美国普林斯顿大学一项研究报告说，通过把一个良好设计的疾病传播模型与可靠的统计推断结合起来，可以准确测量家庭流感干预手段的有效性。诸如增加手的卫生与口罩等干预手段的有效性，在传统上是通过衡量这些干预手段减少表现出症状的家庭接触者的比例而加以评估的。Max Lau及其同事开发了一种测量干预手段有效性的替代方法，它把症状报告和
病毒脱落数据结合起来，并且使用
贝叶斯方法考虑到了未观察到的传播。这组作者
分析了来自参与一个家庭抗流感干预手段研究的322个香港家庭的数据，结果发现，增加手的卫生减少了感染风险达37%，而把增加手的卫生与口罩结合起来，减少了感染风险27.2%。这组作者推断出了在该研究期间所有家庭是否存在感染，并且估计传统方法仅仅能够探测61.7%的家庭感染。这些结果符合此前的研究，此前的研究表明了口罩和改善的手的卫生作为抗流感的非药物干预手段的有效性。这组作者说，这项研究提示，把设计良好的疾病传播模型与统计推断结合起来，能够有助于比传统方法更准确地衡量干预手段的有效性。
Household-based interventions are the mainstay of public
health policy against epidemic respiratory pathogens when vaccination is not available. Although the efficacy of these interventions has traditionally been measured by their ability to reduce the proportion of household contacts who exhibit symptoms [household secondary attack rate (hSAR)], this metric is difficult to interpret and makes only partial use of data collected by modern field studies. Here, we use Bayesian transmission model inference to analyze jointly both symptom reporting and viral shedding data from a three-armed study of influenza interventions. The reduction in hazard of infection in the increased hand hygiene intervention arm was 37.0% [8.3%, 57.8%], whereas the equivalent reduction in the other intervention arm was 27.2% [−0.46%, 52.3%] (increased hand hygiene and face masks). By imputing the presence and timing of unobserved infection, we estimated that only 61.7% [43.1%, 76.9%] of infections met the case criteria and were thus detected by the study design. An assessment of interventions using inferred infections produced more intuitively consistent attack rates when households were stratified by the speed of intervention, compared with the crude hSAR. Compared with adults, children were 2.29 [1.66, 3.23] times as infectious and 3.36 [2.31, 4.82] times as susceptible. The mean generation time was 3.39 d [3.06, 3.70]. Laboratory confirmation of infections by
RT-PCR was only able to detect 79.6% [76.5%, 83.0%] of symptomatic infections, even at the peak of shedding. Our results highlight the potential use of robust inference with well-designed mechanistic transmission models to improve the design of intervention studies.