Utility of Spatial Point-Pattern Analysis Using Residential and Workplace Geospatial Information to Localize Potential Outbreak Sources

• Published in: American journal of epidemiology Vol. 188; no. 5; pp. 940 - 949
• Main Authors: Chua, Jonathan L, Ng, Lee Ching, Lee, Vernon J, Ong, Marcus E H, Lim, Er Luen, Lim, Hoon Chin Steven, Ooi, Chee Kheong, Tyebally, Arif, Seow, Eillyne, Chen, Mark I-Cheng
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 01.05.2019; Oxford Publishing Limited (England)
• Subjects: Adolescent; Adult; Aged; Aged, 80 and over; Algorithms; Confidence; Control methods; Disease Outbreaks - statistics & numerical data; Epidemiologic Methods; Female; Humans; Identification methods; Male; Middle Aged; Neighborhoods; Outbreaks; Parameters; Pattern analysis; Practice of Epidemiology; Residence Characteristics - statistics & numerical data; Residential areas; Sensitivity analysis; Sentinel Surveillance; Spatial Analysis; Spatial distribution; Statistical analysis; Workplace - statistics & numerical data; Young Adult; algorithms; spatial analysis; source of outbreak; geographic information systems; source localization; workplace; food-borne diseases; infectious disease outbreaks
• DOI: 10.1093/aje/kwy290
• ISSN: 0002-9262

Abstract Identifying the source of an outbreak facilitates its control. Spatial methods are not optimally used in outbreak investigation, due to a mix of the complexities involved (e.g., methods requiring additional parameter selection), imperfect performance, and lack of confidence in existing options. We simulated 30 mock outbreaks and compared 5 simple methods that do not require parameter selection but could select between mock cases’ residential and workplace addresses to localize the source. Each category of site had a unique spatial distribution; residential and workplace address were visually and statistically clustered around the residential neighborhood and city center sites respectively, suggesting that the value of workplace addresses is tied to the location where an outbreak might originate. A modification to centrographic statistics that we propose—the center of minimum geometric distance with address selection—was able to localize the mock outbreak source to within a 500 m radius in almost all instances when using workplace in combination with residential addresses. In the sensitivity analysis, when given sufficient workplace data, the method performed well in various scenarios with only 10 cases. It was also successful when applied to past outbreaks, except for a multisite outbreak from a common food supplier.