Published on in Vol 6, No 3 (2020): Jul-Sep

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18281, first published .
Potential Early Identification of a Large Campylobacter Outbreak Using Alternative Surveillance Data Sources: Autoregressive Modelling and Spatiotemporal Clustering

Potential Early Identification of a Large Campylobacter Outbreak Using Alternative Surveillance Data Sources: Autoregressive Modelling and Spatiotemporal Clustering

Potential Early Identification of a Large Campylobacter Outbreak Using Alternative Surveillance Data Sources: Autoregressive Modelling and Spatiotemporal Clustering

Journals

  1. Gupta A, Grannis S, Kasthurirathne S. Evaluation of a Parsimonious COVID-19 Outbreak Prediction Model: Heuristic Modeling Approach Using Publicly Available Data Sets. Journal of Medical Internet Research 2021;23(7):e28812 View
  2. 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
  3. Jing F, Li Z, Qiao S, Zhang J, Olatosi B, Li X. Using geospatial social media data for infectious disease studies: a systematic review. International Journal of Digital Earth 2023;16(1):130 View
  4. Morris R, Wang S. Building a pathway to One Health surveillance and response in Asian countries. Science in One Health 2024;3:100067 View

Books/Policy Documents

  1. Wagenaar J, Newell D, Kalupahana R, Mughini-Gras L. Zoonoses: Infections Affecting Humans and Animals. View
  2. Wagenaar J, Newell D, Kalupahana R, Mughini-Gras L. Zoonoses: Infections Affecting Humans and Animals. View