EP4176451A1

EP4176451A1 - Methods and systems for efficient sample pooling for diagnostic testing

Info

Publication number: EP4176451A1
Application number: EP21834522.1A
Authority: EP
Inventors: Guillermo Jose SIMAN; Smrithi SUNIL; Yash Sudhir PATIL
Original assignee: Wepool Inc
Current assignee: Wepool Inc
Priority date: 2020-07-02
Filing date: 2021-06-30
Publication date: 2023-05-10
Also published as: CA3187387A1; EP4176451A4; AU2021300319A1; WO2022006246A1; CN116134546A

Abstract

The present disclosure provides methods for performing or directing the pooling of a plurality of bodily samples. In an aspect, a method may comprise: (a) obtaining a plurality of: health data, contact tracing data, location data, movement data, or any combination thereof associated with the plurality of subjects; and (b) processing the plurality of: health data, contact tracing data, location data, movement data, or any combination thereof with a trained computer algorithm to assign at least some individual subjects of the plurality of subjects to a pool from among a plurality of pools, wherein a number of pools of the plurality of pools is less than a number of subjects of the plurality of subjects.

Description

METHODS AND SYSTEMS FOR EFFICIENT SAMPLE POOLING FOR

DIAGNOSTIC TESTING

CROSS-REFERENCE

[0001] This application claims the benefit of U.S. Provisional Patent Application No. 63/047,630, filed July 2, 2020, which is incorporated by reference herein in its entirety.

SUMMARY

[0002] Methods and systems are provided for performing or directing the pooling of a plurality of bodily samples. Bodily samples, health data, contact tracing data, location data, and/or movement data may be collected from a plurality of subjects (e.g., patients), and trained computer algorithms may be used to efficiently perform or direct the pooling of the bodily samples into a plurality of sample pools for diagnostic testing. Such efficient sample pooling can be used to perform frequent and widespread diagnostic testing of an infectious disease across a population, which may be essential for containment and mitigation, especially in cases of pandemic outbreaks (e.g., COVID-19).

[0003] In an aspect, the present disclosure provides a method comprising: (a) obtaining a plurality of: health data, contact tracing data, location data, movement data, or any combination thereof associated with a plurality of subjects; and (b) processing the plurality of: health data, contact tracing data, location data, movement data, or any combination thereof with a trained computer algorithm to at least some individual subjects of the plurality of subjects to a pool from among a plurality of pools, wherein a number of pools of the plurality of pools is less than a number of subjects of the plurality of subjects.

[0004] In some embodiments, the method further comprises outputting an electronic recommendation to create, for each of at least two given pools of the plurality of pools, a pooled sample by combining bodily samples or portions thereof obtained from subjects in the at least two given pools. In some embodiments, the method further comprises creating, for each of at least two given pools of the plurality of pools, a pooled sample by combining bodily samples or portions thereof obtained from subjects in the at least two given pools. In some embodiments, the method further comprises obtaining the bodily samples or portions thereof from the plurality of subjects.

[0005] In some embodiments, the bodily samples are individually selected from the group consisting of: nasopharyngeal swab, oropharyngeal swab, blood, serum, plasma, vitreous, sputum, urine, stool, tears, perspiration, saliva, semen, mucosal excretions, mucus, spinal fluid, cerebrospinal fluid (CSF), pleural fluid, peritoneal fluid, amniotic fluid, lymph fluid, eye swab, cheek swab, vaginal swab, cervical swab, rectal swab, cells, and tissue.

[0006] In some embodiments, the method further comprises isolating nucleic acids from the bodily samples, and creating, for a given pool of the plurality of pools, the pooled sample by combining at least some of nucleic acids isolated from bodily samples obtained from the subjects in the given pool. In some embodiments, the method further comprises enriching the nucleic acids for a plurality of genomic regions. In some embodiments, the method further comprises amplifying at least some of the nucleic acids. In some embodiments, the amplification comprises selective amplification. In some embodiments, the amplification comprises universal amplification. In some embodiments, enriching at least some of the nucleic acids for the plurality of genomic regions comprises contacting the nucleic acids with a plurality of probes, each of the plurality of probes having sequence complementarity with at least a portion of a genomic region of the plurality of genomic regions. In some embodiments, the plurality of genomic regions comprises genomic regions associated with a disease or disorder. In some embodiments, the disease or disorder comprises coronavirus disease 2019 (COVID-19), human immunodeficiency virus (HIV), or malaria. In some embodiments, the disease or disorder comprises COVID-19.

[0007] In some embodiments, the method further comprises performing a plurality of diagnostic tests on the plurality of pooled samples to obtain a plurality of diagnostic results associated with the plurality of pooled samples. In some embodiments, the plurality of diagnostic tests are configured to detect a presence or absence of a disease or disorder based on analyzing at least the plurality of pooled samples. In some embodiments, the disease or disorder comprises coronavirus disease 2019 (COVID-19), human immunodeficiency virus (HIV), or malaria. In some embodiments, the disease or disorder comprises COVID-19. [0008] In some embodiments, the method further comprises, for a given pool among the plurality of pools, detecting the absence of the disease or disorder in each of the individual subjects of the given pool when the absence of the disease or disorder is detected based on analyzing the pooled sample corresponding to the given pool. In some embodiments, the method further comprises, for a given pool among the plurality of pools, testing each of the individual subjects of the given pool for the disease or disorder when the presence of the disease or disorder is detected based on analyzing the pooled sample corresponding to the given pool. In some embodiments, the method further comprises, for a given pool among the plurality of pools, testing each of a plurality of sub-pools of the given pool for the disease or disorder when the presence of the disease or disorder is detected based on analyzing the pooled sample corresponding to the given pool.

[0009] In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a clinical sensitivity of at least about 50%. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a clinical sensitivity of at least about 70%. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a clinical sensitivity of at least about 90%. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a clinical sensitivity of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or 100%.

[0010] In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a clinical specificity of at least about 50%. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a clinical specificity of at least about 70%. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a clinical specificity of at least about 90%. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a clinical specificity of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or 100%.

[0011] In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a positive predictive value (PPV) of at least about 50%. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a positive predictive value (PPV) of at least about 70%. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a positive predictive value (PPV) of at least about 90%. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a positive predictive value (PPV) of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or 100%.

[0012] In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a negative predictive value (NPV) of at least about 50%. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a negative predictive value (NPV) of at least about 70%. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a negative predictive value (NPV) of at least about 90%. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a negative predictive value (NPV) of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or 100%.

