SYSTEMS AND METHODS FOR A DATA DRIVEN DISEASE TEST RESULT PREDICTION

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.