Signal-Oriented Framework for Early Identification of Imported Respiratory Infectious Disease Risk.

Authors

Pan Yao, Qiao Xiao, Bing Li, Xiaoming Wu, Penghao Wu, Linhuan Wu, Qinglan Sun, Yuanbin Liu, Long Li, Xiaotong Lu, Jiangjiang Yu, Yan Niu, Qun Li, Hongtao Wu

Published in

China CDC weekly. Volume 8. Issue 39. Pages 1218-1223. Sep 25, 2026.

Abstract

Increasing international travel and population mobility have made surveillance of imported infectious diseases more complex. However, current approaches still rely largely on confirmed epidemiological evidence and retrospective analyses of importation events. Their limited capacity to integrate heterogeneous information constrains early identification of emerging importation risks. Drawing on signal analysis theory, we developed a conceptual and operational framework for early identification of imported respiratory infectious disease risks involving human-to-human transmission. The framework conceptualizes imported disease risk as a dynamic process in which underlying risk factors generate observable surveillance indicators and event-based information that are progressively transformed into candidate risk signals, risk signals, and warning signals. We established an operational workflow comprising signal collection, integration, assessment, and escalation to organize heterogeneous evidence across the importation pathway. The framework incorporates four complementary domains: pathogen characteristics, overseas epidemic situation, cross-border importation risk, and post-entry transmission risk. These domains provide structured inputs for signal identification and assessment. Risk signals are evaluated according to signal characteristics, epidemiological stage, and consequence severity; they may be escalated to warning signals when predefined evidence and priority criteria are met. This framework provides an operational approach for integrating weak and heterogeneous information before importation events are fully confirmed, supporting earlier risk recognition and public health decision-making.

PMID:
42891129
Bibliographic data and abstract were imported from PubMed on 18 Oct 2026.

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