Abstract: A system for second lab testing of genetic materials is presented. The system includes a computing device configured to receive a specimen from a human subject and perform a smart test on the specimen. The smart test includes a first lab test configured to generate a first lab test identifying a first disease agent and a second lab test configured to generate a second lab test identifying a second disease agent, wherein identifying the second disease agent includes generating a second lab machine-learning model, training the second lab machine-learning model as a function of a second lab test training set, and outputting, as a function of the second lab machine-learning model, the second lab test result using specimen data as an input. The computing device is further configured to generate a smart test result as a function of the smart test.

Abstract: A system for smart pooling includes a computing device configured to obtain a feature datum, identify a predictive prevalence value as a function of the feature datum, wherein identifying the predictive prevalence value further comprises receiving a predictive training set correlating the feature datum with a probabilistic outcome, training a predictive machine-learning model as a function of the predictive training set, and identifying the predictive prevalence value as a function of the trained predictive machine-learning model and the feature datum, and determine an enhanced well count.

Abstract: A system for second lab testing of genetic materials is presented. The system includes a computing device configured to receive a specimen from a human subject and perform a smart test on the specimen. The smart test includes a first lab test configured to generate a first lab test identifying a first disease agent and a second lab test configured to generate a second lab test identifying a second disease agent, wherein identifying the second disease agent includes generating a second lab machine-learning model, training the second lab machine-learning model as a function of a second lab test training set, and outputting, as a function of the second lab machine-learning model, the second lab test result using specimen data as an input. The computing device is further configured to generate a smart test result as a function of the smart test.

Abstract: A system for smart pooling includes a computing device configured to obtain a feature datum, identify a predictive prevalence value as a function of the feature datum, wherein identifying the predictive prevalence value further comprises receiving a predictive training set correlating the feature datum with a probabilistic outcome, training a predictive machine-learning model as a function of the predictive training set, and identifying the predictive prevalence value as a function of the trained predictive machine-learning model and the feature datum, and determine an enhanced well count, wherein determining the enhanced well count further comprises generating a pooling threshold, and determining the enhanced well count as a function of the pooling threshold and the predictive prevalence value.

Abstract: A system for second lab testing of genetic materials is presented. The system includes a computing device configured to receive a specimen from a human subject and perform a smart test on the specimen. The smart test includes a first lab test configured to generate a first lab test identifying a first disease agent and a second lab test configured to generate a second lab test identifying a second disease agent, wherein identifying the second disease agent includes generating a second lab machine-learning model, training the second lab machine-learning model as a function of a second lab test training set, and outputting, as a function of the second lab machine-learning model, the second lab test result using specimen data as an input. The computing device is further configured to generate a smart test result as a function of the smart test.