Literature DB >> 24001353

How can we improve global infectious disease surveillance and prevent the next outbreak?

Sara Gorman1.   

Abstract

Despite a significant amount of progress in the past decade, global infectious disease surveillance still often falters, as in the case of the emerging novel coronavirus that has killed at least 17 people in Saudi Arabia. This article argues that we must continuously re-evaluate global infectious disease surveillance systems. It takes stock of problems in various countries' infectious disease surveillance systems and offers recommendations for how to improve surveillance and ensure more rapid reporting. Chief among the recommendations are strategies for reducing fragmentation in global surveillance systems and methods for making these systems less disease-specific. Suggestions are also offered for ways to improve infectious disease surveillance strategies in resource-limited settings.

Entities:  

Mesh:

Year:  2013        PMID: 24001353     DOI: 10.3109/00365548.2013.826877

Source DB:  PubMed          Journal:  Scand J Infect Dis        ISSN: 0036-5548


  5 in total

1.  Estimation of the Outbreak Severity and Evaluation of Epidemic Prevention Ability of COVID-19 by Province in China.

Authors:  Yilei Ma; Xuehan Liu; Weiwei Tao; Yuchen Tian; Yanran Duan; Ming Xiang; Jing Hu; Lei Li; Yalan Lyu; Peng Wang; Yangxin Huang; Caihong Lu; Wenhua Liu; Hongwei Jiang; Ping Yin
Journal:  Am J Public Health       Date:  2020-10-15       Impact factor: 9.308

Review 2.  Visualization and analytics tools for infectious disease epidemiology: a systematic review.

Authors:  Lauren N Carroll; Alan P Au; Landon Todd Detwiler; Tsung-Chieh Fu; Ian S Painter; Neil F Abernethy
Journal:  J Biomed Inform       Date:  2014-04-16       Impact factor: 6.317

3.  Comparing national infectious disease surveillance systems: China and the Netherlands.

Authors:  Willemijn L Vlieg; Ewout B Fanoy; Liselotte van Asten; Xiaobo Liu; Jun Yang; Eva Pilot; Paul Bijkerk; Wim van der Hoek; Thomas Krafft; Marianne A van der Sande; Qi-Yong Liu
Journal:  BMC Public Health       Date:  2017-05-08       Impact factor: 3.295

4.  Automated Classification of Online Sources for Infectious Disease Occurrences Using Machine-Learning-Based Natural Language Processing Approaches.

Authors:  Mira Kim; Kyunghee Chae; Seungwoo Lee; Hong-Jun Jang; Sukil Kim
Journal:  Int J Environ Res Public Health       Date:  2020-12-17       Impact factor: 3.390

5.  Forecasting ESKAPE infections through a time-varying auto-adaptive algorithm using laboratory-based surveillance data.

Authors:  Antonio Ballarin; Brunella Posteraro; Giuseppe Demartis; Simona Gervasi; Fabrizio Panzarella; Riccardo Torelli; Francesco Paroni Sterbini; Grazia Morandotti; Patrizia Posteraro; Walter Ricciardi; Kristian A Gervasi Vidal; Maurizio Sanguinetti
Journal:  BMC Infect Dis       Date:  2014-12-06       Impact factor: 3.090

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.