Foot-and-mouth disease (FMD) is one of the most economically important livestock diseases worldwide. Following the clinical phase of FMD, a large proportion of ruminants remain persistently infected for extended periods. Although extinction of this carrier state occurs continuously at the animal and population levels, studies vary widely in their estimates of the duration of persistent infection. There is a need for robust statistical models to capture the dynamics of persistent infection for the sake of guiding FMD control and trade policies. The goal of the current study was to develop and assess statistical models to describe the extinction of FMD virus (FMDV) persistent infection using data from primary longitudinal studies of naturally infected cattle and Asian buffalo in Vietnam and India. Specifically, accelerated failure time (AFT) models and generalized linear mixed models (GLMM) were developed to predict the probability of persistent infection in seropositive animals and identified carriers at the individual animal level at sequential time points after outbreaks. The primary studies were analyzed by country and combined using an individual-participant data meta-analysis approach. The models estimated similar trends in the duration of persistent infection for the study/species groups included in the analyses, however the significance of the trends differed between the models. The overall probabilities of persistent infection were similar as predicted by the AFT and GLMM models: 6 months: 99% (AFT) /80% (GLMM), 12 months: 51% (AFT) /32% (GLMM), 18 months: 6% (AFT) /5% (GLMM), 24 months: 0.8% (AFT) /0.6% (GLMM). These models utilizing diverse and robust data sets predict higher probabilities of persistence than previously published, suggesting greater endurance of carriers subsequent to an outbreak. This study demonstrates the utility of statistical models to investigate the dynamics of persistent infection and the importance of large datasets, which can be achieved by combining data from several smaller studies in meta-analyses. Results of this study enhance current knowledge of the FMDV carrier state and may inform policy decisions regarding FMDV persistent infection.

中文翻译：

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口蹄疫病毒携带者在自然条件下的灭绝动力学。

口蹄疫（FMD）是全球最重要的经济牲畜疾病之一。在FMD的临床阶段之后，很大一部分反刍动物仍然持续受到长期感染。尽管这种携带者状态的灭绝在动物和种群水平上持续发生，但是研究对持续感染持续时间的估计差异很大。为了指导FMD控制和贸易政策，需要一种可靠的统计模型来捕获持续感染的动态。本研究的目的是使用来自越南和印度的自然感染牛和亚洲水牛的主要纵向研究数据，开发和评估统计模型，以描述FMD病毒（FMDV）持续感染的灭绝。特别，建立了加速失败时间（AFT）模型和广义线性混合模型（GLMM），以预测在疾病暴发后的连续时间点，血清阳性动物和个体动物水平上确定的携带者持续感染的可能性。各国对主要研究进行了分析，并使用个人参与者数据荟萃分析的方法进行了合并。这些模型对分析中包括的研究/物种组的持续感染持续时间进行了相似的趋势估计，但是，模型之间趋势的重要性不同。持续感染的总体可能性与AFT和GLMM模型预测的相似：6个月：99％（AFT）/ 80％（GLMM），12个月：51％（AFT）/ 32％（GLMM），18个月： 6％（AFT）/ 5％（GLMM），24个月：0.8％（AFT）/0.6%（GLMM）。这些利用多样化且稳健的数据集的模型预测的持久性概率要比以前公布的更高，这表明爆发后携带者的耐受力更高。这项研究证明了统计模型可用于调查持续感染的动力学以及大型数据集的重要性，这可以通过将一些较小规模研究的数据结合到荟萃分析中来实现。这项研究的结果增强了对FMDV携带者状态的当前了解，并可能为有关FMDV持续感染的政策决策提供依据。这项研究证明了统计模型可用于调查持续感染的动力学以及大型数据集的重要性，这可以通过将一些较小规模研究的数据结合到荟萃分析中来实现。这项研究的结果增强了对FMDV携带者状态的当前了解，并可能为有关FMDV持续感染的政策决策提供依据。这项研究表明统计模型可用于调查持续感染的动力学以及大型数据集的重要性，这可以通过在荟萃分析中结合几个较小研究的数据来实现。这项研究的结果增强了对FMDV携带者状态的当前了解，并可能为有关FMDV持续感染的政策决策提供依据。