Background: Public health organizations have increasingly harnessed geospatial technologies for disease surveillance, health services allocation, and targeting place-based health promotion initiatives.
Methods: We conducted a systematic review around the theme of space-time clustering detection techniques for infectious diseases using PubMed, Web of Science, and Scopus. Two reviewers independently determined inclusion and exclusion.
Results: Of 2,887 articles identified, 354 studies met inclusion criteria, the majority of which were application papers. Studies of airborne diseases were dominant, followed by vector-borne diseases. Most research used aggregated data instead of point data, and a significant proportion of articles used a repetition of a spatial clustering method, instead of using a "true" space-time detection approach, potentially leading to the detection of false positives. Noticeably, most articles did not make their data available, limiting replicability.
Conclusion: This review underlines recent trends in the application of space-time clustering methods to the field of infectious disease, with a rapid increase during the COVID-19 pandemic.
Keywords: Cluster detection; Infectious disease surveillance; Scan statistics; Space-time; Spatial statistics.
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