference bootstrapped contact matrices.</p

Incidence within younger age groups, over time.

<p>The fraction of incidence each week that occurs in younger people, as predicted using the best-fitting model (using patterns of conversational contacts fitted to HPA incidence estimates) and as reported in the HPA incidence estimate data. Incidence data showing the proportion of incidence in those aged under 25 (black, dashed) and under 15 (black, dash-dotted); model predicted fraction of incidence in those aged under 19 is shown in red; model predictions using the low-difference and high-difference bootstrapped contact matrices are shown in green and blue respectively.</p

Incidence estimates, comparing models and data.

<p>A comparison of estimated per-capita weekly incidence data (black) and best-fitting model output (red). The four panels show A: model using patterns of conversational contacts fitted to HPA incidence estimates; B: model using patterns of conversational contacts fitted to flusurvey-adjusted incidence estimates; C: model using patterns of physical contacts fitted to HPA incidence estimates; D: model using patterns of physical contacts fitted to flusurvey-adjusted incidence estimates. Best-fitting parameter sets and fits using bootstrapped matrices can be found in Table S2 in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002425#pcbi.1002425.s004" target="_blank">Text S1</a>, and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002425#pcbi.1002425.s001" target="_blank">Figs S1</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002425#pcbi.1002425.s002" target="_blank">S2</a>.</p

Daily contact numbers.

<p>Summary of the number of daily contacts reported by participants in each age group, comparing term time with school holidays. For each age group, the mean (standard deviation), and median [inter-quartile range] are shown. p-values give the significance level for differences in number of contacts reported in school term time and school holidays.</p

Genetic Predisposition to Pass the Standard SICCT Test for Bovine Tuberculosis in British Cattle

<div><p>Bovine tuberculosis (bTB) imposes an important financial burden on the British cattle industry, yet despite intense efforts to control its spread, incidence is currently rising. Surveillance for bTB is based on a skin test that measures an immunological response to tuberculin. Cattle that fail the test are classified as “reactors” and slaughtered. Recent studies have identified genetic markers associated with the reaction of cattle to the tuberculin test. At marker INRA111 a relatively common ‘22’ genotype occurs significantly more frequently in non-reactor cattle. Here we test the possibility that the putative protective ‘22’ genotype does not confer resistance but instead causes cattle that carry it to react less strongly to the prescribed test, and hence avoid slaughter, potentially even though they are infected. We show that, after controlling for age and breed, ‘22’ cattle react less strongly to the immunological challenge and may therefore be less likely to be classified as a reactor. These results highlight the potential discrepancy between infection and test status and imply that the effectiveness of the test-and-slaughter policy may be being compromised by selection for cattle that are genetically predisposed to react less strongly to tuberculin.</p> </div

Prediction of difference in swelling size between initial and final measurements at the avian tuberculin injection site (<i>da</i>) by ‘22’ genotype.

<p>Incident risk and odds ratios for both components of a zero-inflated Poisson model fitted to the avian difference (<i>da</i> ∼ Age + <i>a1</i> + p22). Odds and incident risk ratios (from the Poisson count model and binomial zero inflation terms respectively) are presented to two significant figures, along with 95% confidence intervals calculated from 10000 parametric bootstraps. Significant effects at the 95% level are highlighted in bold. While the age co-efficient is highly significant with the Poisson portion of the model, the p22 effect is only marginally significant for <i>da</i>. The marginal significance of the p22 effect is further emphasised by the variability in the bootstrapped confidence interval, which constitutes a more conservative test.</p

Test and animal factors associated with probability of being the ‘22’ genotype.

<p>Estimated parameters from the final selected logistic regression model for the probability of an animal possessing the ‘22’ genotype (p22 ∼ Age + <i>a2</i> + breed). Odds ratios are presented to two significant figures, along with 95% confidence intervals. Significant effects at the 95% level are highlighted in bold. The selected model shows no significant evidence of a lack of fit (p-value = 0.51). Predictive ability of the selected models was assessed using the receiver-operating-characteristic (ROC) curve, which has an area under the curve of 0.68. Breed effects are measured relative to the Holstein Breed (HOL) that is the most represented breed within the study population. For breed codes, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058245#pone-0058245-t001" target="_blank">Table 1</a>.</p

Predicted effect of ‘22’ genotype on the swelling induced by avian and bovine tuberculin challenges.

<p>Swelling size is taken as the difference between the initial measurement, taken immediately following injection, and the final measurement, taken after the prescribed 72 hour time delay that allows an immune response to occur (hereafter = ‘difference’). This controls for skin thickness differences between animals. The graphs show the predicted impact of the ‘22’ parameter (0 = not ‘22’, 1 = ‘22’) on the avian (da, left) and bovine (<i>db</i>, right) differences. Predicted values are calculated from the respective zero-inflated regression models <i>da</i> ∼ age + <i>a</i>1 + p22, <i>db</i> ∼ age + <i>b1</i> + p22 described within the main text (summarised in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058245#pone-0058245-t003" target="_blank">Tables 3</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058245#pone-0058245-t004" target="_blank">4</a>). The distribution of predicted values with (solid line) and without (dashed line) the ‘22’ genotype are compared as smoothed density curves. No effect was found for the bovine differences but the model predicts a smaller avian difference (<i>da</i>) when among animals with the ‘22’ genotype.</p

Predicted effect of ‘22’ genotype on second avian (<i>a2</i>, red) and second bovine (<i>b2</i>, black) measurements.

<p>The predicted impact of the ‘22’ parameter (0 = not ‘22’, 1 = ‘22’) on the second avian and second bovine swelling size measurements within our study population is summarised as a ‘beanplot’. The solid envelope represents the smoothed density kernel for the predicted values. Actual values are over-plotted as solid lines and the vertical dotted lines indicate the mean effect sizes across all breeds for the two measurements. Predicted values are calculated from the two Poisson regression models: <i>a2</i> ∼ p22 + breed + age; <i>b2</i> ∼ p22 + breed+age (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058245#pone.0058245.s002" target="_blank">Tables S2</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058245#pone.0058245.s003" target="_blank">S3</a>).</p