epi info manual ebook

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epi info manual ebookCDC twenty four seven. Saving Lives, Protecting People Appropriate for small to medium size surveillance and response activities and special epidemiologic studies. Appropriate for distributed data collection in locations lacking IT infrastructure. Appropriate for large scale surveillance and response activities in locations with reliable network connectivity. Get custom support directly from the makers of the Epi Info Suite. Discover everything Scribd has to offer, including books and audiobooks from major publishers. Start Free Trial Cancel anytime. Report this Document Download Now Save Save Epi Info 7 Manual Draft For Later 0 ratings 0 found this document useful (0 votes) 603 views 364 pages Epi Info 7 Manual Draft Uploaded by Rodrigo Castro J Description: Full description Save Save Epi Info 7 Manual Draft For Later 0 0 found this document useful, Mark this document as useful 0 0 found this document not useful, Mark this document as not useful Embed Share Print Download Now Jump to Page You are on page 1 of 364 Search inside document Browse Books Site Directory Site Language: English Change Language English Change Language. VAT is added during checkout. Adicionar o Ebook ao Carrinho de Compras Adicionar a lista de desejos Sobre o Livro O software Epi Info 7 e parte integrante das ferramentas classicas dos epidemiologistas ou dos profissionais que queiram atuar com esses conhecimentos. Existem muitas opcoes de softwares para realizar as estatisticas que tanto procuramos na saude publica e varios nos atendem nos mais diferentes aspectos de elaboracao de um banco de dados eletronicos para realizar nossas avaliacoes, mas nao podemos deixar de lado o nosso Epi Info 7 que tanto contribuiu para o desenvolvimento da utilizacao da informatica na saude publica. Por isso, divulgar sua historia e como esse software pode nos ajudar a realizar esses projetos e uma das missoes dessa publicacao.http://auxerretv.com/content/public/dmv-spanish-manual.xml

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Queremos manter ativa a documentacao e as informacoes desse software para que ele continue sendo utilizado pelas novas geracoes e por qualquer um que queira manter aqueles seus ideias de desenvolvimento do projeto ativos e vivos. Compartilhe este livro Feedback Email the Author(s) Sobre o Autor guilherme camara Guilherme Ribeiro Camara Produtor cultural, Presidente do Instituto Cultural Joaquim Ribeiro Sadi e cineclube Sadi Ribeiro e exibidor de filmes apaixonado pela setima arte e o mundo dos livros. Professor de medicina ministra conteudos de semiologia,saude do adulto e idoso, medicina de familia e comunidade, internato de saude coletiva, internato de urgencia e emergencia. Especialista em Medicina Preventiva e Social - Residencia Medica Mestre em Saude Publica especialista em Cardiologista Atua como emergencista em servicos de urgencia e emergencia Autor de cursos on line e educacao a distancia. Table of Contents Nos processamos os reembolsos manualmente, entao eles podem demorar alguns dias para aparecer. Veja os termos completos. Free Updates. DRM Free. If you buy a Leanpub book, you get free updates for as long as the author updates the book. Many authors use Leanpub to publish their books in-progress, while they are writing them. All readers get free updates, regardless of when they bought the book or how much they paid (including free). Most Leanpub books are available in PDF (for computers), EPUB (for phones and tablets) and MOBI (for Kindle). The formats that a book includes are shown at the top right corner of this page. Finally, Leanpub books don't have any DRM copy-protection nonsense, so you can easily read them on any supported device. Learn more about Leanpub's ebook formats and where to read them Write and Publish on Leanpub Authors and publishers use Leanpub to publish amazing in-progress and completed ebooks, just like this one. You can use Leanpub to write, publish and sell your book as well.http://magnachip.com/userfiles/20200922195737.xml Leanpub is a powerful platform for serious authors, combining a simple, elegant writing and publishing workflow with a store focused on selling in-progress ebooks. Leanpub is a magical typewriter for authors: just write in plain text, and to publish your ebook, just click a button. It really is that easy. Learn more about writing on Leanpub Top Books View All. It was developed by the CDC (Centers for Diseases Control) to build databases for epidemiology with basic statistics, maps and graphics. Many users have developed applications in Epi Info to meet their needs, even when they need to collect information about animal or human health; depending on their experiences and expected results. It will show the information from the virtual health records graphically, created based on the fields that you prefer. The software uses the formats CHK, REC, QES for storing the data. Nowadays the software is required in multiple College Degrees for the management of epidemiology databases of different diseases, infection factors, vaccination and others. Using this information they use apply it for they practices and studies. However, if you are using Windows XP, you have to make sure you have SP3. If you don't, you will need to download it from Microsoft. Editor rating:Please add a comment explaining the reasoning behind your vote. Corresponding author. The Creative Commons Public Domain Dedication waiver( ) applies to the data made available in this article, unless otherwise stated. Associated Data Data Availability Statement Detailed installation instructions and a list of development dependencies can be found at. There is a readme.txt in the root of that directory. Abstract Background 