Systems and methods for a data driven disease test result prediction

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.

What is claimed is: 
     
         1 . An apparatus for a data driven disease test result prediction, the apparatus comprising:
 at least a processor: and   a memory communicatively connected to the at least a processor, the memory containing instruction configuring the at least a processor to:   receive user data, wherein the user data includes at least a user parameter;   generate, as a function of 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, as a function of the training data and a machine-learning process, a machine-learning model wherein the machine-learning model is trained to input the user data and output a plurality of infectivity parameters;   calculate, as a function of the at least a user parameter and the trained machine-learning model, the plurality of infectivity parameters;   compare an aggregation of 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.   
     
     
         2 . The apparatus of  claim 1 , wherein the infectious disease further comprises a coronavirus. 
     
     
         3 . The apparatus of  claim 1 , wherein a user parameter is a user's symptomology related to an infectious disease. 
     
     
         4 . The apparatus of  claim 1 , wherein user data includes a user parameter and information related to the infectious disease. 
     
     
         5 . The apparatus of  claim 1 , wherein a prediction parameter is a quantitative score associated with an infectious disease. 
     
     
         6 . The apparatus of  claim 1 , wherein the plurality of infectivity parameters includes a prevalence parameter, and wherein comparing the plurality of infectivity parameters to the retest target threshold further comprises comparing as a function of the prevalence parameter. 
     
     
         7 . The apparatus of  claim 1 , wherein training the machine-learning model using the machine-learning process further comprises ranking, using a ranking function, the plurality of infectivity parameters so that the aggregation of the plurality of infectivity parameters are directly comparable to the retest target threshold. 
     
     
         8 . The apparatus of  claim 1 , wherein determining the retest target threshold further comprises using a trained machine-learning model and a numerical value scale for the prevalence parameter. 
     
     
         9 . The apparatus of  claim 1 , wherein determining the confidence metric further comprises:
 determining a quantification of a relationship between the retest target threshold and the plurality of infectivity parameters; and   generating an output to retest a user as a function of the testing protocol and the relationship between the rest target threshold and the plurality of infectivity parameters.   
     
     
         10 . The apparatus of  claim 1 , further comprising:
 receiving user testing protocol data; and   selecting a testing protocol as a function of the confidence metric based on the user testing protocol data.   
     
     
         11 . A method for a data driven disease test result prediction, the method comprising:
 receiving, by a processor, user data, wherein the user data includes at least a user parameter;   generating, by the processor, as a function of 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;   training, by the processor, as a function of the training data and a machine-learning process, a machine-learning model wherein the machine-learning model is trained to input the user data and output a plurality of infectivity parameters;   calculating, by the processor, as a function of the at least a user parameter and the trained machine-learning model, the plurality of infectivity parameters;   comparing, by the processor, an aggregation of the plurality of infectivity parameters to a retest target threshold; and   determining, by the processor, as a function of the comparison, a confidence metric, wherein the confidence metric informs a testing protocol.   
     
     
         12 . The method of  claim 11 , wherein the infectious disease further comprises a coronavirus. 
     
     
         13 . The method of  claim 11 , wherein a user parameter is a user's symptomology related to an infectious disease. 
     
     
         14 . The method of  claim 11 , wherein user data includes a user parameter and information related to the infectious disease threshold further comprises comparing as a function of the likelihood parameter. 
     
     
         15 . The method of  claim 11 , wherein a prediction parameter is a quantitative score associated with an infectious disease. 
     
     
         16 . The method of  claim 11 , wherein the plurality of infectivity parameters includes a prevalence parameter, and wherein comparing the plurality of infectivity parameters to the retest target threshold further comprises comparing as a function of the prevalence parameter. 
     
     
         17 . The method of  claim 11 , wherein training the machine-learning model using the machine-learning process further comprises ranking, using a ranking function, the plurality of infectivity parameters so that the aggregation of the plurality of infectivity parameters are directly comparable to the retest target threshold. 
     
     
         18 . The method of  claim 11 , wherein determining the retest target threshold further comprises using a trained machine-learning model and a numerical value scale for the prevalence parameter. 
     
     
         19 . The method of  claim 11 , wherein determining the confidence metric further comprises:
 determining a quantification of a relationship between the retest target threshold and the plurality of infectivity parameters; and   generating an output to retest a user as a function of the testing protocol and the relationship between the rest target threshold and the plurality of infectivity parameters.   
     
     
         20 . The method of  claim 11 , further comprising:
 receiving user testing protocol data; and   selecting a testing protocol as a function of the confidence metric based on the user testing protocol data.