Identifying the infection status of each individual during infectious diseases informs public health management. However, performing frequent individual-level tests may not be feasible. Instead, sparse and sometimes group-level tests are performed. Determining the infection status of individuals using sparse group-level tests remains an open problem. We have tackled this problem by extending graph-coupled hidden Markov models with individuals infection statuses as the hidden states and the group test results as the observations. We fitted the model to simulation datasets using the Gibbs sampling method. The model performed about 0.55 AUC for low testing frequencies and increased to 0.80 AUC in the case where the groups were tested every day. The model was separately tested on a daily basis case to predict the statuses over time and after 15 days of the beginning of the spread, which resulted in 0.98 AUC at day 16 and remained above 0.80 AUC until day 128. Therefore, although dealing with sparse tests remains unsolved, the results open the possibility of using initial group screenings during pandemics to accurately estimate individuals infection statuses.

在流行病期间使用初始群体筛查来准确估计个体感染状况的可能性得到了开放，通过使用个体感染状况作为隐藏状态和群体测试结果作为观测结果，扩展了基于图耦合的隐马尔可夫模型，对模拟数据集拟合模型得到了0.55的AUC。

利用图耦合隐马尔可夫模型和稀疏群体检测方法，检测个体级感染