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.https://hunam.mx/no-6253-diseno-web 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. Results We have successfully generated executables for Linux, Mac, and Windows from a single code-base, and we have shown that performance need not be completely sacrificed when building a cross-platform application. This has been accomplished by using Electron as a wrapper for an AngularJS app, a Python analytics module, and a local, browser-based NoSQL database. Conclusions Promising results warrant future research. The desktop software has undergone a number of revisions and is currently built upon the Windows operating system. First, a particular deficiency of the system that needs to be addressed is the software’s inability to run on Linux or Macs. A tool that is truly capable of contributing to the international community’s fight against infectious diseases should support as many operating systems and devices as possible. An open-source and cross-platform version of such software package will allow the developers from around the world to access, design and enhance Epi-Info.https://participativedemocracy.com/images/compliance-manual.pdf Second, having been under development for more than three decades, Epi-Info is now comprised of several separate applications, codebases and use-cases including desktop, mobile, web, and cloud. This has resulted in an unfortunate increase in development complexity. Outbreaks can often spread faster than engineers can keep up. It is not uncommon for new analytics components or data-collection tools to be requested by public health teams on the ground during highly active outbreak situations. This on-the-fly requirements specification and engineering can be difficult to manage together with complicated codebases. Third, the existing interfaces for offline data-collection and maintenance protocols are not altogether intuitive. The processes for importing or broadcasting between remote servers and local client machines may present steep learning curves for public health officials. 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 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. 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. One challenge here is finding a balance between flexibility, speed, and ease-of-use with respect to the form design process. Implementation We present a cross-platform system architecture which allows for intuitive form-design, data-collection, online and offline modes, automatic local-remote data sync, fast analytics, and scalable storage. The overall system architecture is shown in Fig. 1.This data is exposed as RESTful Web Services to the clients, and Multiple clients, each equipped with an AngularJS application that provides all the functionality of Epi-Info including form design, deployment of forms, data collection, and user dashboards, and on demand analytics. Each client stores its data in PouchDB, which is automatically synchronized the CouchDB on the server. All of the client is encapsulated with the Electron, a platform-independent application framework. Client Side Architecture To address the cross-platform system requirement, we use Electron as a wrap- per for an AngularJS front-end, a Python Analytics module, and an embedded NoSQL database called PouchDB. The database accessibility protocol was an important design consideration. PouchDB is a lightweight, browser-based NoSQL database which is designed to automatically sync with a remote Couch (Fig. 2 ) Database. However, the proper access point was not immediately obviously. As shown in Fig. 1, the PouchDB is accessed directly by the Angular front-end. Importantly, this configuration was chosen because the alternative approach, whereby the database is accessed directly by the Python Analytics module, would have required the use of a Python-PouchDB wrapper. The documentation for the wrapper is very light, and it has been much easier to use the original API’s for database interaction. Any data needed by the Python Analytics module can be requested and sent via a simple HTTP connection.When the Electron application is initiated, a child process is spawned which starts the Flask server, allowing access to the Python analytics module. This has proved successful and it has allowed us to seamlessly integrate Angular and Python in a single, local application. When an analytics requ-est is made, for example, the data is simply re-routed to the appropriate Python function via the HTTP connection. Python was chosen because of it’s popularity and platform-agnosticism. It is critical that researchers from around the world be allowed to contribute to this project in a timely way. This can be facilitated by offering a platform comprised of tools which are popular and universal. NoSQL and PouchDB NoSQL databases have been one of our primary areas of research to date. They are understood to scale extremely well because they are well-suited to provide a flexible structure to otherwise unstructured data. That fact has proven helpful when storing Epi-Form schemas. However, we have identified several other database-related issues which require careful consideration. It was necessary to choose an appropriate candidate to be embedded with our Electron Application. This is critical because larger NoSQL databases, like MongoDB, require different installation protocols for different operating systems. Recall once again, our primary objective is to be a platform-agnostic application that is extremely user-friendly, and very little effort to download and install. Thus, our approach has been to embed a lightweight NoSQL database within our Electron desktop application. After research, PouchDB was selected as the NoSQL database, and we consider it to be a viable