[Expert consensus on the key technologies for a multi-point trigger intelligent surveillance and early warning system for infectious diseases].

An effective infectious disease surveillance and early warning system is a crucial component of public health safety and is essential for preventing and controlling outbreaks of infectious diseases. Enhancing surveillance and early warning capabilities is an urgent priority for advancing high-quality disease prevention and control efforts. Combining the research findings and practical experiences of experts in epidemiology, clinical medicine, disease prevention and control, data science, and computer science, and following multiple rounds of expert discussions, we have developed a consensus on the key technologies for a multi-point trigger intelligent surveillance and early warning system for infectious diseases. This consensus primarily covers the related concepts and definitions of the multi-point trigger intelligent surveillance and early warning system for infectious diseases, the key technical framework, sources, acquisition, and governance of multi-channel warning data, classification of early warning methods, multi-point trigger intelligent surveillance and early warning paths, multi-point trigger warning and comprehensive assessment, response to warning signals, and evaluation of early warning effectiveness. It aims to provide technical references for the construction and application of a multi-point trigger intelligent surveillance and early warning system for infectious diseases.

Publication Details

Journal
PubMed
Published
2024-08-20
DOI
https://doi.org/10.3760/cma.j.cn112137-20240612-01317
Citations
2
Primary Topic
Data-Driven Disease Surveillance
Type
article
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

[Expert consensus on the key technologies for a multi-point trigger intelligent surveillance and early warning system for infectious diseases].

2 citations
PubMed
Data-Driven Disease Surveillance
article

[Expert consensus on the key technologies for a multi-point trigger intelligent surveillance and early warning system for infectious diseases].

article en
2 citations

Abstract

An effective infectious disease surveillance and early warning system is a crucial component of public health safety and is essential for preventing and controlling outbreaks of infectious diseases. Enhancing surveillance and early warning capabilities is an urgent priority for advancing high-quality disease prevention and control efforts. Combining the research findings and practical experiences of experts in epidemiology, clinical medicine, disease prevention and control, data science, and computer science, and following multiple rounds of expert discussions, we have developed a consensus on the key technologies for a multi-point trigger intelligent surveillance and early warning system for infectious diseases. This consensus primarily covers the related concepts and definitions of the multi-point trigger intelligent surveillance and early warning system for infectious diseases, the key technical framework, sources, acquisition, and governance of multi-channel warning data, classification of early warning methods, multi-point trigger intelligent surveillance and early warning paths, multi-point trigger warning and comprehensive assessment, response to warning signals, and evaluation of early warning effectiveness. It aims to provide technical references for the construction and application of a multi-point trigger intelligent surveillance and early warning system for infectious diseases.

PubMedVol. 104(32)
Good health and well-being
Data-Driven Disease Surveillance
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.