  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.