[0013] In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with an area under the curve (AUC) of at least about 0.60. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with an area under the curve (AUC) of at least about 0.70. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with an area under the curve (AUC) of at least about 0.80. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with an area under the curve (AUC) of at least about 0.90. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with an area under the curve (AUC) of at least about 0.50, at least about 0.55, at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.85, at least about 0.90, at least about 0.91, at least about 0.92, at least about 0.93, at least about 0.94, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, or at least about 0.99. [0014] In some embodiments, the plurality of health data, contact tracing data, location data, movement data, or any combination thereof associated with the plurality of subjects comprises de-identified data. In some embodiments, the plurality of health data comprises a diagnosis of a disease or disorder, a prognosis of a disease or disorder, a risk of having a disease or disorder, a treatment history of a disease or disorder, a history of previous treatment for a disease or disorder, a history of prescribed medications, a history of prescribed medical devices, age, height, weight, sex, smoking status, one or more symptoms, and one or more vital signs. In some embodiments, the one or more vital signs comprise one or more of: heart rate, heart rate variability, blood pressure, respiratory rate, blood oxygen concentration (SpO?), carbon dioxide concentration in respiratory gases, a hormone level, sweat analysis, blood glucose, body temperature, impedance, conductivity, capacitance, resistivity, electromyography, galvanic skin response, neurological signals, electroencephalography, electrocardiography, immunology markers, and other physiological measurements.

[0015] In some embodiments, the trained computer algorithm comprises a trained machine learning classifier. In some embodiments, the trained machine learning classifier comprises an algorithm selected from the group consisting of: a support vector machine, a neural network, a random forest, a linear regression, a logistic regression, a Bayesian classifier, a boosted classifier, a gradient boosting algorithm, an adaptive boosting (AdaBoost) algorithm, and an extreme gradient boosting (XGBoost) algorithm.

[0016] In some embodiments, the method further comprises processing health data, contact tracing data, location data, or movement data of the individual subject with the trained computer algorithm to determine an expected prevalence of a disease or disorder, and assigning the individual subject of the plurality of subjects to the pool from among the plurality of pools based at least in part on the determined expected prevalence of the disease or disorder. In some embodiments, the method further comprises assigning the individual subject of the plurality of subjects to the pool from among the plurality of pools when the determined expected prevalence of the disease or disorder is less than a pre-determined prevalence threshold. In some embodiments, the pre-determined prevalence threshold is about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, about 35%, about 40%, about 45%, or about 50%. In some embodiments, the method further comprises determining a maximum pool size based on the determined expected prevalence of the disease or disorder, and assigning the individual subject of the plurality of subjects to the pool from among the plurality of pools based on the maximum pool size. In some embodiments, the method further comprises determining an expected level of severity of symptoms of the plurality of subjects. In some embodiments, the expected level of severity of symptoms comprises a moderate level of symptoms, an intermediate level of symptoms, or a severe level of symptoms.

[0017] In some embodiments, the number of pools of the plurality of pools is reduced relative to the number of subjects of the plurality of subjects by at least 50%. In some embodiments, the number of pools of the plurality of pools is reduced relative to the number of subjects of the plurality of subjects by at least 100%. In some embodiments, the number of pools of the plurality of pools is reduced relative to the number of subjects of the plurality of subjects by at least 200%. In some embodiments, the number of pools of the plurality of pools is reduced relative to the number of subjects of the plurality of subjects by at least 300%. In some embodiments, the number of pools of the plurality of pools is reduced relative to the number of subjects of the plurality of subjects by at least about 10%, at least about 20%, at least about 30%, at least about 40%, at least about 50%, at least about 60%, at least about 70, at least about 80%, at least about 90%, at least about 100%, at least about 110%, at least about 120%, at least about 130%, at least about 140%, at least about 150%, at least about 160%, at least about 170%, at least about 180%, at least about 190%, at least about 200%, at least about 210%, at least about 220%, at least about 230%, at least about 240%, at least about 250%, at least about 260%, at least about 270%, at least about 280%, at least about 290%, at least about 300%, at least about 310%, at least about 320%, at least about 330%, at least about 340%, at least about 350%, at least about 360%, at least about 370%, at least about 380%, at least about 390%, at least about 400%, or more than about 400%.

[0018] In some embodiments, the method further comprises, based on the detected presence or absence of the disease or disorder in the plurality of subjects, administering a therapeutically effective dose of a treatment to treat the disease or disorder detected in at least a subset of the plurality of subjects.

[0019] In another aspect, the present disclosure provides a computer system, comprising: a database that is configured to store a plurality of: health data, contact tracing data, location data, movement data, or any combination thereof associated with a plurality of subjects; and one or more computer processors operatively coupled to the database, wherein the one or more computer processors are individually or collectively programmed to: process the plurality of: health data, contact tracing data, location data, movement data, or any combination thereof with a trained computer algorithm to assign at least some individual subjects of the plurality of subjects to a pool from among a plurality of pools, wherein a number of pools of the plurality of pools is less than a number of subjects of the plurality of subjects.

[0020] Another aspect of the present disclosure provides a non-transitory computer- readable medium comprising machine-executable code that, upon execution by one or more computer processors, implements any of the methods above or elsewhere herein.

[0021] Another aspect of the present disclosure provides a system comprising one or more computer processors and computer memory coupled thereto. The computer memory comprises machine-executable code that, upon execution by the one or more computer processors, implements any of the methods above or elsewhere herein.

[0022] Additional aspects and advantages of the present disclosure will become readily apparent to those skilled in this art from the following detailed description, wherein only illustrative embodiments of the present disclosure are shown and described. As will be realized, the present disclosure is capable of other and different embodiments, and its several details are capable of modifications in various obvious respects, all without departing from the disclosure. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.

INCORPORATION BY REFERENCE

[0023] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. To the extent publications and patents or patent applications incorporated by reference contradict the disclosure contained in the specification, the specification is intended to supersede and/or take precedence over any such contradictory material.

BRIEF DESCRIPTION OF THE DRAWINGS [0024] The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings (also “Figure” and “FIG.” herein), of which:

[0025] FIG. 1A shows an example method for performing sample pooling of a plurality of clinical samples for diagnostic testing. [0026] FIG. IB shows a general workflow for performing sample pooling of a plurality of clinical samples for diagnostic testing, including data input, consuming and consolidating data, performing a method for sample pooling, recommending pooling protocols, and performing diagnostic testing on the sample pools.

[0027] FIG. 1C shows a workflow for performing sample handling and testing.

[0028] FIG. ID shows an example of a system procedure diagram of methods and systems of the present disclosure.

[0029] FIG. IE shows an example of a user activity diagram of methods and systems of the present disclosure.

[0030] FIG. 2A shows an example of sample pooling of a plurality of clinical samples for diagnostic testing. If a sample pool comprising a plurality of individual clinical samples receives a negative clinical test outcome, then all the individual clinical samples in the sample pool must also have a negative clinical test outcome. Conversely, if the sample pool comprising the plurality of individual clinical samples receives a positive clinical test outcome, then either the individual clinical samples can be individually tested, or the sample pool can be further sub-divided into smaller subsets and the sample pooling process can be repeated.

[0031] FIG. 2B shows an example of how methods and systems of the present disclosure advantageously use sample pooling. For example, currently about 2 out of 10 test results return positive; patterns can be learned via machine learning about the 8 out of 10 negative patients. Using methods and systems of the present disclosure, predictive analytics and machine learning may be applied to identify patients with a lower prevalence rate. A plurality of predicted negative samples may be pooled together to create sample pool, and a single diagnostic test may be performed on the sample pool.

[0032] FIG. 3 shows a computer system that is programmed or otherwise configured to implement methods provided herein.

