ArticleMMWR. Morbidity and mortality weekly report2020
Salmonellosis Outbreak Detected by Automated Spatiotemporal Analysis - New York City, May-June 2019.
Article in MMWR. Morbidity and mortality weekly report, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 20 citations in OpenAlex.
- Detection of Acute Hepatitis C Outbreaks in California Using SaTScan: Statewide Spatiotemporal Analysis.JMIR public health and surveillance · 2026Article
- CalCORVID: a dynamic RShiny dashboard approach to visualize spatiotemporal clusters for public health surveillance.BMC public health · 2026Article
- From infectious diseases to chronic diseases: the paradigm shift of spatial epidemiology in disease prevention and control.Frontiers in public health · 2025Review
- Spatiotemporal Cluster Detection for COVID-19 Outbreak Surveillance: Descriptive Analysis Study.JMIR public health and surveillance · 2024Article
- DODGE: automated point source bacterial outbreak detection using cumulative long term genomic surveillance.Bioinformatics (Oxford, England) · 2024Article
- Prospective Spatiotemporal Cluster Detection Using SaTScan: Tutorial for Designing and Fine-Tuning a System to Detect Reportable Communicable Disease Outbreaks.JMIR public health and surveillance · 2024Article
- Geographical distribution and space-time clustering of human illnesses with majorEpidemiology and infection · 2022Article
- A Broad Safety Assessment of the Recombinant Herpes Zoster Vaccine.American journal of epidemiology · 2022Article
- Geospatial Transmission Hotspots of Recent HIV Infection - Malawi, October 2019-March 2020.MMWR. Morbidity and mortality weekly report · 2022Article
- Article
- Genome Sequencing Identifies Previously Unrecognized Klebsiella pneumoniae Outbreaks in Neonatal Intensive Care Units in the Philippines.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2021Article
- Detecting COVID-19 Clusters at High Spatiotemporal Resolution, New York City, New York, USA, June-July 2020.Emerging infectious diseases · 2021Article
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Authors and funding
13 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In May 2019, the New York City Department of Health and Mental Hygiene (NYCDOHMH) detected an unusual cluster of five salmonellosis patients via automated spatiotemporal analysis of notifiable diseases using free SaTScan software (1). Within 1 day of cluster detection, graduate student interviewers determined that three of the patients had eaten prepared food from the same grocery store (establishment A) located inside the cluster area. NYCDOHMH initiated an investigation to identify additional cases, establish the cause, and provide control recommendations. Overall, 15 New York City (NYC) residents with laboratory-diagnosed salmonellosis who reported eating food from establishment A were identified. The most commonly consumed food item was chicken, reported by 10 patients. All 11 clinical isolates available were serotyped as Salmonella Blockley, sequenced, and analyzed by core genome multilocus sequence typing; isolates had a median difference of zero alleles. Environmental assessments revealed food not held at the proper temperature, food not cooled properly, and potential cross-contamination during chicken preparation. Elevated fecal coliform counts were found in two of four ready-to-eat food samples collected from establishment A, and Bacillus cereus was detected in three. The outbreak strain of Salmonella was isolated from one patient's leftover chicken. Establishing automated spatiotemporal cluster detection analyses for salmonellosis and other reportable diseases could aid in the detection of geographically focused, community-acquired outbreaks even before laboratory subtyping results become available.
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