Algorithm Development for Early Warning and Prediction of Chemical and Biological Threat Exposure Leveraging Artificial Intelligence and Machine Learning (AI/ML) Approaches

Purpose

This solicitation, issued MTEC, represents a Request for Project Proposals (RPP) for MTEC in support of the U.S. Defense Threat Reduction Agency (DTRA). Military relevance is a critical component of the Enhanced White Paper submission. Strategic oversight for the award(s) supported by this RPP will be provided by DTRA’s Digital Battlespace Management Division (RD-CBI).

This RPP is focused on the development of a software prototype containing algorithm(s) for early warning of chemical and biological threats. The project will involve proof-of-concept algorithmic development for providing predictive values indicative of a potential exposure/infection to a chemical/biological threat prior to symptom onset by utilizing physiological data captured from wearable devices.

Scope of Work

Although Enhanced White Papers that propose to meet all of the product attributes outlined in Section 3.2 are preferred, the Government may consider responses proposing to achieve only a portion of the final product attributes if the Offeror’s approach can address how the remaining requirements can be met over time. Therefore, it is expected that an Offeror’s Enhanced White Paper (using the template provided in Section 8 of the RPP) will describe in detail what they plan to accomplish within the budget currently available (as noted in Section 2.2). Although the Government anticipates $1.47M available in FY23, Offerors are encouraged to request only what is needed to accomplish the solution requirements.

If the proposal is not able to fully address the envisioned solution within the $1.47M of anticipated FY23 funding, the Offeror is expected to describe and provide a roadmap (using the template provided in Addendum 2) to achieve the envisioned end state during the proposed PoP (if more funding was available). If future appropriations (FY24+) become available, this roadmap may help inform future modifications to increase the funding of the awarded project(s) and add additional tasks to move closer to the Offeror(s)’s envisioned solution.

Offerors are encouraged to bring forth proposals that include teaming arrangements with other organizations to try to address all or as many of the attributes as possible (see Section 2.6 for teaming).

In the Enhanced White Paper, Offerors are expected to provide the following information in their submissions:

• Describe prior successful experience in AI/ML data analytics and algorithm development.
• Discuss the intended use of specific AI/ML approaches for advanced data analytics to achieve a high-fidelity solution leveraging the DTRA-provided datasets and existing datasets from the Offeror.
• The ability to apply AI/ML approaches is often contingent on high-quality data inputs, and thus can be difficult to develop a reliable algorithmic solution using AI/ML from wearable device-based data, which is often noisy and has long breaks in data points. Proposals should address the ability to overcome these challenges and methods by which AI/ML approaches can benefit algorithm development.
• Description of the types of AI/ML algorithms for medical purposes that will be developed in addition to any potential algorithms that could be developed and the types of data/amount of data necessary for developing those algorithms, as well as the strategy and feasibility of successfully developing each algorithm.
• Work conducted under this effort should comply with the DoD policies, guidance, and best practices referenced in Addendum 3 of this RPP.

At the end of the PoP, the Offeror is expected to deliver to the Government a Technical Data Package to include a software design description, computer software product (e.g., executables, source code, and algorithms), software test plan, and verification and validation support documentation.

Points of Contact

For inquiries, please direct your correspondence to Biomedical Research Associate Chuck Hutti, Ph.D. at Chuck.Hutti@ati.org.