Evidence map›Paper›PMID 39114314›Full record

ArticleChina CDC weekly2024

Developing a Research Network of Early Warning Systems for Infectious Diseases Transmission Between China and Australia.

Cynthia Lu, Liping Wang, Ian Barr, Stephen Lambert, Kerrie Mengersen, Weizhong Yang, Zhongjie Li, Xiaohan Si, Hannah McClymont, Shovanur Haque and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in China CDC weekly, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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.

Cynthia LuEcosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia.
Liping WangDivision of Infectious Disease, National Key Laboratory of Intelligent Tracking and Forcasting for Infectious Diseases, Chinese Centre for Disease Control and Prevention, Beijing, China.
Ian BarrWHO Collaborating Centre for Reference and Research on Influenza, VIDRL, Doherty Institute, Melbourne, Australia.
Stephen LambertCommunicable Disease Branch, Queensland Health, Brisbane, Queensland, Australia.
Kerrie MengersenSchool of Mathematical Sciences, Faculty of Science, Queensland University of Technology, Brisbane, Queensland, Australia.
Weizhong YangSchool of Population Medicine & Public Health, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China.
Zhongjie LiSchool of Population Medicine & Public Health, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China.
Xiaohan SiEcosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia.
Hannah McClymontEcosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia.
Shovanur HaqueEcosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia.
Ting GanEcosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia.
Sotiris VardoulakisHEAL Global Research Centre, Health Research Institute, University of Canberra, Australian Capital Territory, Australia.
Hilary BambrickNational Centre for Epidemiology and Population Health, College of Health and Medicine, The Australian National University, Canberra, Australian Capital Territory, Australia.
Wenbiao HuEcosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article offers a thorough review of current early warning systems (EWS) and advocates for establishing a unified research network for EWS in infectious diseases between China and Australia. We propose that future research should focus on improving infectious disease surveillance by integrating data from both countries to enhance predictive models and intervention strategies. The article highlights the need for standardized data formats and terminologies, improved surveillance capabilities, and the development of robust spatiotemporal predictive models. It concludes by examining the potential benefits and challenges of this collaborative approach and its implications for global infectious disease surveillance. This is particularly relevant to the ongoing project, early warning systems for Infectious Diseases between China and Australia (NetEWAC), which aims to use seasonal influenza as a case study to analyze influenza trends, peak activities, and potential inter-hemispheric transmission patterns. The project seeks to integrate data from both hemispheres to improve outbreak predictions and develop a spatiotemporal predictive modeling system for seasonal influenza transmission based on socio-environmental factors.

Indexed as

Early WarningInfectious diseasesInfluenzaSurveillance

Identifiers

PMID39114314
PMCPMC11301605

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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.