Estimating the prevalence of a disease is necessary for evaluating and mitigating risks of its transmission within or between populations. Estimates that consider how prevalence changes with time provide more information about these risks but are difficult to obtain due to the necessary sampling intensity and commensurate testing costs. We propose pooling and jointly testing multiple samples to reduce testing costs and use a novel nonparametric, hierarchical Bayesian model to infer population prevalence from the pooled test results. This approach is shown to reduce uncertainty compared to individual testing at the same budget and to produce similar estimates compared to individual testing at a much higher budget through two synthetic studies and two case studies of natural infection data.
翻译:估计一种疾病的流行程度对于评估和减轻其在人口内部或人口之间传播的风险是必要的; 考虑流行程度随时间而变化如何提供有关这些风险的更多资料的估计数,但由于必要的取样强度和相称的测试费用,难以获得这种资料; 我们提议汇集和联合测试多种样品,以减少测试费用,并使用一种新的非参数性的、等级分级的巴耶斯模式从综合测试结果中推断人口流行程度; 这种方法表明,与同一预算的个别测试相比,可以减少不确定性,并通过两项合成研究和两项自然感染数据案例研究,得出与个人测试相比的类似估计数,其预算要高得多。