[0033] FIG. 4A shows the number of people who were able to be tested with 1,000 diagnostic tests, using Boston vs. Cambridge cohort information (left 3 columns), clinical symptom information (middle 3 columns), and contact tracing information (right 3 columns). Within each set of 3 columns, the number of people who were able to be tested with 1,000 diagnostic tests is indicated using no sample pooling (left), simple sample pooling (middle), and intelligent sample pooling based on methods and systems of the present disclosure (right). [0034] FIG. 4B shows that using intelligent sample pooling based on methods and systems of the present disclosure, the rules for diagnostic testing can be redefined.

[0035] FIG. 5A shows the percentage of test savings that is achieved using simple sample pooling (gray), using intelligent sample pooling based on methods and systems of the present disclosure (purple), and an upper bound achieved using intelligent sample pooling (dashed).

[0036] FIG. 5B shows the percentage increase in diagnostic testing capacity that is achieved using simple sample pooling (gray), using intelligent sample pooling based on methods and systems of the present disclosure (purple), and an upper bound achieved using intelligent sample pooling (dashed).

[0037] FIG. 6 shows a relative number of diagnostic tests that is required versus the sample pool size (ranging from 1 sample to 20 samples per pool), at a prevalence rate (PR) of 1% (light blue), 5% (orange), 10% (gray), 20% (yellow), and 30% (dark blue).

[0038] FIG. 7 shows that using methods and systems of the present disclosure, an optimal pool size of 4 samples per pool was selected, and a 40% reduction in diagnostic test kit utilization was achieved, along with a clinical sensitivity of 80% and a clinical specificity of 96%.

DETAILED DESCRIPTION

[0039] The term “nucleic acid,” or “polynucleotide,” as used herein, generally refers to a molecule comprising one or more nucleic acid subunits, or nucleotides. A nucleic acid may include one or more nucleotides selected from adenosine (A), cytosine (C), guanine (G), thymine (T) and uracil (U), or variants thereof. A nucleotide generally includes a nucleoside and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more phosphate (P03) groups. A nucleotide can include a nucleobase, a five-carbon sugar (either ribose or deoxyribose), and one or more phosphate groups, individually or in combination.

[0040] Ribonucleotides are nucleotides in which the sugar is ribose. Deoxyribonucleotides are nucleotides in which the sugar is deoxyribose. A nucleotide can be a nucleoside monophosphate or a nucleoside polyphosphate. A nucleotide can be a deoxyribonucleoside polyphosphate, such as, e.g., a deoxyribonucleoside triphosphate (dNTP), which can be selected from deoxyadenosine triphosphate (dATP), deoxycytosine triphosphate (dCTP), deoxyguanosine triphosphate (dGTP), uridine triphosphate (dUTP) and deoxythymidine triphosphate (dTTP) dNTPs, that include detectable tags, such as luminescent tags or markers (e.g., fluorophores). A nucleotide can include any subunit that can be incorporated into a growing nucleic acid strand. Such subunit can be an A, C, G, T, or U, or any other subunit that is specific to one or more complementary A, C, G, T or U, or complementary to a purine (i.e., A or G, or variant thereof) or a pyrimidine (i.e., C, T or U, or variant thereof). In some examples, a nucleic acid is deoxyribonucleic acid (DNA), ribonucleic acid (RNA), or derivatives or variants thereof. A nucleic acid may be single- stranded or double stranded. A nucleic acid molecule may be linear, curved, or circular or any combination thereof.

[0041] The terms “nucleic acid molecule,” “nucleic acid sequence,” “nucleic acid fragment,” “oligonucleotide” and “polynucleotide,” as used herein, generally refer to a polynucleotide that may have various lengths, such as either deoxyribonucleotides or ribonucleotides (RNA), or analogs thereof. A nucleic acid molecule can have a length of at least about 5 bases, 10 bases, 20 bases, 30 bases, 40 bases, 50 bases, 60 bases, 70 bases, 80 bases, 90, 100 bases, 110 bases, 120 bases, 130 bases, 140 bases, 150 bases, 160 bases, 170 bases, 180 bases, 190 bases, 200 bases, 300 bases, 400 bases, 500 bases, 1 kilobase (kb), 2 kb, 3, kb, 4 kb, 5 kb, 10 kb, or 50 kb or it may have any number of bases between any two of the aforementioned values. An oligonucleotide is typically composed of a specific sequence of four nucleotide bases: adenine (A); cytosine (C); guanine (G); and thymine (T) (uracil (U) for thymine (T) when the polynucleotide is RNA). Thus, the terms “nucleic acid molecule,” “nucleic acid sequence,” “nucleic acid fragment,” “oligonucleotide” and “polynucleotide” are at least in part intended to be the alphabetical representation of a polynucleotide molecule. Alternatively, the terms may be applied to the polynucleotide molecule itself. This alphabetical representation can be input into databases in a computer having a central processing unit and/or used for bioinformatics applications such as functional genomics and homology searching. Oligonucleotides may include one or more nonstandard nucleotide(s), nucleotide analog(s) and/or modified nucleotides.

[0042] The term “sample,” as used herein, generally refers to a biological sample. Examples of biological samples include nucleic acid molecules, amino acids, polypeptides, proteins, carbohydrates, fats, or viruses. In an example, a biological sample is a nucleic acid sample including one or more nucleic acid molecules. The nucleic acid molecules may be cell-free or cell-free nucleic acid molecules, such as cell-free DNA (cfDNA) or cell-free RNA (cfRNA). The nucleic acid molecules may be derived from a variety of sources including human, mammal, non-human mammal, ape, monkey, chimpanzee, reptilian, amphibian, or avian, sources. Further, samples may be extracted from a variety of animal fluids containing cell-free sequences, including but not limited to bodily fluid samples such as blood, serum, plasma, vitreous, sputum, urine, tears, perspiration, saliva, semen, mucosal excretions, mucus, spinal fluid, cerebrospinal fluid (CSF), pleural fluid, peritoneal fluid, amniotic fluid, lymph fluid, and the like. Cell free polynucleotides (e.g., cfDNA) may be fetal in origin (via fluid taken from a pregnant subject), or may be derived from tissue of the subject itself.

[0043] The term “subject,” as used herein, generally refers to an individual having a biological sample that is undergoing processing or analysis. A subject can be an animal or plant. The subject can be a mammal, such as a human, dog, cat, horse, pig or rodent. The subject can be a patient, e.g., have or be suspected of having a disease, such as one or more cancers (e.g., brain cancer, breast cancer, cervical cancer, colorectal cancer, endometrial cancer, esophageal cancer, gastric cancer, hepatobiliary tract cancer, leukemia, liver cancer, lung cancer, lymphoma, ovarian cancer, pancreatic cancer, skin cancer, urinary tract cancer), one or more infectious diseases, one or more genetic disorder, or one or more tumors, or any combination thereof. For subjects having or suspected of having one or more tumors, the tumors may be of one or more types.

[0044] The term “whole blood,” as used herein, generally refers to a blood sample that has not been separated into sub-components (e.g., by centrifugation). The whole blood of a blood sample may contain cfDNA and/or germline DNA. Whole blood DNA (which may contain cfDNA and/or germline DNA) may be extracted from a blood sample. Whole blood DNA sequencing reads (which may contain cfDNA sequencing reads and/or germline DNA sequencing reads) may be extracted from whole blood DNA.

