Ensemble inference of unobserved infections in networks using partial observations

• Published in: PLoS computational biology Vol. 19; no. 8; p. e1011355
• Main Authors: Zhang, Renquan, Tai, Jilei, Pei, Sen
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.08.2023; Public Library of Science (PLoS)
• Subjects: Agent based models; Algorithms; Analysis; Bayes Theorem; Bayesian analysis; Biology and Life Sciences; Computer and Information Sciences; Contact tracing; COVID-19; Decision making; Disease transmission; Dynamic models; Epidemics; Gene Regulatory Networks; Health aspects; Humans; Infection; Infectious diseases; Influenza; Mathematical models; Medicine and Health Sciences; Methicillin; Methods; Networks; Parameters; Physical Sciences; Probability; Research and Analysis Methods; Severe acute respiratory syndrome coronavirus 2; Simulation; Social Sciences; Staphylococcus infections; Statistical inference; Uncertainty; China
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1011355

Undetected infections fuel the dissemination of many infectious agents. However, identification of unobserved infectious individuals remains challenging due to limited observations of infections and imperfect knowledge of key transmission parameters. Here, we use an ensemble Bayesian inference method to infer unobserved infections using partial observations. The ensemble inference method can represent uncertainty in model parameters and update model states using all ensemble members collectively. We perform extensive experiments in both model-generated and real-world networks in which individuals have differential but unknown transmission rates. The ensemble method outperforms several alternative approaches for a variety of network structures and observation rates, despite that the model is mis-specified. Additionally, the computational complexity of this algorithm scales almost linearly with the number of nodes in the network and the number of observations, respectively, exhibiting the potential to apply to large-scale networks. The inference method may support decision-making under uncertainty and be adapted for use for other dynamical models in networks.