Estimating new HIV infections is significant yet challenging due to the difficulty in distinguishing between recent and long-term infections. We demonstrate that HIV recency status (recent v.s. long-term) could be determined from the combination of self-report testing history and biomarkers, which are increasingly available in bio-behavioral surveys. HIV recency status is partially observed, given the self-report testing history. For example, people who tested positive for HIV over one year ago should have a long-term infection. Based on the nationally representative samples collected by the Population-based HIV Impact Assessment (PHIA) Project, we propose a likelihood-based probabilistic model for HIV recency classification. The model incorporates both labeled and unlabeled data and integrates the mechanism of how HIV recency status depends on biomarkers and the mechanism of how HIV recency status, together with the self-report time of the most recent HIV test, impacts the test results, via a set of logistic regression models. We compare our method to logistic regression and the binary classification tree (current practice) on Malawi, Zimbabwe, and Zambia PHIA data, as well as on simulated data. Our model obtains more efficient and less biased parameter estimates and is relatively robust to potential reporting error and model misspecification.

翻译：准确估计新发HIV感染数量具有重要意义，但由于难以区分新近感染与长期感染，这项工作极具挑战性。我们证明，结合自我报告检测史和生物标志物（在生物行为学调查中日益普及）可以判定HIV感染新近状态（新近感染与长期感染）。在给定自我报告检测史的情况下，HIV感染新近状态是部分可观测的。例如，一年前HIV检测呈阳性者应属于长期感染。基于“基于人群的HIV影响评估”（PHIA）项目收集的全国代表性样本，我们提出了一种基于似然的概率模型用于HIV新近感染分类。该模型整合了有标签和无标签数据，并通过一组逻辑回归模型，融合了HIV感染新近状态依赖于生物标志物的机制，以及HIV感染新近状态与最近一次HIV检测的自我报告时间共同影响检测结果的机制。我们在马拉维、津巴布韦和赞比亚的PHIA数据以及模拟数据上，将我们的方法与逻辑回归和二元分类树（当前实践方法）进行了比较。我们的模型获得了更高效、偏差更小的参数估计，并且对潜在的报告错误和模型设定错误具有相对稳健性。