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)
Specialized Center--Cooperative Agreements (U54)
Project #
Application #
Study Section
Special Emphasis Panel (ZGM1-BBCB-5 (MI))
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Harvard University
United States
Zip Code
Freeman, G; Ng, S; Perera, R A P M et al. (2015) Multivariate analysis of factors affecting the immunogenicity of trivalent inactivated influenza vaccine in school-age children. Epidemiol Infect 143:540-9
Chang, Hsiao-Han; Hartl, Daniel L (2015) Recurrent bottlenecks in the malaria life cycle obscure signals of positive selection. Parasitology 142 Suppl 1:S98-S107
Wu, Peng; Fang, Vicky J; Liao, Qiuyan et al. (2014) Responses to threat of influenza A(H7N9) and support for live poultry markets, Hong Kong, 2013. Emerg Infect Dis 20:882-6
Tsang, Tim K; Cauchemez, Simon; Perera, Ranawaka A P M et al. (2014) Association between antibody titers and protection against influenza virus infection within households. J Infect Dis 210:684-92
Cowling, Benjamin J; Chan, Kwok-Hung; Feng, Shuo et al. (2014) The effectiveness of influenza vaccination in preventing hospitalizations in children in Hong Kong, 2009-2013. Vaccine 32:5278-84
Grad, Yonatan H; Kirkcaldy, Robert D; Trees, David et al. (2014) Genomic epidemiology of Neisseria gonorrhoeae with reduced susceptibility to cefixime in the USA: a retrospective observational study. Lancet Infect Dis 14:220-6
Freeman, Guy; Cowling, Benjamin J (2014) Serological responses following influenza A(H7N9) virus infection. J Infect Dis 209:2018-9
Johnson, Steven R; Grad, Yonatan; Ganakammal, Satishkumar Ranganathan et al. (2014) In Vitro selection of Neisseria gonorrhoeae mutants with elevated MIC values and increased resistance to cephalosporins. Antimicrob Agents Chemother 58:6986-9
Worby, Colin J; Chang, Hsiao-Han; Hanage, William P et al. (2014) The distribution of pairwise genetic distances: a tool for investigating disease transmission. Genetics 198:1395-404
Cowling, Benjamin J; Ip, Dennis K M; Fang, Vicky J et al. (2014) Modes of transmission of influenza B virus in households. PLoS One 9:e108850

Showing the most recent 10 out of 186 publications