option going forward. It’s robust documentation, community support, and seamless synchronization with CouchDB makes it very attractive. Furthermore, it is easy to embed, and can be interfaced directly with the Angular frontend. Specifically, PouchDB is designed to sync automatically with a remote Couch Database. This allows for seamless transition between online and offline modes, and guards against the potential for data-loss during transfer. As a result of this auto-sync, any underlying changes to data on local client machines can be automatically broadcast a centralized remote database, and subsequently on to any additional client machines. Furthermore, PouchDB provides a detailed change-log which identifies and explains any alterations in local data or data-structure. Analytics Module 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. It was important to precisely identify each point of data-transfer and manipulation in order to pinpoint any potential bottlenecks (Fig. 3, Table 1 ). We keep one copy in PouchDB, so that it may be available for automatic syncing with the central CouchDB. We keep another in a compressed format native to Python, called HDF5. Even on a slow machine, the read and write times for HDF5 are extremely fast, better even than SQL or CSV. Additionally, the excellent compression ratio means that even though we store the data twice, we increase the total storage-size requirement by less than 10. As shown in Table 1, the most costly processes, with respect to time, involve retrieving the data from PouchDB, sending the data to the analytics module, and converting the data to a useable DataFrame. This problem is exacerbated if the database is allowed to accumulate alot of data prior to carrying out these steps. Thus, it is possible to mitigate such effects by performing the operations iteratively, whenever new data is entered into the database. The compressed HDF5 DataFrame must be continuously maintained, allowing for immediate analytics requests at all times. Fortunately, PouchDB comes equipped witha change-log which offers a detailed explanation of any changes to the underlying data. This can be used to subsequently update the compressed DataFrame. The result is a system that would allow for very fast access to data and analytics which rivals even traditional approaches. Additional performance metrics are provided in the “ Results ” section of this paper. Form Designer The challenge associated with building a web-based form designer is derived from a need to balance flexibility with specificity. The current desktop form-designer provided by Epi-Info offers extreme precision, allowing form-creators the ability to define form elements on a pixel-by-pixel basis. The form-schemas are then stored as XML, and the exact positions of form elements are subsequently recorded. On the one hand, this is desirable because health form appearance often requires such acute attention to detail. On the other, this can cause a large increase in design time. With our web-based AngularJS form designer, we strike a balance between the two characteristics, offering users an acceptable level of precision while simultaneously expediting the form-design process with a flexible and intuitive interface (Fig. 4 ).Figure 5 shows a typical Epi-Info desktop workflow.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. Additionally, the use of multiple cores can further optimize the analytics module. This allows multiple analytics requests to be made on-the-fly as needed. Reports are sent back to the user-interface as those jobs are completed. That is, any single request need not wait for a previous job to finish as long as there is another core available for use on the machine (Fig. 7 ).However, numerous other frameworks, architectures, and configurations could potentially prove adequate. In this section, our reasons for favoring this system will be explained more thoroughly. 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. Central to the initial conceptualization of our design was the selection as Python as the language of choice for the Analytics Module. As mentioned, this was due in large part to its popularity among the scientific computing community and its platform agnostic quality. This ultimately factored greatly into the choice of a suitable cross-platform framework. Framework candidates which were discussed included Electron, Kivy, and.NET-Core. Electron and Kivy were selected for closer inspection due to language familiarity amongst the design team. Kivy is a cross-platform framework for developing Python apps. It runs on iOS, Linux, Windows, Android, and OSX; making it very attractive. However, it would have required time to become acquainted with the front-end framework provided by Kivy, as it does not rely on traditional web-technologies. Ultimately, this encouraged us to move towards Electron. Electron, as opposed to Kivy, allows engineers to work with familiar technologies which can be easily encapsulated in the framework. We feel that this should allow for increased flexibility, a reduction in development time, and greater ability to share components across applications. It is not clear, for example, that it would be easy to deploy the majority of a Kivy app to the web. However, the Electron app we have designed should be fairly easy to migrate. Additionally, there are mobile frameworks, such as Ionic, which would also facilitate the simple transfer of the majority of code to a mobile app. 