T
Tim Churches
Pre-alpha versions of several Pythonic tools for epidemiology and public
health practice are now available under a slightly modified version of
the Mozilla Public License - see http://www.netepi.org for further
details. Some screenshots and copies of README files and end-user
documentation are available from the project page on SourceForge (follow
the link on the above page).
Development of these tools was motivated by concerns over bioterrorism,
and by the global SARS outbreak in early 2003, but they have a wide
range of potential uses in population health epidemiology, in disease
outbreak investigation and management, and in other areas of public
health practice (including management of public health problems in
post-disaster situtaions).
Currently the tools are functional but lack important features and
require more testing (and more unit and functional tests) before they
can be used routinely. It is hoped the the tools can be brought to
"production-ready" status during 2005.
The main tools are:
- NetEpi Case Manager: this is a tool for securely collecting structured
information about cases and contacts of diseases (and other conditions)
of public health importance, through Web browsers and the Internet. New
data collection forms can be designed and deployed quickly by
epidemiologists, using a "point-and-click" interface, without the need
for knowledge of or training in any programming language (not even
Python). Data can then be collected from users of the system, who can be
located anywhere in the world, into a centralised database. All that is
needed by users of the system is a relatively recent Web browser and an
Internet connection. In some respects, NetEpi Case Manager is like a
Web-enabled version of the data entry facilities in the very popular Epi
Info suite of programmes provided in various forms since the mid-1980s
by the United States Centers for Disease Control and Prevention.
However, NetEpi Case Manager aims to provide additional facilties to
make it easier to administer a rapidly-deployable, widely-distributed
data collection system which can scale to handle hundreds of thousands
of cases and contacts if required.
- NetEpi Analysis: this is a tool for conducting epidemiological
analysis of data sets, both large and small, either through a Web
browser interface, or via a Pythonic programmatic interface. In some
respects it is similar to the analysis facilities included in Epi Info,
except that NetEpi Analysis is primarily designed to be installed on
servers and accessed remotely via Web browsers (or other remote
procedure call or web service mechanisms), rather than being installed
on individual desktop or laptop computers (although it can and does run
nicely on Linux and Apple Mac OS X desktops and laptops, and on
Microsoft Windows machines in future versions).
Contributions by experienced Python programmers to the further
development of these tools are welcome - please use the mailing list
(details on the project home page at http://www.netepi.org) for initial
contact.
Also many thanks to the Python development team, and to the developers
of the various Python packages which these tools make use of -
particularly the Albatross web application framework, the eGenix
MxDateTime module, the pyPGSQL DB-API package, Numeric Python, and the
RPy package which interfaces Python with R (and thanks to the R
development team for the R statsistics package, of course).
Tim C
Sydney, Australia
health practice are now available under a slightly modified version of
the Mozilla Public License - see http://www.netepi.org for further
details. Some screenshots and copies of README files and end-user
documentation are available from the project page on SourceForge (follow
the link on the above page).
Development of these tools was motivated by concerns over bioterrorism,
and by the global SARS outbreak in early 2003, but they have a wide
range of potential uses in population health epidemiology, in disease
outbreak investigation and management, and in other areas of public
health practice (including management of public health problems in
post-disaster situtaions).
Currently the tools are functional but lack important features and
require more testing (and more unit and functional tests) before they
can be used routinely. It is hoped the the tools can be brought to
"production-ready" status during 2005.
The main tools are:
- NetEpi Case Manager: this is a tool for securely collecting structured
information about cases and contacts of diseases (and other conditions)
of public health importance, through Web browsers and the Internet. New
data collection forms can be designed and deployed quickly by
epidemiologists, using a "point-and-click" interface, without the need
for knowledge of or training in any programming language (not even
Python). Data can then be collected from users of the system, who can be
located anywhere in the world, into a centralised database. All that is
needed by users of the system is a relatively recent Web browser and an
Internet connection. In some respects, NetEpi Case Manager is like a
Web-enabled version of the data entry facilities in the very popular Epi
Info suite of programmes provided in various forms since the mid-1980s
by the United States Centers for Disease Control and Prevention.
However, NetEpi Case Manager aims to provide additional facilties to
make it easier to administer a rapidly-deployable, widely-distributed
data collection system which can scale to handle hundreds of thousands
of cases and contacts if required.
- NetEpi Analysis: this is a tool for conducting epidemiological
analysis of data sets, both large and small, either through a Web
browser interface, or via a Pythonic programmatic interface. In some
respects it is similar to the analysis facilities included in Epi Info,
except that NetEpi Analysis is primarily designed to be installed on
servers and accessed remotely via Web browsers (or other remote
procedure call or web service mechanisms), rather than being installed
on individual desktop or laptop computers (although it can and does run
nicely on Linux and Apple Mac OS X desktops and laptops, and on
Microsoft Windows machines in future versions).
Contributions by experienced Python programmers to the further
development of these tools are welcome - please use the mailing list
(details on the project home page at http://www.netepi.org) for initial
contact.
Also many thanks to the Python development team, and to the developers
of the various Python packages which these tools make use of -
particularly the Albatross web application framework, the eGenix
MxDateTime module, the pyPGSQL DB-API package, Numeric Python, and the
RPy package which interfaces Python with R (and thanks to the R
development team for the R statsistics package, of course).
Tim C
Sydney, Australia