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: 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 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.