Production impact of influenza A(H1N1)pdm09 virus infection on fattening pigs in Norway
Production impact of influenza A(H1N1)pdm09 virus infection on fattening pigs in Norway
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DOI：
10.2527/jas.2015-9251
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发表时间：
2016-02-01
影响因子：
3.3
通讯作者：
Lium, B.
中科院分区：
文献类型：
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作者：
Er, C.;Skjerve, E.;Lium, B.
挪威猪中新出现的甲型H1N1流感pdm 09病毒感染，尽管经常以亚临床形式观察到，但可通过降低饲料效率（饲料转化率较差）降低猪的生长性能。受感染的猪将消耗更多的饲料，并需要延长生产时间才能达到市场重量。在我们的观察性纵向研究中，使用混合线性回归模型分析了来自728头对照猪和193头具有已知病毒散毒时间点的感染猪的生长性能数据，以估计感染的边际效应。描述个体猪水平估计值变异性的高斯曲线构成了我们随机模型的基本输入。构建模型以模拟150头生长在33 - 100 kg的育肥猪在批次水平上感染的总负面效应。其他可变性和不确定性输入包括：1）批次传播点，2）猪感染点，以反映病毒的疾病传播动态，3）批次中受感染猪的最终流行率。蒙特卡洛随机抽样对每头猪的边际效应输出给出了5,000个估计值。总结这些结果，以提供150头猪批量的估计值。该数字通过我们的最终患病率分布函数进行了调整，该函数也来自12个感染猪队列的纵向研究。对于从群体中随机选择的150头育肥猪群，感染的边际效应为：1）采食量增加835 kg（第5百分位数）至1，350 kg（第95百分位数），2）超过未感染批次预期数字的194（第5百分位数）至334（第95百分位数）猪日数。在生长阶段3期间感染的批次（81至100 kg BW）产生了最差结果，因为纵向研究表明，与年轻感染猪相比，在生长阶段3期间感染的猪需要更多的饲料和更长的生产时间。敏感性分析显示，最终患病率对感染边际效应的条件均值和变异影响最大。批量传输点是第二大影响因素。降低最终流行率和防止年龄较大的育肥猪被感染将在节省饲料成本和减少猪上市延迟方面具有最大的好处。
Newly emerged influenza A(H1N1) pdm09 virus infection in Norwegian pigs, although often observed in a subclinical form, can lower the pig's growth performance by reducing feed efficiency in terms of a poorer feed conversion ratio. Infected pigs would consume more feed and require protracted production time to reach market weight. In our observational longitudinal study, growth performance data from 728 control pigs and 193 infected pigs with known viral shedding time points were analyzed using mixed linear regression models to give estimates of the marginal effects of infection. Gaussian curves describing the variability of the estimates at the individual pig level formed the fundamental inputs to our stochastic models. The models were constructed to simulate the summed negative effects of the infection at the batch level of 150 fattening pigs growing from 33 to 100 kg. Other inputs of variability and uncertainty were 1) batch transmission points, 2) pig infection points to reflect the disease transmission dynamics of the virus, and 3) final prevalence of infected pigs in the batch. Monte Carlo random sampling gave 5,000 estimates on the outputs of the marginal effects for each pig. These results were summed up to provide estimates for a batch size of 150 pigs. This figure was adjusted by our final prevalence distribution function, which was also derived from the longitudinal study with 12 cohorts of infected pigs. For a 150-fattening-pig herd randomly selected from the population, the marginal effects of the infection were 1) 835 kg (fifth percentile) to 1,350 kg (95th percentile) increased feed intake and 2) 194 (fifth percentile) to 334 (95th percentile) pig days in excess of expected figures for an uninfected batch. A batch infected during growth phase 3 (81 to 100 kg BW) gave the worst results since the longitudinal study showed that a pig infected during growth phase 3 required more feed and a greater protracted production time compared to younger infected pigs. Sensitivity analysis showed that final prevalence had the greatest impact on the conditional mean and variation of the marginal effects of infections. Batch transmission point was the next most influential factor. Lowering the final prevalence and preventing older fattening pigs from being infected will have the greatest benefit in saving feed cost and reducing delay in getting the pigs to the market.