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A novel spatial and stochastic model to evaluate the within and between farm transmission of classical swine fever virus: II Validation of the model

机译:一种新型的空间和随机模型,用于评估经典猪瘟病毒的场内传播和场内传播:II模型验证

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摘要

A new, recently published, stochastic and spatial model for the evaluation of classical swine fever virus (CSFV) spread into Spain has been validated by using several methods. Internal validity, sensitivity analysis, validation using historical data, comparison with other models and experiments on data validity were used to evaluate the overall reliability and robustness of the model. More than 100 modifications in input data and parameters were evaluated. Outputs were obtained after 1000 iterations for each new scenario of the model. As a result, the model was shown to be robust, being the probability of infection by local spread, the time from infectious to clinical signs state, the probability of detection based on clinical signs at day t after detection of the index case outside the control and surveillance zones and the maximum number of farms to be depopulated at day t the parameters that more influence (>10% of change) on the magnitude and duration of the epidemic. The combination of a within- and between- farm spread model was also shown to give significantly different results than using a purely between-farm spread model. Methods and results presented here were intended to be useful to better understand and apply the model, to identify key parameters for which it will be critical to have good estimates and to provide better support for prevention and control of future CSFV outbreaks.
机译:已经使用多种方法验证了一种新的,最近发表的随机和空间模型,用于评估传播到西班牙的经典猪瘟病毒(CSFV)。内部有效性,敏感性分析,使用历史数据进行的验证,与其他模型的比较以及数据有效性的实验用于评估模型的整体可靠性和鲁棒性。对输入数据和参数的100多种修改进行了评估。该模型的每个新方案经过1000次迭代后都获得了输出。结果表明,该模型具有较强的鲁棒性,它是通过局部传播感染的概率,从传染性到临床体征状态的时间,在检测到无法控制的索引病例后第t天基于临床体征的检测概率。监测区域以及在第t天要减少人口的最大农场数量,这些参数会对疫情的程度和持续时间产生更大的影响(> 10%的变化)。与单纯使用农场之间的传播模型相比,内部和农场之间的传播模型的组合还显示出明显不同的结果。本文介绍的方法和结果旨在有助于更好地理解和应用该模型,确定关键参数,对于这些关键参数而言,进行良好的估算至关重要,并为预防和控制未来的CSFV爆发提供更好的支持。

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