Evidence map›Paper›PMID 40822276›Full record

ArticleInfectious Disease Modelling2025

Hierarchical signal amplification for regional infectious disease risk assessment: A multidimensional approach from urban China.

Rui Shen, Xiao Liu, Yi Yuan, Jiang Long, Xueying Xu, Yugang Li, Jie Qian, Zilu Xu, Na Sun, Hao Jiang and 4 more

Abstract read
In one paragraph

Article in Infectious Disease Modelling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Rui ShenPublic Health Emergency Management Innovation Center, School of Population Medicine & and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Key Laboratory of Pathogen Infection Prevention and Control, Peking Union Medical College, Ministry of Education, State Key Laboratory of Respiratory Health and Multimorbidity, Beijing, 100730, China.
Xiao LiuPublic Health Emergency Management Innovation Center, School of Population Medicine & and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Key Laboratory of Pathogen Infection Prevention and Control, Peking Union Medical College, Ministry of Education, State Key Laboratory of Respiratory Health and Multimorbidity, Beijing, 100730, China.
Yi YuanChongqing Center for Disease Control and Prevention (Chongqing Academy of Preventive Medical Sciences), Chongqing, China.
Jiang LongChongqing Center for Disease Control and Prevention (Chongqing Academy of Preventive Medical Sciences), Chongqing, China.
Xueying XuSchool of Public Health, China Medical University, Shenyang, China.
Yugang LiPublic Health Emergency Management Innovation Center, School of Population Medicine & and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Key Laboratory of Pathogen Infection Prevention and Control, Peking Union Medical College, Ministry of Education, State Key Laboratory of Respiratory Health and Multimorbidity, Beijing, 100730, China.
Jie QianPublic Health Emergency Management Innovation Center, School of Population Medicine & and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Key Laboratory of Pathogen Infection Prevention and Control, Peking Union Medical College, Ministry of Education, State Key Laboratory of Respiratory Health and Multimorbidity, Beijing, 100730, China.
Zilu XuPublic Health Emergency Management Innovation Center, School of Population Medicine & and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Key Laboratory of Pathogen Infection Prevention and Control, Peking Union Medical College, Ministry of Education, State Key Laboratory of Respiratory Health and Multimorbidity, Beijing, 100730, China.
Na SunPublic Health Emergency Management Innovation Center, School of Population Medicine & and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Key Laboratory of Pathogen Infection Prevention and Control, Peking Union Medical College, Ministry of Education, State Key Laboratory of Respiratory Health and Multimorbidity, Beijing, 100730, China.
Hao JiangPublic Health Emergency Management Innovation Center, School of Population Medicine & and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Key Laboratory of Pathogen Infection Prevention and Control, Peking Union Medical College, Ministry of Education, State Key Laboratory of Respiratory Health and Multimorbidity, Beijing, 100730, China.
Weizhong YangPublic Health Emergency Management Innovation Center, School of Population Medicine & and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Key Laboratory of Pathogen Infection Prevention and Control, Peking Union Medical College, Ministry of Education, State Key Laboratory of Respiratory Health and Multimorbidity, Beijing, 100730, China.
Huadong ZhangChongqing Center for Disease Control and Prevention (Chongqing Academy of Preventive Medical Sciences), Chongqing, China.
Li QiChongqing Center for Disease Control and Prevention (Chongqing Academy of Preventive Medical Sciences), Chongqing, China.
Luzhao FengPublic Health Emergency Management Innovation Center, School of Population Medicine & and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Key Laboratory of Pathogen Infection Prevention and Control, Peking Union Medical College, Ministry of Education, State Key Laboratory of Respiratory Health and Multimorbidity, Beijing, 100730, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Infectious disease surveillance systems face methodological limitations in early warning capabilities, particularly in integrating correlated indicators, assessing healthcare system impacts, and synthesizing diverse data streams into actionable intelligence. We aimed to develop and validate a hierarchical signal amplification algorithm generating daily risk scores for regional infectious disease assessment. Methods: In this retrospective observational study in Chongqing, China (population 32.1 million; 2023-2024), we constructed a dual-dimensional framework integrating transmission potential (n = 5) and healthcare system impact (n = 8) indicators with nonlinear aggregation mechanisms. The framework was assessed across respiratory infections, intestinal diseases, and hand-foot-and-mouth disease. Results: The integrated risk assessment system achieved 83.92 % sensitivity and 88.62 % specificity at risk score 5. Detection capabilities varied by disease category: respiratory infections (98.53 % sensitivity), intestinal diseases (89.39 %), and hand-foot-and-mouth disease (65.22 %). Early warning lead times reached 11, 7, and 6 days respectively. System stability was validated through Monte Carlo simulation (consistency index 0.96). Conclusions: The hierarchical signal amplification approach transforms diverse surveillance data into a daily regional risk score that provides substantial lead times for preemptive public health action while maintaining signal integrity across multiple transmission patterns.

Indexed as

Early warningHierarchical algorithmInfectious diseaseNonlinear aggregationRisk assessmentSignal processing

Identifiers

PMID40822276
PMCPMC12356008

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.