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

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: The @choum Study Protocol (Preprint). JMIR Research Protocols 2021 View

Books/Policy Documents

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