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Assessing current temporal and space-time anomalies of disease incidence.


Approaches used to early and accurately characterize epidemiologic patterns of disease incidence in a temporal and spatial series are becoming increasingly important. Cluster tests are generally designed for retrospective detection of epidemiologic anomalies in a temporal or space-time series. Timely identification of anomalies of disease or poisoning incidence during ongoing surveillance or an outbreak requires the use of sensitive statistical methods that recognize an incidence pattern at the time of occurrence. This report describes 2 novel analytical methods that focus on detecting anomalies of incidence at the time of occurrence in a temporal and space-time series. The first method describes the paucity of incidence at the time of occurrence in an ongoing surveillance and is designed to evaluate whether a decline in incidence occurs on the single current day or during the most recent few days. The second method provides an overall assessment of current clustering or paucity of incidence in a space-time series, allowing for several space regions. We illustrate the application of these methods using a subsample of a temporal series of data on the largest dengue outbreak in Taiwan in 2015 since World War II and demonstrate that they are useful to efficiently monitor incoming data for current clustering and paucity of incidence in a temporal and space-time series. In light of the recent global emergence and resurgence of Zika, dengue, and chikungunya infection, these approaching for detecting current anomalies of incidence in the ongoing surveillance of disease are particularly desired and needed.


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    Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan.


    Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan. School of Chinese Medicine, China Medical University, Taichung, Taiwan.

    Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.


    PloS one 12:11 2017 pg e0188065


    Models, Theoretical
    Retrospective Studies

    Pub Type(s)

    Journal Article



    PubMed ID