This paper is in the following e-collection/theme issue:

Surveillance Systems (462) Tools, Programs and Algorithms (266) Reviews on Public Health Technology and Innovation (69) Statistical Methods for Surveillance and Population Health (156)

Published on in Vol 6, No 2 (2020): Apr-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/11512, first published .
Cluster Detection Mechanisms for Syndromic Surveillance Systems: Systematic Review and Framework Development

Cluster Detection Mechanisms for Syndromic Surveillance Systems: Systematic Review and Framework Development

Cluster Detection Mechanisms for Syndromic Surveillance Systems: Systematic Review and Framework Development

Authors of this article:

Prosper Kandabongee Yeng1, 2 Author Orcid Image ;   Ashenafi Zebene Woldaregay1 Author Orcid Image ;   Terje Solvoll3 Author Orcid Image ;   Gunnar Hartvigsen1 Author Orcid Image
Article Authors Cited by (11) Tweetations (1) Metrics

Journals

  1. Woldaregay A, Launonen I, Årsand E, Albers D, Holubová A, Hartvigsen G. Toward Detecting Infection Incidence in People With Type 1 Diabetes Using Self-Recorded Data (Part 1): A Novel Framework for a Personalized Digital Infectious Disease Detection System. Journal of Medical Internet Research 2020;22(8):e18911 View
  2. Woldaregay A, Launonen I, Albers D, Igual J, Årsand E, Hartvigsen G. A Novel Approach for Continuous Health Status Monitoring and Automatic Detection of Infection Incidences in People With Type 1 Diabetes Using Machine Learning Algorithms (Part 2): A Personalized Digital Infectious Disease Detection Mechanism. Journal of Medical Internet Research 2020;22(8):e18912 View
  3. De Ridder D, Loizeau A, Sandoval J, Ehrler F, Perrier M, Ritch A, Violot G, Santolini M, Greshake Tzovaras B, Stringhini S, Kaiser L, Pradeau J, Joost S, Guessous I. Detection of Spatiotemporal Clusters of COVID-19–Associated Symptoms and Prevention Using a Participatory Surveillance App: Protocol for the @choum Study. JMIR Research Protocols 2021;10(10):e30444 View
  4. Tsang T, Huang X, Guo Y, Lau E, Cowling B, Ip D. Monitoring School Absenteeism for Influenza-Like Illness Surveillance: Systematic Review and Meta-analysis. JMIR Public Health and Surveillance 2023;9:e41329 View
  5. Dequeker S, Callies M, Catteau L, Int Panis L, Islamaj E, Klamer S, Latour K, Pauwels M, Vernemmen C, Mahieu R, Masson H, Savsin M, De Clercq E, Thomas M, Catry B, Vandael E. COVID-19 Clusters in Belgian Nursing Homes: Impact of Facility Characteristics and Vaccination on Cluster Occurrence, Duration and Severity. Viruses 2023;15(1):232 View
  6. Craig A, Leong R, Donoghoe M, Muscatello D, Mojica V, Octavo C. Comparison of statistical methods for the early detection of disease outbreaks in small population settings. IJID Regions 2023;8:157 View
  7. Morris R, Wang S. Building a pathway to One Health surveillance and response in Asian countries. Science in One Health 2024;3:100067 View
  8. Zhang L, Li M, Zhi C, Zhu M, Ma H. Identification of Early Warning Signals of Infectious Diseases in Hospitals by Integrating Clinical Treatment and Disease Prevention. Current Medical Science 2024;44(2):273 View
  9. Sim J, Pinto S, van Mourik M. Comparing automated surveillance systems for detection of pathogen-related clusters in healthcare settings. Antimicrobial Resistance & Infection Control 2024;13(1) View

Books/Policy Documents

  1. Glatman-Freedman A, Kaufman Z. Encyclopedia of Sustainability Science and Technology. View
  2. Glatman-Freedman A, Kaufman Z. Infectious Diseases. View