Epi Info Software For Mac

If you mean the "Epi Info" from the Centers for Disease Control, that appears to be available only in Windows and MS-DOS versions. To run it on your Mac, you would need to acquire one of the packages that allow Windows software to run on a Mac, such as the commercial Parallels, VMWare Fusion, or Crossover, or one of the free ones like Wine. (I have never tried Epi Info, even on an actual Windows computer, so I could not tell you which of these might handle it well.)

The Epi-Info software suite, built and maintained by the Centers for Disease Control and Prevention (CDC), is widely used by epidemiologists and public health researchers to collect and analyze public health data, especially in the event of outbreaks such as Ebola and Zika. As it exists today, Epi-Info Desktop runs only on the Windows platform, and the larger Epi-Info Suite of products consists of separate codebases for several different devices and use-cases. Software portability has become increasingly important over the past few years as it offers a number of obvious benefits. These include reduced development time, reduced cost, and simplified system architecture. Thus, there is a blatant need for continued research. Specifically, it is critical to fully understand any underlying negative performance issues which arise from platform-agnostic systems. Such understanding should allow for improved design, and thus result in substantial mitigation of reduced performance. In this paper, we present a viable cross-platform architecture for Epi-Info which solves many of these problems.

In this work, we propose and implement a new cross-platform architecture for Epi-Info software suite, which can simplify the codebases, expedite the development process and incorporate open-source techniques for flexible interfaces. The proposed architecture adopts the Electron [7] as the cross-platform framework to achieve significant reduction in development time and cost. The open-source techniques in NoSQL and Python are also introduced into Epi-Info. NoSQL, as a viable database option, can scale extremely well and provide a flexible structure to otherwise unstructured data. Python [8] has emerged as a very popular languages for data analytics and becomes the coding language of choice for many in the science community. Its robust statistical libraries and machine learning frameworks make it a suitable choice for Epi-Info. In addition, the ease-of-use and platform universality from Python can greatly reduce the development time of any new modules in the event of some emergencies or outbreak.

Epi-Info is essentially data-collection and analytics software. Consequently, the analytics module is perhaps the most crucial component, and the primary objective was to increase speed and efficiency. In the following paragraphs, we outline an approach which successfully mitigates the negative effects often found in cross-platform and NoSQL systems.

By incorporating the use of a compressed HDF5 DataFrame, we have successfully demonstrated that we can expedite the analytics cycle, thus mitigating many of the negative effects typically associated with cross-platform or NoSQL applications. For a dataset with 50,000 records and 200 columns, the software can read the data, perform a user- defined 10-variable multiple logistic regression, and report the results in under 2 s, even on modest machines.

The research conducted during the course of this project resulted in discussions with several additional research groups. Of particular note, was a collaborative multi-day meeting with a team from the University of Brasilia and representatives from the Itaipu Bi-National Energy Plant. During the meeting, a consensus was articulated which highlighted the need for greater amounts of international standardization and collaboration as separate nations and organizations seek to fight the spread of infectious diseases, particularly with respect to the technology involved. On that front, there was additional agreement that there are two domains where this is particularly important: data standardization, and software standardization.

The standardization of data is a challenging task, but progress has been made thanks in part to Health Level Seven International (HL7). Recently they have published a standard for public-health data knows as the FHIR, and it is currently being incorporated into various software tools around the planet. There seems to be less cohesion, however, on the software standardization front. This can be attributed to the enormous amounts of specific use-cases, location-specific needs, and a disjoint international community of engineers. Indeed, the CDC often plays a leadership role in many areas of the world in the event of outbreaks. Nevertheless, there are countless other organizations, such as Itaipu, which each have separate teams building unique tools to combat specific regional problems. Consequently, it appears there is a fair amount of redundancy with respect to functionality and code. This problem is likely to persist without the oversight of an international standardizing orginization. However, it is possible that the problem could be mitigated, even slightly, by the use of broadly-adopted and flexible technologies. When appropriate, generality and popularity should be favored.

We were greatly encouraged by the technological similarity presented by the group from Itaipu. Like us, they use a combination of AngualarJS and Python. Their current app, however, is entirely web-based, yet they have a need for offline capability. Because we both are using highly flexible, similar technologies, there is a real opportunity for collaboration and outright code-sharing. We feel it would be easy to extend their application, wrap it in an electron framework with an embedded NoSQL database, and allow for a robust offline use-case. This would simply not be possible if each group were not using such widely adopted technologies, and it shows the power and need for additional software standardization.

This article has been published as part of BMC Bioinformatics Volume 19 Supplement 11, 2018: Proceedings from the 6th Workshop on Computational Advances in Molecular Epidemiology (CAME 2017). The full contents of the supplement are available online at -19-supplement-11.

The methodology was updated considering the complex sample design of most of the recent surveys, compared to the methodology used in Anthro software (below). Improvements affect only measures of accuracy around estimates (i.e. standard errors and confidence intervals). The tool is based on R, with Shiny package.

Outputs include a set of z-scores, a file with prevalence estimates by the various stratification variables following the format in the expanded database, a report template on data quality assessments and a summary report with a template to be filled in with basic required survey information and ready-to-use graphics and tables depicting survey analysis results.

To download the software and respective manuals click on the links below. We recommend users to study first the manual, in particular the description of the requirements and installation (e.g.,.NET framework required before installing the software).

It includes functions to calculate z-scores and prevalence estimates (and CIs), and z-score summary statistics (and CIs) based on methodology recommended and described in the guide document jointly released by WHO and UNICEF Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old. It provides results for the indicators: length/height-for-age, weight-for-age, weight-for-length, weight-for-height, body mass index-for-age, head circumference-for-age, arm circumference-for-age, triceps skinfold-for-age and subscapular skinfold-for-age. The package is available in the CRAN repository at -project.org/package=anthro.

Should you encounter problems with the download due to slow internet connection, please send your contact details to the address below and we shall mail you a CD ROM including the software and manuals.

To report any software or macro problems please make a brief bug report containing the details on issue (product, kind of problem found, whether it appeared systematically or randomly, computer system, etc.). Send this brief report including the error log file to the following email address: [email protected] with the informative subject. Please note: This is not a helpline address.

What is EpiData Software ?- See General flyer (250kb pdf) SPC flyer (175kb pdf)A field guide including field epidemiology examples, a guide to create an SPC Control Chart in 5 minutes, further notes and examples are available. New versions released !! The new xml file format based system has been released, see the specific mail with information and get the software fromthe download page

Strategy. Since year 2000 EpiData Software has developed from securing the principles of Epi Info V6 to an independent documentation oriented system with several translations and numerous downloads. To secure continued viability organisations and governments working outside low-income countries are expected to assist with funding or other support for development and maintenance of software. A full conversion plan to secure this and convert the software to open-source has been made .

You can access the following software from any workstation using Virtual Desktop. Some software is also available off campus.Virtualdesktop.uiowa.edu, log on with your Iowa HawkID and password

Epi Info 7 is developed by Centers for Disease Control and Prevention (CDC). The most popular version of this product among our users is 3.5. The name of the program executable file is Launch Epi Info 7.exe. The product will soon be reviewed by our informers.

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