A Multiprocess State Space Model with Feedback and Switching for Patterns of Clinical Measurements Associated with COVID-19

TL;DR

This paper introduces a multiprocess state space model with feedback and switching to analyze complex temporal patterns in clinical measurements, demonstrated on COVID-19 patient temperature data.

Contribution

The paper presents a novel multiprocess state space model with feedback and switching mechanisms, along with an EM-based estimation approach for analyzing clinical time series.

Findings

Efficient estimation algorithm demonstrated through simulations

Model effectively captures dynamics of clinical measurements

Applied to COVID-19 patient data to estimate infection and recovery probabilities

Abstract

Clinical measurements, such as body temperature, are often collected over time to monitor an individual's underlying health condition. These measurements exhibit complex temporal dynamics, necessitating sophisticated statistical models to capture patterns and detect deviations. We propose a novel multiprocess state space model with feedback and switching mechanisms to analyze the dynamics of clinical measurements. This model captures the evolution of time series through distinct latent processes and incorporates feedback effects in the transition probabilities between latent processes. We develop estimation methods using the EM algorithm, integrated with multiprocess Kalman filtering and multiprocess fixed-interval smoothing. Simulation study shows that the algorithm is efficient and performs well. We apply the proposed model to body temperature measurements from COVID-19-infected hemodialysis patients to examine temporal dynamics and estimate infection and recovery probabilities.