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

Tutorial (2) Public (e)Health, Digital Epidemiology and Public Health Informatics (1014) Surveillance Systems (514) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1141) Infectious Diseases (non-STD/STI) (1727) GIS (Geographic Information Systems) Applications in Public Health and Spatial Epidemiology (138) mHealth for Data Collection and Research (1203)

Published on in Vol 10 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/50653, first published .
Prospective Spatiotemporal Cluster Detection Using SaTScan: Tutorial for Designing and Fine-Tuning a System to Detect Reportable Communicable Disease Outbreaks

Prospective Spatiotemporal Cluster Detection Using SaTScan: Tutorial for Designing and Fine-Tuning a System to Detect Reportable Communicable Disease Outbreaks

Prospective Spatiotemporal Cluster Detection Using SaTScan: Tutorial for Designing and Fine-Tuning a System to Detect Reportable Communicable Disease Outbreaks

Authors of this article:

Alison Levin-Rector1 Author Orcid Image ;   Martin Kulldorff2 Author Orcid Image ;   Eric R Peterson1 Author Orcid Image ;   Scott Hostovich3 Author Orcid Image ;   Sharon K Greene1 Author Orcid Image
Article Authors Cited by (10) Tweetations (4) Metrics

Journals

  1. Martonik R, Oleson C, Marder E. Spatiotemporal Cluster Detection for COVID-19 Outbreak Surveillance: Descriptive Analysis Study. JMIR Public Health and Surveillance 2024;10:e49871 View
  2. Santana K, Marino A, Martins G, Lima P, Mendonça P, Rolnik R. Space–Time Analysis of the COVID-19 Pandemic and Its Relationship with Socioeconomic and Demographic Variables in the Metropolitan Region of São Paulo, Brazil. ISPRS International Journal of Geo-Information 2024;13(11):397 View
  3. Lee T, Kim J. Rapid spread and high prevalence of the pine wilt disease around wildfire areas. Trees, Forests and People 2025;20:100805 View
  4. Panova K, Marinov M, Nikolov B. Vocal Activity Rate of the Semi-Collared Flycatcher Ficedula semitorquata is Associated with Forest Features. Ardeola 2025;72(2) View
  5. Chen C, Chen D, Du Y, Jiang D, Cao K, Yang M, Wu X, Chen M, Zhou W, Qi J, You Y, Yan R, Yang S. Global patterns and trends in deaths of influenza-associated lower respiratory infections from 1990 to 2019. Epidemiology and Infection 2025;153 View
  6. Greene S, Latash J, Peterson E, Levin-Rector A, Luoma E, Wang J, Bernard K, Olsen A, Li L, Waechter H, Mattias A, Rohrer R, Kulldorff M. Applying prospective tree-temporal scan statistics to genomic surveillance data to detect emerging SARS-CoV-2 variants and salmonellosis clusters in New York City. International Journal of Epidemiology 2025;54(2) View
  7. Anjali , Kumar B. Spatiotemporal dynamics and policy impact on farmer suicides in Maharashtra, India. Scientific Reports 2025;15(1) View
  8. Punyapornwithaya V, Srisawang S, Arjkumpa O, Phimpraphai W, Buamithup N, Purevsuren B, Rinzin K, Abila R, Sutar A, Korennoy F. Investigating Transboundary Spread Patterns and Cluster Characteristics of Lumpy Skin Disease (LSD) Outbreaks in Asia: Levering the Outbreak Data (2019–2023) to Support the LSD Prevention and Control Strategies. Transboundary and Emerging Diseases 2025;2025(1) View
  9. Punyapornwithaya V, Srisawang S, Jainonthee C, Li W, Abila R, Rinzin K, Sutar A, Purevsuren B. Insights into the epidemiological characteristics, temporal changepoints, and space-time clusters of foot and mouth disease outbreaks in the SEACFMD region to support eradication efforts. Preventive Veterinary Medicine 2025;245:106683 View
  10. Qin K, Dai X, Wu L, Gao M. Spatiotemporal analysis and risk prediction of foodborne diseases based on meteorological risk factors: a case study of Wuxi city, China. BMC Infectious Diseases 2025;25(1) View