Estimation of Covid-19 prevalence from serology tests: A partial identification approach
利用血清学检测估计新冠感染率：部分识别方法AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究疾病感染率的部分识别与有限样本推断，未直接对应所列因果、分布效应或其他细分。
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利用血清学检测估计新冠感染率：部分识别方法
- 研究问题
- 当抗体检测的真阳性率与假阳性率未知、感染率可能无法点识别时，如何从血清学样本获得具有有限样本有效性的推断？
- 方法与思路
- 作者扫描整个参数空间，以联合数据密度为检验统计量排除参数值，再形成感染率等参数的置信集合，依赖检测结果独立。
- 材料陈述的贡献
- 摘要称，即使模型未点识别也可进行有限样本有效推断；美国血清学应用展示检测误差不确定性如何扩大感染率置信集合。
- 阅读关注点
- 应核对联合密度检验的校准、边际置信集合的投影方式、检测结果独立性的适用性，以及跨地区数据合并所需条件。
- 阅读边界
- 仅据所给出版社摘要，不能判断抽样代表性或独立性是否成立；报告的感染率区间针对研究时点，不能解释为当前流行情况。
原文摘要
We propose a partial identification method for estimating disease prevalence from serology studies. Our data are results from antibody tests in some population sample, where the test parameters, such as the true/false positive rates, are unknown. Our method scans the entire parameter space, and rejects parameter values using the joint data density as the test statistic. The proposed method is conservative for marginal inference, in general, but its key advantage over more standard approaches is that it is valid in finite samples even when the underlying model is not point identified. Moreover, our method requires only independence of serology test results, and does not rely on asymptotic arguments, normality assumptions, or other approximations. We use recent Covid-19 serology studies in the US, and show that the parameter confidence set is generally wide, and cannot support definite conclusions. Specifically, recent serology studies from California suggest a prevalence anywhere in the range 0%-2% (at the time of study), and are therefore inconclusive. However, this range could be narrowed down to 0.7%–1.5% if the actual false positive rate of the antibody test was indeed near its empirical estimate (∼0.5%). In another study from New York state, Covid-19 prevalence is confidently estimated in the range 13%–17% in mid-April of 2020, which also suggests significant geographic variation in Covid-19 exposure across the US. Combining all datasets yields a 5%–8% prevalence range. Our results overall suggest that serology testing on a massive scale can give crucial information for future policy design, even when such tests are imperfect and their parameters unknown.