Posterior means and 95% posterior credibility intervals (PCI) of sensitivities (Se) and specificities (Sp) of the tests combined in parallel or serial reading (2 PCR tests performed in bulk tank milk, PCR BTM, and indoor dust samples, PCR DUST) for the 2 models with different priors for the specificity of PCR DUST.

<p>PCR BTM (n = 380) and PCR DUST (n = 380) tests were performed in 95 dairy cattle herds in the Finistère department, France between November 2012 and April 2014.</p

Posterior means and 95% posterior credibility intervals (PCI) of sensitivities (Se) and specificities (Sp) for the PCR tests performed in bulk tank milk (PCR BTM) and indoor dust samples (PCR DUST), for the 2 models with different priors for the specificity of PCR DUST.

<p>PCR BTM (n = 380) and PCR DUST (n = 380) tests were performed in 95 dairy cattle herds in the Finistère department, France between November 2012 and April 2014.</p

Binary and quantitative test responses for the PCR tests performed in bulk tank milk (PCR BTM) and indoor dust samples (PCR DUST) according to the stratified population.

<p>PCR BTM (n = 380) and PCR DUST (n = 380) tests were performed in 95 dairy cattle herds in the Finistère department, France between November 2012 and April 2014.</p

Relative risk and confidence interval of the bluetongue covariate computed over common time intervals.

<p>Relative risks (RR) and confidence intervals of the bluetongue covariate are displayed for the <i>départements</i> in which the bluetongue covariate had a positive effect, for heifers (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119012#pone.0119012.g005" target="_blank">Fig. 5A</a>, on the left, 31 <i>départements</i>), and for parous cows (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119012#pone.0119012.g005" target="_blank">Fig. 5B</a>, on the right, 36 <i>départements</i>). Départements are sorted in the ascending order of their RR. In the other <i>départements</i>, the bluetongue covariate had a non-significant effect.</p

Weekly calculation of the MAIR.

<p>Suppose a situation in which five females A, B, C, D and E are recorded over week <i>w</i> at different stages of reproduction (with the same parity and in the same <i>département</i>). The period between D_{AI+90 days} and D_{AI+180 days} starts 90 days and ends 180 days after the first AI. By aggregating the number of mid-term abortions and the number of females at risk of having a mid-term abortion on a weekly timescale, one mid-term abortion was identified out of three female-weeks (i.e. 21 female-days/7) at risk.</p

Observed and predicted weekly MAIR and number of BT8 cases in the Aisne <i>département</i>.

<p>The observed mid-term abortion incidence rate, MAIR_<i>ijw</i> (in grey), was computed for heifers (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119012#pone.0119012.g002" target="_blank">Fig. 2A, on</a> the left) and parous cows (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119012#pone.0119012.g002" target="_blank">Fig. 2B</a>, on the right) as the ratio of the observed number of mid-term abortions to the number of at-risk female-weeks. The predicted MAIR (solid pink line) and predictive intervals (dashed pink line) were estimated by modeling the MAIR according to time and the BT8 covariate averaged over a <i>département</i>-specific time interval (with the candidate model having the lowest QAIC, M2). The number of clinical BT8 cases is plotted in black.</p

Distribution of <i>départements</i> according to their weighted mean of time lags <i>ℓ_{m_ij}</i> and <i>k</i>_{m_ij}.

<p>Each graph represents the distribution of the 51 <i>départements</i> (among heifers, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119012#pone.0119012.g004" target="_blank">Fig. 4A</a>, on the left) and 55 <i>départements</i> (among parous cows, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119012#pone.0119012.g004" target="_blank">Fig. 4B</a>, on the right) depending on their weighted mean of time lags <i>ℓ_{m_ij}</i> and <i>k</i>_{m_ij}. Time lags <i>ℓ̅_j</i> and <i>k̅_j</i> averaged among <i>départements</i> are also plotted (black triangle).</p

Effect of BT8 on the MAIR by <i>département</i>.

<p><i>Département</i>-level effects are shown for heifers (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119012#pone.0119012.g003" target="_blank">Fig. 3A</a>, on the left) and parous cows (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119012#pone.0119012.g003" target="_blank">Fig. 3B</a>, on the right). <i>Départements</i> in dark gray were excluded because they reported BT8 and BT1 cases; <i>départements</i> in light gray are those excluded from the study because the marginal probability density functions of and were multimodal, with the probability value of the second best mode higher than half of the value of the major mode. The effect of the mean number of BT8 cases during the <i>département</i>-specific weighted average time interval on the weekly MAIR was positive in <i>départements</i> in dark pink, non-significant in <i>départements</i> in light pink, and negative in <i>départements</i> in pale pink.</p

Qualitative effect of the BT8 covariate on the MAIR computed over <i>département</i>-specific and common time intervals.

<p>Qualitative effect of the BT8 covariate on the MAIR computed over <i>département</i>-specific and common time intervals.</p

Parameters used for the simulation of disease induced milk losses.

<p>Three combinations of parameters were simulated and combined with the disease spread simulations given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073726#pone-0073726-t001" target="_blank">Table 1</a>. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073726#pone-0073726-g002" target="_blank">Figure 2</a> for details on the simulation model of milk losses.</p