Enhanced cluster detection and noise reduction for geospatial time series data of COVID-19
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-15901-0.pdf
Reference31 articles.
1. Balamchi S, Torabi M (2020) Spatial modeling of repeated events with an application to disease mapping. Spatial Stat 4:1–16. https://doi.org/10.1016/j.spasta.2020.100425
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3. Cordes J, Castro MC (2020) Spatial analysis of COVID-19 clusters and contextual factors in New York City. Spatial Spatio-Temporal Epidemiol 34:1–8. https://doi.org/10.1016/j.sste.2020.100355
4. Corizzo R, Ceci M, Japkowicz N (2019) Anomaly detection and repair for accurate predictions in geo-distributed Big Data. Big Data Res 16:18–35. https://doi.org/10.1016/j.bdr.2019.04.001
5. Desjardins MR, Hohl A, Delmelle EM (2020) Rapid surveillance of COVID-19 in the United States using a prospective space-time scan statistic: Detecting and evaluating emerging clusters. Appl Geogr 118:1–7. https://doi.org/10.1016/j.apgeog.2020.102202
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