NetEpi, which is short for Inter Net -enabled Epi demiology, is a suite of free, open source software tools for epidemiology and public health practice which make full use of the Internet.
Public health is an area of endeavour concerned with protecting and improving the health of sub-groups in the population (or the entire population), rather than with the care of individual patients.
Epidemiology is the study of factors affecting the health and illness of populations, and serves as the foundation of almost all modern public health health practice.
There are currently two main NetEpi applications:
NetEpi Collection
NetEpi Collection is a data collection and data management tool for use in communicable disease outbreaks and other epidemiological investigations and studies. It is written in Python and uses only open-source software components and infrastructure, including the PostgreSQL database. Development commenced in 2003 during the global SARS epidemic and has continued episodically since then. Version 1.0, suitable for production use, was released in December 2007. In many respects, NetEpi Collection is rather like the popular Epi Info tool provided by the US Centers for Disease Control and Prevention, but unlike Epi Info, NetEpi is designed for use on the World Wide Web, and is not just for computers running the Microsoft MS-DOS or Microsoft Windows operating systems. NetEpi Collection also incorporates distributed task management functions needed to deal with large public health emergencies such as a human influenza pandemic.
NetEpi Analysis
NetEpi Analysis is a tool for interactive, exploratory data analysis of large population health (and possibly clinical) data sets - "large" meaning in the range of 10 to 100 million records, although, of course, it also works well with smaller data sets. This tool is also written primarily in Python and also makes use of Numeric Python (NumPy) and the open-source R statistical environment. NetEpi Analysis uses a simple but somewhat novel approach to data filtering, reduction and summarisation, involving an object-oriented implementation of fast set operations on sorted inverse ordinal mappings of vertically-disaggregated dataset columns (described in a paper, a copy of which is available on request from its author). Both Web browser and programmatic (Python API) interfaces are provided.
