Response to public health threats occurs on different timescales and requires a suite of different types of analytical tools. For current public health crises and emerging epidemics, statistical analysis of biomedical data typically requires an initial exploratory stage where one looks for potential trends and associations visually before carrying out rigorous hypothesis testing. This stage is of great importance in research as well as in medical applications, since it complements the expert's intuition with various representations of the raw data and its underlying dependency structures. The insights gained during data exploration, as well as the patterns missed due to shortcomings in the visualization, might set specific directions, for better or worse, during later model building or decision-making. While """"""""dashboard"""""""" software tools for multivariate data visualization exist, both commercial [1] and free [2, 3], they appear to be of seldom use in theoretical and applied epidemiology. On one hand, researchers often have highly customized software protocols already in place in order to carry out sophisticated statistical analysis. Adding a visual stage to these protocols in a way that is non-obtrusive and complementary to the existing tools is not a simple problem. On the other hand, medical doctors actively engaged in fieldwork might not have the technical expertise nor the time to install and use complex visualization packages. Furthermore, integration of these packages with the programs that physicians and other health care specialists typically use for routine statistical tasks such as hypothesis testing and linear regression (Excel, SAS, SPSS, etc.) is not easily accessible.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
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