A powerful tool for automating the early detection of disease
             outbreaks in time series data. 'aeddo' employs advanced statistical 
             methods, including hierarchical models, in an innovative manner to 
             effectively characterize outbreak signals. It is particularly useful 
             for epidemiologists, public health professionals, and researchers 
             seeking to identify and respond to disease outbreaks in a timely 
             fashion. For a detailed reference on hierarchical models, consult 
             Henrik Madsen and Poul Thyregod's book (2011), ISBN: 9781420091557.

Package to incorporate change point analysis in ARIMA forecasting.

A powerful tool for automating the early detection of seasonal epidemic
             onsets in time series data. It offers the ability to estimate growth rates
             across consecutive time intervals, calculate the sum of cases (SoC) within
             those intervals, and estimate seasonal onsets within user defined seasons.
             With use of a disease-specific threshold it also offers the possibility to
             estimate seasonal onset of epidemics.
             Additionally it offers the ability to estimate burden levels for seasons
             based on historical data. It is aimed towards epidemiologists,
             public health professionals, and researchers seeking to identify and respond
             to seasonal epidemics in a timely fashion.

We extend existing gene enrichment tests to perform adverse
    event enrichment analysis. Unlike the continuous gene expression data,
    adverse event data are counts. Therefore, adverse event data has many zeros
    and ties. We propose two enrichment tests. One is a modified Fisher's exact
    test based on pre-selected significant adverse events, while the other is
    based on a modified Kolmogorov-Smirnov statistic. We add Covariate
    adjustment to improve the analysis."Adverse event enrichment tests using
    VAERS" Shuoran Li, Lili Zhao (2020) <doi:10.48550/arXiv.2007.02266>.