SOLICITATION NOTICE
D -- Collaborative Advanced Analytics & Data Sharing (CAADS) Software. - JOFOC
- Notice Date
- 7/26/2016
- Notice Type
- Presolicitation
- NAICS
- 541511
— Custom Computer Programming Services
- Contracting Office
- Department of Health and Human Services, Centers for Disease Control and Prevention, Procurement and Grants Office (Atlanta), 2920 Brandywine Road, Room 3000, Atlanta, Georgia, 30341-4146
- ZIP Code
- 30341-4146
- Solicitation Number
- 2016-Q-65123
- Archive Date
- 8/13/2016
- Point of Contact
- Nancy Khalil, Phone: 7704882070
- E-Mail Address
-
kuj2@cdc.gov
(kuj2@cdc.gov)
- Small Business Set-Aside
- N/A
- Description
- JOFOC Date: 0726 Year: 16 Zip: 30341 Centers for Disease Control and Prevention (CDC); 2920 Brandywine Road Atlanta, GA 30341 Contact: Nancy Khalil, Contract Specialist, nmkhalil@cdc.gov FAR, Subpart 5.204-NOTICE OF INTENT TO SOLE SOURCE The Centers for Disease Control and Prevention (CDC), National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (NCHHSTP) intends to solicit a single source and award a firm-fixed price purchase order to Lockheed Martin Services Inc., 700 North Frederick Ave Gaithersburg, MD 20879-3328, in accordance with FAR Part 12. The purpose of this procurement is to acquire Collaborative Advanced Analytics & Data Sharing (CAADS) Software. The CDC's ability to achieve real-time surveillance and advanced molecular detection of disease transmission hinges on our ability to quickly and easily integrate data from novel laboratory methods and traditional epidemiologic sources. Quick and easy characterization of transmission clusters will afford subject matter experts at CDC, as well as those with intimate knowledge of a particular outbreak, the ability to discern geospatial, demographic and behavioral risk patterns specific to an outbreak. This software will improve outbreak detection, but more importantly, allow for the development of community-tailored intervention strategies providing significant public health benefits at the national, state, and local levels. No prior acquisition information. This is a new purchase. We are developing a bioinformatics pipeline to infer, analyze, and visualize microbial transmission networks for bacterial and viral pathogens in each of the four divisions (DHAP, DSTDP, DTBE, DVH) in our center (NCHHSTP). The software allows joining of very large datasets, including partial or whole genomes for each pathogen with the associated epidemiological data to infer, characterize, and analyze transmission networks. This new project is funded by the Advanced Molecular Detection (AMD) Initiative. Currently, we can achieve the necessary data science, exploration, and visualization techniques on only a very small scale using existing tools. However, these abilities require specialized and novel skills that involve the use of highly specialized software and in-house script development by skilled programmers. A large team of programmers and bioinformaticians would be necessary to scale our analytic capacity such that the methods (1) be applied to any pathogen, (2) any transmission cluster, (3) extremely large datasets, (4) and are accessible to subject matter experts and (5) local health professionals, and (6) which exceeds our project budget. Hence, we define the following requirements of our project that must be met by existing a commercial off the shelf software package. The software suite must demonstrate compatibility with our HPC environment and stringent network requirements of the CDC. The authentication system should be unified such that a user need only supply a single login credential in order to access all of the suite's services, preferably through integration with lightweight directory access protocol (LDAP). The system should be capable of encryption of data while in motion and at rest to protect data privacy and ensure mission integrity and the reputation of the agency. The system should allow for scalable and parallelized ingestion of large datasets (ranging from giga- to petabytes in size) that can be ingested from raw sources (unstructured, semi-structured and structured) and existing databases. This ingestion process should not require any programming expertise and be accessible through a point-and-click, web-based interface. During ingestion, the software should provide visualizations (such as histograms and statistical summaries) of each column in the data. The software should guide the user via machine learning to generate a data cleaning script that can be run in parallel on a compute cluster. Script generation and maintenance should not require programming expertise. That script should be sharable in plain text to facilitate the transformation step or process for later use or sharing with other data scientists for reproduction of the transformation (both within and outside the CDC). The software should also include a point-and-click work flow design component that include, but are not limited to, the ability to (1) join and transform data via dropdown menus and check boxes (2) statistical summaries (3) predictive analytic and machine learning tools to aid decision making [these include k-means clustering, decision trees, linear dirichlet allocation (LDA), support vector machine modeling, naïve Bayes predictors, principle component analysis (PCA), linear and logistic regression, neural networks, receiver operating curves (ROC), and various other analytic methods] as well as tooltips and online instructions to support users with selecting and applying these tools. The outputs of elements of the work flow should contain visualizations of the results obtained during that analytical step that can be exported as images for easy dissemination and sharing. The system should provide limited collaborator access to visualize the results of each analytical step without the ability to modify the work flow. The software will also include a network visualization/animation component that renders networks-based on static network data, but also builds, visualizes, and analyzes novel network representations of that data using a method called data discovery link-analysis. The network visualization component should be fully customizable with respect to the raw data and aggregate functions applied to the raw data to allow for simple re-characterization of a transmission network to investigate new hypotheses in real-time. Therefore, the suite requires the ability to dynamically model data based on graphical user interface interaction. It should also include an automatic recognition of data types, such as with dates and/or times, to generate time-series visualizations and animations. The network visualization component should also include the functionality to save the analytics and visualizations as a template for sharing with other data analysts to re-use without having to repeat the same work flow and analytics. The software should also support the integration of geographical information system (GIS) tools and data into the data visualization component. This integration must leverage the extensive investment and experience the agency has in the Environmental Systems Research Institute (ESRI) suite for GIS and geodatabase analyses. The software also has to be modular such that as better tools for the project are identified they can replace or add to the existing components. Only one responsible source and no other supplies or services will satisfy agency requirements. The associated NAICS Code is 541511. The Classification Code is D318 for IT and Telecom-Integrated Hardware/Software. This is not a solicitation and proposals are not requested. Interested organizations may submit their capabilities/qualifications statement to perform the effort, in writing to Mrs. Nancy Khalil, via email at nmkhalil@cdc.gov with "Solicitation Number 2016-Q-65123" referenced in the subject line, no later than 10:00am EST on Friday, July 29, 2016. Disclaimer and Important Notes. This notice does not obligate the Government to award a contract or otherwise pay for the information provided in response. The Government reserves the right to use information provided by respondents for any purpose deemed necessary and legally appropriate. Any organization responding to this notice should ensure that its response is complete and sufficiently detailed to allow the Government to determine the organization's qualifications to perform the work. Respondents are advised that the Government is under no obligation to acknowledge receipt of the information received or provide feedback to respondents with respect to any information submitted. After a review of the responses received, a pre-solicitation synopsis and solicitation may be published in Federal Business Opportunities. However, responses to this notice will not be considered adequate responses to a solicitation. Confidentiality. No proprietary, classified, confidential, or sensitive information should be included in your response. The Government reserves the right to use any non-proprietary technical information in any resultant solicitation(s).
- Web Link
-
FBO.gov Permalink
(https://www.fbo.gov/spg/HHS/CDCP/PGOA/2016-Q-65123/listing.html)
- Place of Performance
- Address: 2920 Brandywine Road, Atlanta, Georgia, 30341, United States
- Zip Code: 30341
- Zip Code: 30341
- Record
- SN04195816-W 20160728/160726234543-409a292d0f4697b9e8f02f15afa44231 (fbodaily.com)
- Source
-
FedBizOpps Link to This Notice
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