[0045] Diagnostic testing of subjects (e.g., patients) for a disease or disorder may be limited or scarce. In some cases, diagnostic testing may be saved for symptomatic subjects or high-risk subjects; this can be less than ideal because asymptomatic subjects continue to be contagious and spread infectious diseases such as viruses (e.g., COVID-19 or HIV), bacteria, or parasites (e.g., malaria). Therefore, frequent and widespread diagnostic testing of an infectious disease across a population may be essential for containment and mitigation, especially in cases of pandemic outbreaks.

[0046] Methods and systems are provided for pooling a plurality of bodily samples. Bodily samples, health data, contact tracing data, location data, and/or movement data may be collected from a plurality of subjects (e.g., patients), and trained computer algorithms may be used to efficiently pool the bodily samples into a plurality of sample pools for diagnostic testing. Such efficient sample pooling can be used to perform frequent and widespread diagnostic testing of an infectious disease across a population, which may be essential for

-li containment and mitigation, especially in cases of pandemic outbreaks (e.g., COVID-19,

HIV, or malaria).

[0047] In an aspect, the present disclosure provides a method comprising: (a) obtaining a plurality of: health data, contact tracing data, location data, movement data, or any combination thereof associated with a plurality of subjects; and (b) processing the plurality of: health data, contact tracing data, location data, movement data, or any combination thereof with a trained computer algorithm to at least some individual subjects of the plurality of subjects to a pool from among a plurality of pools, wherein a number of pools of the plurality of pools is less than a number of subjects of the plurality of subjects.

[0048] In some embodiments, the method further comprises outputting an electronic recommendation to create, for each of at least two given pools of the plurality of pools, a pooled sample by combining bodily samples or portions thereof obtained from subjects in the at least two given pools. In some embodiments, the method further comprises creating, for each of at least two given pools of the plurality of pools, a pooled sample by combining bodily samples or portions thereof obtained from subjects in the at least two given pools. In some embodiments, the method further comprises obtaining the bodily samples or portions thereof from the plurality of subjects.

[0049] In some embodiments, the bodily samples are individually selected from the group consisting of: nasopharyngeal swab, oropharyngeal swab, blood, serum, plasma, vitreous, sputum, urine, stool, tears, perspiration, saliva, semen, mucosal excretions, mucus, spinal fluid, cerebrospinal fluid (CSF), pleural fluid, peritoneal fluid, amniotic fluid, lymph fluid, eye swab, cheek swab, vaginal swab, cervical swab, rectal swab, cells, and tissue.

[0050] In some embodiments, the method further comprises isolating nucleic acids from the bodily samples, and creating, for a given pool of the plurality of pools, the pooled sample by combining at least some of nucleic acids isolated from bodily samples obtained from the subjects in the given pool. In some embodiments, the method further comprises enriching the nucleic acids for a plurality of genomic regions. In some embodiments, the method further comprises amplifying at least some of the nucleic acids. In some embodiments, the amplification comprises selective amplification. In some embodiments, the amplification comprises universal amplification. In some embodiments, enriching the nucleic acids for the plurality of genomic regions comprises contacting the nucleic acids with a plurality of probes, each of the plurality of probes having sequence complementarity with at least a portion of a genomic region of the plurality of genomic regions. In some embodiments, the plurality of genomic regions comprises genomic regions associated with a disease or disorder. In some embodiments, the disease or disorder comprises coronavirus disease 2019 (COVID-19), human immunodeficiency virus (HIV), or malaria. In some embodiments, the disease or disorder comprises COVID-19.

[0051] In some embodiments, the method further comprises performing a plurality of diagnostic tests on the plurality of pooled samples to obtain a plurality of diagnostic results associated with the plurality of pooled samples. In some embodiments, the plurality of diagnostic tests are configured to detect a presence or absence of a disease or disorder based on analyzing at least the plurality of pooled samples. In some embodiments, the disease or disorder comprises coronavirus disease 2019 (COVID-19), human immunodeficiency virus (HIV), or malaria. In some embodiments, the disease or disorder comprises COVID-19.

[0052] In some embodiments, the method further comprises, for a given pool among the plurality of pools, detecting the absence of the disease or disorder in each of the individual subjects of the given pool when the absence of the disease or disorder is detected based on analyzing the pooled sample corresponding to the given pool. In some embodiments, the method further comprises, for a given pool among the plurality of pools, testing each of the individual subjects of the given pool for the disease or disorder when the presence of the disease or disorder is detected based on analyzing the pooled sample corresponding to the given pool. In some embodiments, the method further comprises, for a given pool among the plurality of pools, testing each of a plurality of sub-pools of the given pool for the disease or disorder when the presence of the disease or disorder is detected based on analyzing the pooled sample corresponding to the given pool.

[0053] In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a clinical sensitivity of at least about 50%. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a clinical sensitivity of at least about 70%. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a clinical sensitivity of at least about 90%. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a clinical sensitivity of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or 100%. [0054] In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a clinical specificity of at least about 50%. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a clinical specificity of at least about 70%. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a clinical specificity of at least about 90%. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a clinical specificity of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or 100%.

[0055] In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a positive predictive value (PPV) of at least about 50%. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a positive predictive value (PPV) of at least about 70%. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a positive predictive value (PPV) of at least about 90%. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a positive predictive value (PPV) of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or 100%.

[0056] In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a negative predictive value (NPV) of at least about 50%. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a negative predictive value (NPV) of at least about 70%. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a negative predictive value (NPV) of at least about 90%. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with a negative predictive value (NPV) of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or 100%.

[0057] In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with an area under the curve (AUC) of at least about 0.60. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with an area under the curve (AUC) of at least about 0.70. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with an area under the curve (AUC) of at least about 0.80. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with an area under the curve (AUC) of at least about 0.90. In some embodiments, the method further comprises detecting the presence or absence of the disease or disorder in the plurality of subjects with an area under the curve (AUC) of at least about 0.50, at least about 0.55, at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.85, at least about 0.90, at least about 0.91, at least about 0.92, at least about 0.93, at least about 0.94, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, or at least about 0.99.

[0058] In some embodiments, the plurality of health data comprises a diagnosis of a disease or disorder, a prognosis of a disease or disorder, a risk of having a disease or disorder, a treatment history of a disease or disorder, a history of previous treatment for a disease or disorder, a history of prescribed medications, a history of prescribed medical devices, age, height, weight, sex, smoking status, one or more symptoms, and one or more vital signs. In some embodiments, the one or more vital signs comprise one or more of: heart rate, heart rate variability, blood pressure, respiratory rate, blood oxygen concentration (SpO?), carbon dioxide concentration in respiratory gases, a hormone level, sweat analysis, blood glucose, body temperature, impedance, conductivity, capacitance, resistivity, electromyography, galvanic skin response, neurological signals, electroencephalography, electrocardiography, immunology markers, and other physiological measurements.

