Accurate estimates of seasonal infection risk can be used by animal health officials to predict future disease risk and better understand the mechanisms driving disease dynamics. It can be difficult to estimate seasonal infection risk in wildlife disease systems because surveillance assays typically target antibodies ('serosurveillance'), which are not necessarily indicative of current infection, and serosurveillance sampling is often opportunistic. Recently developed methods estimate past time of infection from serosurveillance data using quantitative serological assays that indicate the amount of antibodies in a serology sample. However, current methods do not account for common opportunistic and uneven sampling associated with serosurveillance data. We extended the framework of survival analysis to improve estimates of seasonal infection risk from serosurveillance data across population and regional scales. We found that accounting for the right-censored nature of quantitative serology samples greatly improved estimates of seasonal infection risk, even when sampling was uneven in time. Survival analysis can also be used to account for common challenges when estimating infection risk from serology data, such as biases induced by host demography and continually elevated antibodies following infection. The framework developed herein is widely applicable for estimating seasonal infection risk from serosurveillance data in humans, wildlife, and livestock.

中文翻译：

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从血清学样本推断人口和区域尺度的季节性感染风险


动物卫生官员可以利用对季节性感染风险的准确估计来预测未来的疾病风险，并更好地了解驱动疾病动态的机制。估计野生动物疾病系统中的季节性感染风险可能很困难，因为监测测定通常针对抗体（“血清监测”），这不一定表明当前感染，并且血清监测采样通常是机会性的。最近开发的方法使用定量血清学测定来根据血清监测数据估计过去的感染时间，该测定表明血清学样本中的抗体量。然而，当前的方法没有考虑与血清监测数据相关的常见机会性和不均匀采样。我们扩展了生存分析框架，以改进根据跨人口和区域规模的血清监测数据对季节性感染风险的估计。我们发现，考虑到定量血清学样本的右审查性质，即使在采样时间不均匀的情况下，也大大提高了对季节性感染风险的估计。生存分析还可用于解决根据血清学数据估计感染风险时的常见挑战，例如宿主人口统计学引起的偏差和感染后抗体持续升高。本文开发的框架广泛适用于根据人类、野生动物和牲畜的血清监测数据估计季节性感染风险。