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Parameters setting of spatial-temporal scan statistics in the study of hand-foot-and-mouth disease cluster / 公共卫生与预防医学
Journal of Public Health and Preventive Medicine ; (6): 49-52, 2020.
Artigo em Chinês | WPRIM | ID: wpr-837480
ABSTRACT
Objective To explore the optimal combination of parameters for the maximum spatial cluster size and maximum temporal cluster size of scan statistics. Methods The daily incidence data of hand-foot-and-mouth disease (HFMD) in Jingzhou in 2016 was collected as data source. The maximum spatial cluster sizes were set to 50%, 40%, 30%, 20%, and 10% of the population at risk. The maximum temporal cluster sizes were set to 7d, 14d, 30d, and 60d. A total of 20 parameter setting schemes were formed and spatial-temporal scanning was conducted one by one. The areas where the number of towns covered by the scanning area was less than 25 were selected, and the clustered epidemic of hand-foot-mouth disease can be detected at the same time in Xiejiaping Town of Songzi City and Sanzhou Town of Jianli County. The combination of large LLR and RR values was the optimal parameter setting. Results When the spatial windows were set to 20% of the population at risk, and the temporal windows were set to 30d, a total of 6 aggregation areas were detected. The number of covered townships was less than 25, and the clustered epidemic of Xiejiaping Township and Sanzhou Town were successfully detected. The LLR and RR values of the detected aggregation area were relatively large. This combination was the optimal parameter setting. Conclusion The combination of different parameters has a significant impact on the results of spatial-temporal scan statistics. It is recommended that parameters be optimized before applying this method.

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Índice: WPRIM (Pacífico Ocidental) Idioma: Chinês Revista: Journal of Public Health and Preventive Medicine Ano de publicação: 2020 Tipo de documento: Artigo

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Índice: WPRIM (Pacífico Ocidental) Idioma: Chinês Revista: Journal of Public Health and Preventive Medicine Ano de publicação: 2020 Tipo de documento: Artigo