[0059] In some embodiments, the trained computer algorithm comprises a trained machine learning classifier. In some embodiments, the trained machine learning classifier comprises an algorithm selected from the group consisting of: a support vector machine, a neural network, a random forest, a linear regression, a logistic regression, a Bayesian classifier, a boosted classifier, a gradient boosting algorithm, an adaptive boosting (AdaBoost) algorithm, and an extreme gradient boosting (XGBoost) algorithm.

[0060] In some embodiments, the method further comprises processing health data, contact tracing data, location data, or movement data of the individual subject with the trained computer algorithm to determine an expected prevalence of a disease or disorder, and assigning the individual subject of the plurality of subjects to the pool from among the plurality of pools based at least in part on the determined expected prevalence of the disease or disorder. In some embodiments, the method further comprises assigning the individual subject of the plurality of subjects to the pool from among the plurality of pools when the determined expected prevalence of the disease or disorder is less than a pre-determined prevalence threshold. In some embodiments, the pre-determined prevalence threshold is about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, about 35%, about 40%, about 45%, or about 50%. In some embodiments, the method further comprises determining a maximum pool size based on the determined expected prevalence of the disease or disorder, and assigning the individual subject of the plurality of subjects to the pool from among the plurality of pools based on the maximum pool size.

[0061] In some embodiments, the number of pools of the plurality of pools is reduced relative to the number of subjects of the plurality of subjects by at least 50%. In some embodiments, the number of pools of the plurality of pools is reduced relative to the number of subjects of the plurality of subjects by at least 100%. In some embodiments, the number of pools of the plurality of pools is reduced relative to the number of subjects of the plurality of subjects by at least 200%. In some embodiments, the number of pools of the plurality of pools is reduced relative to the number of subjects of the plurality of subjects by at least 300%. In some embodiments, the number of pools of the plurality of pools is reduced relative to the number of subjects of the plurality of subjects by at least about 10%, at least about 20%, at least about 30%, at least about 40%, at least about 50%, at least about 60%, at least about 70, at least about 80%, at least about 90%, at least about 100%, at least about 110%, at least about 120%, at least about 130%, at least about 140%, at least about 150%, at least about 160%, at least about 170%, at least about 180%, at least about 190%, at least about 200%, at least about 210%, at least about 220%, at least about 230%, at least about 240%, at least about 250%, at least about 260%, at least about 270%, at least about 280%, at least about 290%, at least about 300%, at least about 310%, at least about 320%, at least about 330%, at least about 340%, at least about 350%, at least about 360%, at least about 370%, at least about 380%, at least about 390%, at least about 400%, or more than about 400%.

[0062] In some embodiments, the method further comprises, based on the detected presence or absence of the disease or disorder in the plurality of subjects, administering a therapeutically effective dose of a treatment to treat the disease or disorder detected in at least a subset of the plurality of subjects.

[0063] FIG. 1A shows an example method 100 for performing sample pooling of a plurality of clinical samples for diagnostic testing. The method 100 may comprise obtaining a plurality of bodily samples from a plurality of subjects (as in operation 102). In some embodiments, the bodily samples comprises a nasopharyngeal swab, oropharyngeal swab, blood, serum, plasma, vitreous, sputum, urine, stool, tears, perspiration, saliva, semen, mucosal excretions, mucus, spinal fluid, cerebrospinal fluid (CSF), pleural fluid, peritoneal fluid, amniotic fluid, lymph fluid, eye swab, cheek swab, vaginal swab, cervical swab, rectal swab, cells, or tissue. Next, the method 100 may comprise obtaining a plurality of: health data, contact tracing data, location data, movement data, or any combination thereof associated with the plurality of subjects (as in operation 104). In some instances, any of the data herein (e.g., health data, contact tracing data, location data, or movement data associated with the plurality of subjects) comprises de-identified data. In some embodiments, the plurality of health data comprises a diagnosis of a disease or disorder, a prognosis of a disease or disorder, a risk of having a disease or disorder, a treatment history of a disease or disorder, a history of previous treatment for a disease or disorder, a history of prescribed medications, a history of prescribed medical devices, age, height, weight, sex, smoking status, one or more symptoms, and one or more vital signs. In some embodiments, the one or more vital signs comprise one or more of: heart rate, heart rate variability, blood pressure, respiratory rate, blood oxygen concentration (SpO?), carbon dioxide concentration in respiratory gases, a hormone level, sweat analysis, blood glucose, body temperature, impedance, conductivity, capacitance, resistivity, electromyography, galvanic skin response, neurological signals, electroencephalography, electrocardiography, immunology markers, and other physiological measurements. In some embodiments, the plurality of contact tracing data, location data, or movement data comprises data associated with subjects’ environment, location, movement, or daily schedules. For example, for a delivery warehouse that is performing diagnostic testing on its employees, intelligent pooling may be performed based on the employees’ delivery routes, shift schedules, contact tracing information, etc. In some embodiments, the method 100 is applied to genetic sequencing of samples, such that pooling of samples is performed where the mutation rate is low. Next, the method 100 may comprise processing the plurality of: health data, contact tracing data, location data, movement data, or any combination thereof with a trained computer algorithm to at least some individual subjects of the plurality of subjects to a pool from among a plurality of pools (as in operation 106). In some embodiments, a number of pools of the plurality of pools is less than a number of subjects of the plurality of subjects. In some embodiments, the trained computer algorithm comprises a trained machine learning classifier. In some embodiments, the trained machine learning classifier comprises an algorithm selected from the group consisting of: a support vector machine, a neural network, a random forest, a linear regression, a logistic regression, a Bayesian classifier, a boosted classifier, a gradient boosting algorithm, an adaptive boosting (AdaBoost) algorithm, and an extreme gradient boosting (XGBoost) algorithm.

[0064] FIG. IB shows a general workflow for performing sample pooling of a plurality of clinical samples for diagnostic testing, including data input, consuming and consolidating data, performing a method for sample pooling, recommending pooling protocols, and performing diagnostic testing on the sample pools. In some embodiments, performing the diagnostic testing on the sample pools comprises using an integrated robot and/or robotics application programming interface (API) to perform liquid handling of a plurality of bodily samples and/or sample pools. In some embodiments, performing the diagnostic testing on the sample pools comprises detecting a plurality of diseases or disorders (e.g., common cold, influenza, COVID-19 infection, and/or COVID-19 immunity) using a same sample pool. In some embodiments, performing the diagnostic testing on the sample pools comprises tagging a plurality of bodily samples and/or sample pools (e.g., using sample tags or sample barcodes) to increase a multiplexity of the diagnostic testing (e.g., via DNA sequencing,

RNA sequencing, or reverse-transcription polymerase chain reaction (RT-PCR)). In some embodiments, performing the diagnostic testing on the sample pools comprises performing genetic sequencing on a plurality of bodily samples and/or sample pools (e.g., in cases where a mutation rate is low).

