Some patients with COVID-19 show changes in signs and symptoms such as temperature and oxygen saturation days before being positively tested for SARS-CoV-2, while others remain asymptomatic. It is important to identify these subgroups and to understand what biological and clinical predictors are related to these subgroups. This information will provide insights into how the immune system may respond differently to infection and can further be used to identify infected individuals. We propose a flexible nonparametric mixed-effects mixture model that identifies risk factors and classifies patients with biological changes. We model the latent probability of biological changes using a logistic regression model and trajectories in the latent groups using smoothing splines. We developed an EM algorithm to maximize the penalized likelihood for estimating all parameters and mean functions. We evaluate our methods by simulations and apply the proposed model to investigate changes in temperature in a cohort of COVID-19-infected hemodialysis patients.

翻译：部分COVID-19患者在SARS-CoV-2检测阳性前数日即出现体温、血氧饱和度等体征与症状变化，而其他患者则始终无症状。识别这些亚群并理解与这些亚群相关的生物学及临床预测因素至关重要。这些信息将揭示免疫系统对感染产生不同反应的机制，并可用于识别感染者。我们提出一种灵活的非参数混合效应混合模型，用于识别风险因素并对存在生物学变化的患者进行分类。我们采用逻辑回归模型对生物学变化的潜在概率进行建模，并利用平滑样条函数刻画潜在组别中的变化轨迹。通过开发EM算法最大化惩罚似然函数，实现所有参数与均值函数的估计。我们通过模拟实验评估该方法，并将该模型应用于调查某血液透析新冠感染患者队列的体温变化模式。