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This Government Contract opportunity from Department Of Homeland Security was posted on May 7, 2026. The submission period has ended. Browse the details below for market research, or find similar active opportunities.

Environmental Microbial Baseline Modeling and Data Analysis

Closed
Federal

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The contract involves developing predictive models to analyze the impact of environmental and temporal variables on microbial composition and the accuracy of biothreat detection systems. The goal is to enhance detection capabilities by minimizing false positives and false negatives, thereby improving overall reliability in identifying potential biological threats. This work requires sophisticated data analysis techniques to establish a comprehensive environmental microbial baseline. Issued by the Department of Homeland Security's Cwmd Acquisition Division, this subcontract falls under the NAICS code 541690, which pertains to scientific research and development services. The place of performance is listed as Springfield, with a deadline for responses set for May 28, 2026. The contract emphasizes the critical role of innovative data modeling in national security efforts related to biothreat detection.

General Info

Develop predictive models to improve biothreat detection accuracy by analyzing environmental microbial data.

Agency

Department Of Homeland Security → Cwmd Acq Division

NAICS

541690 - Other Scientific and Technical Consulting ServicesView NAICS

Place of Performance

Springfield, VA, 22150, USA

Set-Aside

NONE

Documents

(0)

No documents available

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Timeline

PhaseClosed
Posted

subcontract

Response Deadline

Deadline has passed

Submission Closed

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Organization & Contact Information

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AgencyDepartment Of Homeland Security → Cwmd Acq Division
ContactsNo contacts available
OfficeN/A
Organization / Agency
Department Of Homeland Security → Cwmd Acq Division
Office AddressN/A
ContactsNo contact information available

Full Description

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Develop predictive models analyzing how environmental and temporal factors influence microbial composition and biothreat detection accuracy to reduce false positives/negatives.