[0065] FIG. 1C shows a workflow for performing sample handling and testing. The workflow for performing sample handling and testing may comprise sample collection of a plurality of bodily samples from a plurality of subjects. Logistical synergies (or disadvantages) may be associated with the handling of the bodily samples. Seamless information flow may be required for the software (e.g., barcodes may be used to identify subject samples through a connected EHR system, if the bodily samples at the collection sites lack the required data). Next, the workflow for performing sample handling and testing may comprise extracting nucleic acids (e.g., DNA or RNA) from the bodily fluid samples. Next, the workflow for performing sample handling and testing may comprise pooling the samples, where the pools are selected using methods and systems of the present disclosure. Such pools are selected using machine learning to inform decisions on which samples to pool together and/or the sample size to pool together. Next, the workflow for performing sample handling and testing may comprise performing a diagnostic test or panel on the pooled samples (e.g., an RT-PCR diagnostic panel).

[0066] FIG. ID shows an example of a system procedure diagram of methods and systems of the present disclosure.

[0067] FIG. IE shows an example of a user activity diagram of methods and systems of the present disclosure.

[0068] FIG. 2A shows an example of sample pooling of a plurality of clinical samples for diagnostic testing. If a sample pool comprising a plurality of individual clinical samples receives a negative clinical test outcome, then all the individual clinical samples in the sample pool must also have a negative clinical test outcome. Conversely, if the sample pool comprising the plurality of individual clinical samples receives a positive clinical test outcome, then either the individual clinical samples can be individually tested, or the sample pool can be further sub-divided into smaller subsets and the sample pooling process can be repeated.

[0069] FIG. 2B shows an example of how methods and systems of the present disclosure advantageously use sample pooling. For example, currently about 2 out of 10 test results return positive; patterns can be learned via machine learning about the 8 out of 10 negative patients. Using methods and systems of the present disclosure, predictive analytics and machine learning may be applied to identify patients with a lower prevalence rate. A plurality of predicted negative samples may be pooled together to create sample pool, and a single diagnostic test may be performed on the sample pool. As a result, the same number of subjects may be tested using a smaller number of diagnostic tests; the saved diagnostic tests may be used to test a broader population of subjects. Generally, when a disease or disorder has a high enough prevalence, the expected probability of encountering at least one positive sample among a sample pool may increase to a high enough value such that the savings from sample pooling may be spoiled. Therefore, intelligent pooling strategies of the methods and systems of the present disclosure enables the efficient use of pool testing.

[0070] In some embodiments, nucleic acids may be extracted from bodily samples and analyzed for diagnostic testing. For example, sequencing reads may be generated from the nuclei c acids using any suitable sequencing method. The sequencing method can be a first- generation sequencing method, such as Maxam-Gilbert or Sanger sequencing, or a high- throughput sequencing (e.g., next-generation sequencing or NGS) method. A high-throughput sequencing method may sequence simultaneously (or substantially simultaneously) at least 10,000, 100,000, 1 million, 10 million, 100 million, 1 billion, or more polynucleotide molecules. Sequencing methods may include, but are not limited to: pyrosequencing, sequencing-by-synthesis, single-molecule sequencing, nanopore sequencing, semiconductor sequencing, sequencing-by-ligation, sequencing-by-hybridization, Digital Gene Expression (Helicos), massively parallel sequencing, e.g., Helicos, Clonal Single Molecule Array (Solexa/Illumina), sequencing using PacBio, SOLiD, Ion Torrent, or Nanopore platforms. [0071] In some embodiments, the sequencing comprises whole genome sequencing (WGS). The sequencing may be performed at a depth sufficient to assess tumor progression or tumor non-progression in a subject with a desired performance (e.g., accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), or the area under curve (AUC) of a receiver operator characteristic (ROC)).

[0072] In some embodiments, the sequencing reads may be aligned or mapped to a reference genome. The reference genome may comprise at least a portion of a genome (e.g., the human genome). The reference genome may comprise an entire genome (e.g., the entire human genome). The reference genome may comprise an entire genome with certain base conversions applied (e.g., the entire human genome with cytosines converted to thymines), as may be used for methylation data alignment. The reference genome may comprise a database comprising a plurality of genomic regions that correspond to coding and/or non-coding genomic regions of a genome. The database may comprise a plurality of genomic regions that correspond to disease-associated coding and/or non-coding genomic regions of a genome, such as single nucleotide variants (SNVs), copy number variants (CNVs), insertions or deletions (indels), and fusion genes. The alignment may be performed using a Burrows- Wheeler algorithm (BWA), a sambamba algorithm, a samtools algorithm, or any other suitable alignment algorithm.

[0073] In some embodiments, a quantitative measure of the sequencing reads may be generated for each of a plurality of genomic regions. Quantitative measures of the sequencing reads may be generated, such as counts of sequencing reads that are aligned with a given genomic region. Sequencing reads having a portion or all of the sequencing read aligning with a given genomic region may be counted toward the quantitative measure for that genomic region. [0074] In some embodiments, genomic regions may comprise disease markers. Patterns of specific and non-specific genomic regions may be indicative of disease progression or disease non-progression status. Changes over time in these patterns of genomic regions may be indicative of changes in disease progression or disease non-progression status.

[0075] In some embodiments, nucleic acids may be assayed by performing binding measurements of the nucleic acids at each of a plurality of genomic regions. In some embodiments, performing the binding measurements comprises assaying the nucleic acids using probes that are selective for at least a portion of a plurality of genomic regions in the plurality of nucleic acids. In some embodiments, the probes are nucleic acid molecules having sequence complementarity with nucleic acid sequences of the plurality of genomic regions. In some embodiments, the nucleic acid molecules are primers or enrichment sequences. In some embodiments, the assaying comprises use of array hybridization or polymerase chain reaction (PCR), or nucleic acid sequencing.

[0076] In some embodiments, the nucleic acids are enriched for at least a portion of the plurality of genomic regions. In some embodiments, the enrichment comprises amplifying at least some of the nucleic acids. For example, the nucleic acids may be amplified by selective amplification (e.g., by using a set of primers or probes comprising nucleic acid molecules having sequence complementarity with nucleic acid sequences of the plurality of genomic regions). Alternatively or in combination, the nucleic acids may be amplified by universal amplification (e.g., by using universal primers). In some embodiments, the enrichment comprises selectively isolating at least a portion of the plurality of nucleic acids.

[0077] In some embodiments, the sequencing reads may be normalized or corrected. For example, the sequencing reads may be de-deduplicated, normalized, and/or corrected to account for known biases in sequencing and library preparation and/or known biases in sequencing and library preparation. In some embodiments, a subset of the quantitative measures (e.g., statistical measures) may be filtered out, e.g., based on whether the changes in such quantitative measures (e.g., across different time points) are significantly different from those observed in unaffected subjects (e.g., a background profile of nucleic acids).

[0078] The plurality of genomic regions may comprise at least about 10 distinct genomic regions, at least about 50 distinct genomic regions, at least about 100 distinct genomic regions, at least about 500 distinct genomic regions, at least about 1 thousand distinct genomic regions, at least about 5 thousand distinct genomic regions, at least about 10 thousand distinct genomic regions, at least about 50 thousand distinct genomic regions, at least about 100 thousand distinct genomic regions, at least about 500 thousand distinct genomic regions, at least about 1 million distinct genomic regions, at least about 2 million distinct genomic regions, at least about 3 million distinct genomic regions, at least about 4 million distinct genomic regions, at least about 5 million distinct genomic regions, at least about 10 million distinct genomic regions, at least about 15 million distinct genomic regions, at least about 20 million distinct genomic regions, at least about 25 million distinct genomic regions, at least about 30 million distinct genomic regions, or more than 30 million distinct genomic regions.

