Detecting individual-level infections using sparse group-testing through graph-coupled hidden Markov models

TL;DR

This paper introduces a graph-coupled hidden Markov model to infer individual infection statuses from sparse group tests, showing promising accuracy over time, which could aid pandemic management.

Contribution

The study extends hidden Markov models to incorporate group testing data for individual infection detection, demonstrating effective inference with sparse testing regimes.

Findings

Achieved 0.55 AUC with low testing frequency

Reached 0.80 AUC with daily group testing

Maintained above 0.80 AUC for over 100 days

Abstract

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.