Abstract: In an aspect a system for informing a user of a COVID-19 infection status is presented. A system includes a computing device configured to receive user data from a user through a diagnostic tool. A computing device is configured to compare user data to an infection criterion. A computing device is configured to determine, as a function of a comparison, a COVID-19 infection status of a user. A computing device is configured to provide a COVD-19 infection status to the user.

Abstract: Aspects relate to generating a contagion prevention health assessment. An exemplary apparatus includes an optical device, at least a processor communicatively connected to the optical device and a memory communicatively connected to the at least a processor and to the optical device; the processor configured to receive an authentication datum from a user, authenticate the user as a function of the authentication datum and scan the user as a function of the authentication, where scanning the user further includes using a motion recognition machine learning model, generate a guidance datum as a function of the scan and to receive at least a user datum. and generate a health assessment as a function of a contagion status machine learning model, where the contagion status machine learning model is configured to receive the guidance datum and the at least a user datum as input and output the health assessment.

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 a data driven disease test result prediction, the system comprising a computing device configured to receive user data, wherein the user data includes user parameters, generate, using the user data, training data wherein the training data includes a plurality of entries wherein each entry correlates user parameter data to at least a prediction parameter of the plurality of prediction parameters associated with an infectious disease, train, using the training data and a machine-learning process, a machine-learning model, wherein the trained machine-learning model is configured to generate a plurality of infectivity parameters; compare the plurality of infectivity parameters to a retest target threshold, and determine, as a function of the comparison, a confidence metric, wherein the confidence metric informs a testing protocol.

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 a data driven disease test result prediction, the system comprising a computing device configured to receive user data, wherein the user data includes user parameters, generate, using the user data, training data wherein the training data includes a plurality of entries wherein each entry correlates user parameter data to at least a prediction parameter of the plurality of prediction parameters associated with an infectious disease, train, using the training data and a machine-learning process, a machine-learning model, wherein the trained machine-learning model is configured to generate a plurality of infectivity parameters; compare the plurality of infectivity parameters to a retest target threshold, and determine, as a function of the comparison, a confidence metric, wherein the confidence metric informs a testing protocol.

Abstract: A system for a data driven disease test result prediction, the system comprising a computing device configured to receive user data, wherein the user data includes user parameters, generate, using the user data, training data wherein the training data includes a plurality of entries wherein each entry correlates user parameter data to at least a prediction parameter of the plurality of prediction parameters associated with an infectious disease, train, using the training data and a machine-learning process, a machine-learning model, wherein the trained machine-learning model is configured to generate a plurality of infectivity parameters; compare the plurality of infectivity parameters to a retest target threshold, and determine, as a function of the comparison, a confidence metric, wherein the confidence metric informs a testing protocol.

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 method of classifying sample data for robotically extracted samples is disclosed. A specimen is received from a human subject with a potential infection of a first disease agents or a plurality of disease agents. The specimen includes genetic material collected from a human subject using a collection device and stored in a collection carrier. The specimen includes a unique identifier on the collection carrier. The unique identifier contains human subject descriptive data. The method classifies the human subject descriptive data to identify a second disease agent. The method extracts a sequence of genetic material from the specimen using an automated robot. The method determines a test result for the first disease agent as a function of the sequence of genetic material. A system comprising a computer device configured to classify sample data for robotically extracted samples is also disclosed.

Abstract: A method of classifying sample data for robotically extracted samples is disclosed. A specimen is received from a human subject with a potential infection of a first disease agents or a plurality of disease agents. The specimen includes genetic material collected from a human subject using a collection device and stored in a collection carrier. The specimen includes a unique identifier on the collection carrier. The unique identifier contains human subject descriptive data. The method classifies the human subject descriptive data to identify a second disease agent. The method extracts a sequence of genetic material from the specimen using an automated robot. The method determines a test result for the first disease agent as a function of the sequence of genetic material. A system comprising a computer device configured to classify sample data for robotically extracted samples is also disclosed.