Computer systems

[0079] The present disclosure provides computer systems that are programmed to implement methods of the disclosure. FIG. 3 shows a computer system 301 that is programmed or otherwise configured to, for example, direct the obtaining of bodily samples from subjects, obtain health data, contact tracing data, location data, or movement data associated with subjects, process health data, contact tracing data, location data, or movement data with a trained computer algorithm to assign individual subjects to a pool, output an electronic recommendation to create a pooled sample by combining bodily samples or portions thereof obtained from subjects in a pool, direct the creating of a pooled sample by combining bodily samples or portions thereof obtained from subjects in a pool, direct the isolating of nucleic acids from bodily samples, direct the enriching of nucleic acids, direct the amplifying of nucleic acids, direct the performing of diagnostic tests on pooled samples to obtain diagnostic results associated with the pooled samples, and detect the absence of the disease or disorder in individual subjects of a pool when an absence of a disease or disorder is detected based on analyzing a pooled sample corresponding to the pool. The computer system 301 can regulate various aspects of analysis, calculation, and generation of the present disclosure, such as, for example, directing the obtaining of bodily samples from subjects, obtaining health data, contact tracing data, location data, or movement data associated with subjects, processing health data, contact tracing data, location data, or movement data with a trained computer algorithm to assign individual subjects to a pool, outputting an electronic recommendation to create a pooled sample by combining bodily samples or portions thereof obtained from subjects in a pool, directing the creating of a pooled sample by combining bodily samples or portions thereof obtained from subjects in a pool, directing the isolating of nucleic acids from bodily samples, directing the enriching of nucleic acids, directing the amplifying of nucleic acids, directing the performing of diagnostic tests on pooled samples to obtain diagnostic results associated with the pooled samples, and detecting the absence of the disease or disorder in individual subjects of a pool when an absence of a disease or disorder is detected based on analyzing a pooled sample corresponding to the pool. The computer system 301 can be an electronic device of a user or a computer system that is remotely located with respect to the electronic device. The electronic device can be a mobile electronic device. [0080] The computer system 301 includes a central processing unit (CPU, also “processor” and “computer processor” herein) 305, which can be a single core or multi core processor, or a plurality of processors for parallel processing. The computer system 301 also includes memory or memory location 310 (e.g., random-access memory, read-only memory, flash memory), electronic storage unit 315 (e.g., hard disk), communication interface 320 (e.g., network adapter) for communicating with one or more other systems, and peripheral devices 325, such as cache, other memory, data storage and/or electronic display adapters.

The memory 310, storage unit 315, interface 320 and peripheral devices 325 are in communication with the CPU 305 through a communication bus (solid lines), such as a motherboard. The storage unit 315 can be a data storage unit (or data repository) for storing data. The computer system 301 can be operatively coupled to a computer network (“network”) 330 with the aid of the communication interface 320. The network 330 can be the Internet, an internet and/or extranet, or an intranet and/or extranet that is in communication with the Internet. The network 330 in some cases is a telecommunication and/or data network. The network 330 can include one or more computer servers, which can enable distributed computing, such as cloud computing. For example, one or more computer servers may enable cloud computing over the network 330 (“the cloud”) to perform various aspects of analysis, calculation, and generation of the present disclosure, such as, for example, directing the obtaining of bodily samples from subjects, obtaining health data, contact tracing data, location data, or movement data associated with subjects, processing health data, contact tracing data, location data, or movement data with a trained computer algorithm to assign individual subjects to a pool, outputting an electronic recommendation to create a pooled sample by combining bodily samples or portions thereof obtained from subjects in a pool, directing the creating of a pooled sample by combining bodily samples or portions thereof obtained from subjects in a pool, directing the isolating of nucleic acids from bodily samples, directing the enriching of nucleic acids, directing the amplifying of nucleic acids, directing the performing of diagnostic tests on pooled samples to obtain diagnostic results associated with the pooled samples, and detecting the absence of the disease or disorder in individual subjects of a pool when an absence of a disease or disorder is detected based on analyzing a pooled sample corresponding to the pool. Such cloud computing may be provided by cloud computing platforms such as, for example, Amazon Web Services (AWS), Mi crosoft Azure, Google Cloud Platform, and IBM cloud. The network 330, in some cases with the aid of the computer system 301, can implement a peer-to-peer network, which may enable devices coupled to the computer system 301 to behave as a client or a server.

[0081] The CPU 305 can execute a sequence of machine-readable instructions, which can be embodied in a program or software. The instructions may be stored in a memory location, such as the memory 310. The instructions can be directed to the CPU 305, which can subsequently program or otherwise configure the CPU 305 to implement methods of the present disclosure. Examples of operations performed by the CPU 305 can include fetch, decode, execute, and writeback.

[0082] The CPU 305 can be part of a circuit, such as an integrated circuit. One or more other components of the system 301 can be included in the circuit. In some cases, the circuit is an application specific integrated circuit (ASIC).

[0083] The storage unit 315 can store files, such as drivers, libraries and saved programs. The storage unit 315 can store user data, e.g., user preferences and user programs. The computer system 301 in some cases can include one or more additional data storage units that are external to the computer system 301, such as located on a remote server that is in communication with the computer system 301 through an intranet or the Internet.

[0084] The computer system 301 can communicate with one or more remote computer systems through the network 330. For instance, the computer system 301 can communicate with a remote computer system of a user (e.g., a physician, a nurse, a caretaker, a patient, or a subject). Examples of remote computer systems include personal computers (e.g., portable PC), slate or tablet PC’s (e.g., Apple® iPad, Samsung® Galaxy Tab), telephones, Smart phones (e.g., Apple® iPhone, Android-enabled device, Blackberry®), or personal digital assistants. The user can access the computer system 301 via the network 330.

[0085] Methods as described herein can be implemented by way of machine (e.g., computer processor) executable code stored on an electronic storage location of the computer system 301, such as, for example, on the memory 310 or electronic storage unit 315. The machine-executable or machine-readable code can be provided in the form of software. During use, the code can be executed by the processor 305. In some cases, the code can be retrieved from the storage unit 315 and stored on the memory 310 for ready access by the processor 305. In some situations, the electronic storage unit 315 can be precluded, and machine-executable instructions are stored on memory 310.

[0086] The code can be pre-compiled and configured for use with a machine having a processor adapted to execute the code, or can be compiled during runtime. The code can be supplied in a programming language that can be selected to enable the code to execute in a pre-compiled or as-compiled fashion.

