System and method for smart pooling

1. A system for smart pooling, the system comprising a computing device, wherein the computing device is 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, wherein the probabilistic outcome includes a contagion factor;

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.

2. The system of claim 1 , wherein obtaining the feature datum further comprises identifying a clinical element and obtaining the feature datum as a function of the clinical element.

3. The system of claim 1 , wherein obtaining the feature datum further comprises receiving a medical input and obtaining the feature datum as a function of the medical input.

4. The system of claim 1 , wherein identifying the predictive prevalence value further comprises determining a probabilistic distribution and identifying the predictive prevalence value as a function of the probabilistic distribution.

5. The system of claim 1 , wherein generating the pooling threshold further comprises:

receiving a probability limiter; and

generating the pooling threshold as a function of the probability limiter.

6. The system of claim 1 , wherein the computing device is further configured to:

receive a lab specimen associated with the feature datum;

generate an assignment of the lab specimen to a well as a function of the enhanced well count; and

produce a pool database as a function of assigning the lab specimen to the well.

7. The system of claim 6 , wherein generating the assignment further comprises:

receiving a grouping element; and

generating the assignment the lab specimen as a function of the grouping element and a grouping machine-learning model.

8. The system of claim 6 , wherein generating the assignment further comprises:

identifying a similar predictive prevalence; and

generating the assignment as a function of the similar predictive prevalence.

9. The system of claim 6 , wherein producing the pool database further comprises identifying a delegated pooling strategy and producing the pool database as a function of the delegated pooling strategy.

10. The system of claim 6 , wherein the computing device is further configured to:

determine a deviant outcome as a function of the lab specimen; and

identify a retest element as a function of the deviant outcome.

11. A method for smart pooling, the method comprising:

obtaining, by a computing device, a feature datum;

identifying, by the computing device, 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, wherein the probabilistic outcome includes a contagion factor;

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

determining, by the computing device, 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.

12. The method of claim 11 , wherein obtaining the feature datum further comprises identifying a clinical element and obtaining the feature datum as a function of the clinical element.

13. The method of claim 11 , wherein obtaining the feature datum further comprises receiving a medical input and obtaining the feature datum as a function of the medical input.

14. The method of claim 11 , wherein identifying the predictive prevalence value further comprises determining a probabilistic distribution and identifying the predictive prevalence value as a function of the probabilistic distribution.

15. The method of claim 11 , wherein generating the pooling threshold further comprises:

receiving a probability limiter; and

generating the pooling threshold as a function of the probability limiter.

16. The method of claim 11 , further comprising:

receiving a lab specimen associated with the feature datum;

generating an assignment of the lab specimen to a well as a function of the enhanced well count; and

producing a pool database as a function of assigning the lab specimen to the well.

17. The method of claim 16 , wherein generating the assignment further comprises:

receiving a grouping element; and

generating the assignment the lab specimen as a function of the grouping element and a grouping machine-learning model.

18. The method of claim 16 , wherein generating the assignment further comprises:

identifying a similar predictive prevalence;

generating the assignment as a function of the similar predictive prevalence.

19. The method of claim 16 , wherein producing the pool database further comprises identifying a delegated pooling strategy and producing the pool database as a function of the delegated pooling strategy.

20. The method of claim 16 , further comprising:

determining a deviant outcome as a function of the lab specimen; and

identifying a retest element as a function of the deviant outcome.