Systems and methods for a data driven disease test result prediction

1. A system for a data driven disease test result prediction, the system comprising:

a computing device, wherein the computing device is designed and configured 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;

sort the training data using a classification algorithm, wherein the training data is sorted as a function of a COVID-19 status and zip code;

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 wherein the plurality of infectivity parameters comprises a likelihood parameter, a severity parameter, and a prevalence parameter;

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 system of claim 1 , wherein the infectious disease further comprises a coronavirus.

3. The system of claim 1 , wherein generating training data further comprises receiving a parameter category and generating training data as a function of the parameter category.

4. The system of claim 1 , wherein the plurality of infectivity parameters includes the likelihood parameter, and wherein comparing the plurality of infectivity parameters to the retest target threshold further comprises comparing as a function of the likelihood parameter.

5. The system of claim 1 , wherein the plurality of infectivity parameters includes the severity parameter, and wherein comparing the plurality of infectivity parameters to the retest target threshold further comprises comparing as a function of the severity parameter.

6. The system of claim 1 , wherein the plurality of infectivity parameters includes the 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 system 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 system 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 system 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 system 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 computing device, user data, wherein the user data includes at least a user parameter;

generating, by the computing device, 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;

sorting, by the computing device, the training data using a classification algorithm, wherein the training data is sorted as a function of a COVID-19 status and zip code;

training, by the computing device, 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 wherein the plurality of infectivity parameters comprises a likelihood parameter, a severity parameter, and a prevalence parameter;

calculating, by the computing device, as a function of the at least a user parameter and the trained machine-learning model, the plurality of infectivity parameters;

comparing, by the computing device, an aggregation of the plurality of infectivity parameters to a retest target threshold; and

determining, by the computing device, 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 generating training data further comprises receiving a parameter category and generating training data as a function of the parameter category.

14. The method of claim 11 , wherein the plurality of infectivity parameters includes the likelihood parameter, and wherein comparing the plurality of infectivity parameters to the retest target threshold further comprises comparing as a function of the likelihood parameter.

15. The method of claim 11 , wherein the plurality of infectivity parameters includes the severity parameter, and wherein comparing the plurality of infectivity parameters to the retest target threshold further comprises comparing as a function of the severity parameter.

16. The method of claim 11 , wherein the plurality of infectivity parameters includes the 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.