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. Importantly, new technologies must first be examined and evaluated based on the quality of community support. Public-Health is a critical domain, and it would not be wise to imprudently experiment with untested and weakly-supported tools. This was discovered first hand by our researchers, particularly when tasked with choosing an acceptable local NoSQL database. Many newer, lightweight NoSQL databases have very shallow documentation and community support. ForerunnerDB was one such product which ultimately proved impractical. Development was drastically slowed whenever a small bug was found in the database code because the required documentation literally did not exist. Eventually, PouchDB was discovered, and we found it to have adequate support, greatly simplifying and expediting the development process. Conclusions Creative system design can alleviate many of the undesirable qualities typically associated with cross-platform frameworks, such as Electron. This can require a mix of languages, databases, and design patterns. The power of the resulting system has, in this case, proven to be worth the effort, successfully addressing many of the necessary system requirements. With our cross-platform framework design for Epi-Info, the international community will now have the tools to rapidly respond to an emergency outbreak, even under remote conditions. By designing a single code-base that is capable of generating executables for multiple platforms, developers can quickly provide customized components to those deployed. Our work towards optimizing the data analytics will enable better coordination and a more effective response to any outbreak around the globe. However, future research is still needed, such as the development and deployment of Epi-Info on mobile devices, including hand held tablets and smart phones. However, there are known issues with respect to installing electron on Ubuntu 16.04. Recommend Ubuntu 14 for development. Programming language(s): NodeJS, AngularJS, Python Other requirements: In the repository located at, the active branch is called ’dev’. The current software is located in the directory called ’electron-with-python’. There is a readme.txt in the root of that directory. Will not work out of the box on Ubuntu 16.04. License: MIT Any restrictions to use by non-acedemics: Not applicable. Funding Research was partially funded by the CDC under contract number 200-2016-91969. Publication costs are covered by the Department of Computer Science, Georgia State University. Availability of data and materials Detailed installation instructions and a list of development dependencies can be found at. There is a readme.txt in the root of that directory. About this supplement 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. Abbreviations CDC Centers for Disease Control and Prevention HDF5 Hierarchical Data Format (HDF) is a set of file formats (HDF4, HDF5) designed to store and organize large amounts of data NoSQL Non-relational database Authors’ contributions BC was responsible for project Management. He was fully involved in most aspects of system specifications, design, and development. AGB was the project lead and contributed substantially with respect to analysis of system performance. XC acted in a supervory role and offered networking expertise. RS acted in a supervisory role and contributed significantly during the system specification and design phase. JKM was the primary researcher responsible for data analytics. NR researched and selected the appropriate databases. JW was responsible for development of the form designer. All authors read and approved the final manuscript. Notes Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Exploring the role of gis during community health assessment problem solving: experiences of public health professionals. Articles from BMC Bioinformatics are provided here courtesy of BioMed Central. Please upgrade By clicking any link on this page you are giving your consent to our Privacy Policy and Cookies Policy.Epidemiologists can calculate sample sizes, collect data, and perform analysis using their smartphones or inexpensive tablets to investigate outbreaks, respond to emergencies, or conduct public health research in locations lacking IT infrastructure. Limitation of Liability. In no event shall the Centers for Disease Control and Prevention (CDC) or the United States (U.S.) Government be liable to the user or to anyone else for any direct, special, incidental, indirect or consequential damages of any kind, or any damages whatsoever, including without limitation, loss of profit, loss of use, savings or revenue, loss of data or the claims of third parties, whether or not CDC or the U.S. Government has been advised of the possibility of such loss, however caused and on any theory of liability, arising out of or in connection with the possession, use or performance of this software. Please try again.Please try again.Please try again. Epi Info allows the user to develop a questionnaire, customize the data entry process, enter data, and analyze the data. Statistics, graphs, tables, and maps can be produced with simple commands.Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required. Register a free business account To calculate the overall star rating and percentage breakdown by star, we don’t use a simple average. Instead, our system considers things like how recent a review is and if the reviewer bought the item on Amazon. It also analyzes reviews to verify trustworthiness. Please try again later. Ron King 5.0 out of 5 stars I recommend this book to every students, professors, nurses, and other public health officials. I did enjoy using this book to learn about the EPI INFO software. Very easy to use material. It generates surveys comprised of multiple questions, 2 x 2 tables, matched-pair case-control studies, etc. Automated analysis routines are available. The program automatically creates a database from the questionnaire and allows users to enter new data, modify existing data or search for records.