[0087] Aspects of the systems and methods provided herein, such as the computer system 301, can be embodied in programming. Various aspects of the technology may be thought of as “products” or “articles of manufacture” typically in the form of machine (or processor) executable code and/or associated data that is carried on or embodied in a type of machine- readable medium. Machine-executable code can be stored on an electronic storage unit, such as memory (e.g., read-only memory, random-access memory, flash memory) or a hard disk. “Storage” type media can include any or all of the tangible memory of the computers, processors or the like, or associated modules thereof, such as various semiconductor memories, tape drives, disk drives and the like, which may provide non-transitory storage at any time for the software programming. All or portions of the software may at times be communicated through the Internet or various other telecommunication networks. Such communications, for example, may enable loading of the software from one computer or processor into another, for example, from a management server or host computer into the computer platform of an application server. Thus, another type of media that may bear the software elements includes optical, electrical and electromagnetic waves, such as used across physical interfaces between local devices, through wired and optical landline networks and over various air-links. The physical elements that carry such waves, such as wired or wireless links, optical links or the like, also may be considered as media bearing the software. As used herein, unless restricted to non-transitory, tangible “storage” media, terms such as computer or machine “readable medium” refer to any medium that participates in providing instructions to a processor for execution.

[0088] Hence, a machine-readable medium, such as computer-executable code, may take many forms, including but not limited to, a tangible storage medium, a carrier wave medium or physical transmission medium. Non-volatile storage media include, for example, optical or magnetic disks, such as any of the storage devices in any computer(s) or the like, such as may be used to implement the databases, etc. shown in the drawings. Volatile storage media include dynamic memory, such as main memory of such a computer platform. Tangible transmission media include coaxial cables; copper wire and fiber optics, including the wires that comprise a bus within a computer system. Carrier-wave transmission media may take the form of electric or electromagnetic signals, or acoustic or light waves such as those generated during radio frequency (RF) and infrared (IR) data communications. Common forms of computer-readable media therefore include for example: a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD or DVD- ROM, any other optical medium, punch cards paper tape, any other physical storage medium with patterns of holes, a RAM, a ROM, a PROM and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave transporting data or instructions, cables or links transporting such a carrier wave, or any other medium from which a computer may read programming code and/or data. Many of these forms of computer-readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.

[0089] The computer system 301 can include or be in communication with an electronic display 335 that comprises a user interface (EΊ) 340 for providing, for example, health data, contact tracing data, location data, or movement data, recommendations to create pooled samples, and diagnostic results of pooled samples. Examples of UIs include, without limitation, a graphical user interface (GET) and web-based user interface.

[0090] Methods and systems of the present disclosure can be implemented by way of one or more algorithms. An algorithm can be implemented by way of software upon execution by the central processing unit 305. The algorithm can, for example, direct the obtaining of bodily samples from subjects, obtain health data, contact tracing data, location data, or movement data associated with subjects, process health data, contact tracing data, location data, or movement data with a trained computer algorithm to assign individual subjects to a pool, output an electronic recommendation to create a pooled sample by combining bodily samples or portions thereof obtained from subjects in a pool, direct the creating of a pooled sample by combining bodily samples or portions thereof obtained from subjects in a pool, direct the isolating of nucleic acids from bodily samples, direct the enriching of nucleic acids, direct the amplifying of nucleic acids, direct the performing of diagnostic tests on pooled samples to obtain diagnostic results associated with the pooled samples, and detect the absence of the disease or disorder in individual subjects of a pool when an absence of a disease or disorder is detected based on analyzing a pooled sample corresponding to the pool.

EXAMPLES

Example 1: Efficient sample pooling for increasing diagnostic testing capacity of COVID-19

[0091] Using methods and systems of the present disclosure, efficient sample pooling was performed for increasing diagnostic testing capacity of coronavirus disease 2019 (COVID- 19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. The sample pooling was performed using one of three different types of information to perform the classification of a plurality of individual samples each into a respective sample pool from among a plurality of sample pools, including Boston vs. Cambridge cohort information, clinical symptom information, and contact tracing information. FIG. 4A shows the number of people who were able to be tested with 1,000 diagnostic tests, using Boston vs. Cambridge cohort information (left 3 columns), clinical symptom information (middle 3 columns), and contact tracing information (right 3 columns). Within each set of 3 columns, the number of people who were able to be tested with 1,000 diagnostic tests is indicated using no sample pooling (left), simple sample pooling (middle), and intelligent sample pooling based on methods and systems of the present disclosure (right).

[0092] As shown in FIG. 4A, simple sample pooling outperformed no sample pooling for all three different types of information used; further, intelligent sample pooling based on methods and systems of the present disclosure outperformed simple sample pooling for all three different types of information used (and significantly so for the cases of clinical symptom information and contact tracing information. Therefore, using intelligent sample pooling based on methods and systems of the present disclosure, diagnostic testing capacity for COVID-19 can be increased by about 3X, with at least a 40% cost savings at 96% specificity.

[0093] FIG. 4B shows that using intelligent sample pooling based on methods and systems of the present disclosure, the rules for diagnostic testing can be redefined. Based on the current state (left side), symptoms alone are the prevalence assessment. Some subjects who meet a set of certain criteria receive testing, while the others are denied testing (e.g., because of rationing of diagnostic testing due to a shortage of testing capacity). Using intelligent sample pooling based on methods and systems of the present disclosure (right side), the ratio of diagnostic tests available to individuals tested for COVID-19 is no longer 1:1. This ratio can be effectively increased, for example, to about 1.1, about 1.2, about 1.3, about 1.4, about 1.5, about 1.6, about 1.7, about 1.8, about 1.9, about 2.0, about 2.1, about 2.2, about 2.3, about 2.4, about 2.5, about 2.6, about 2.7, about 2.8, about 2.9, about 3.0, or more than about 3.0.

Example 2: Efficient sample pooling using PrecisionFDA data

[0094] Using methods and systems of the present disclosure, efficient sample pooling was performed using PrecisionFDA data for increasing diagnostic testing capacity of COVID-19. The PrecisionFDA data was analyzed as follows. First, the data set was obtained. Next, the COVID-19 related observations were filtered.

[0095] Next, patient details regarding those observations were obtained (e.g., clinical health data, contact tracing data, location data, movement data, and demographic data, including age). Next, diagnostic test outcomes were obtained (represented in binary format, where values of 1 indicate positive test results and values of 0 indicates negative test results). Next, gender data was obtained (represented in binary format, where values of 0 indicate male and values of 1 indicate female). Next, race and ethnicity data was obtained (represented in one hot encoding via 6 binary values corresponding to each race/ethnicity category). Next, the data were filtered to keep certain columns corresponding to pertinent data features, including marital status, race, ethnicity, gender, healthcare expenses, age, counts by county, positive cases by county, counts by city, positive cases by city, prevalence by county, prevalence by city. As an example of an input data structure, the data for each subject or patient may comprise one or more of: a diagnostic test result, an age, sex/gender, zip code (or another de- identified geolocator), reason for diagnostic testing, vital signs (if available and accessible), and symptoms (if available). For example, symptoms may include fever, cough, sore throat, tiredness, body temperature, respiratory rate, loss of taste, loss of smell, vomiting, shortness of breath, chills, hypoxemia, etc. Next, prevalence data was obtained from the Starschema COVID-19 epidemiological data set.

[0096] Next, the data was split into a training dataset and a testing dataset. Next, a supervised machine learning classifier (e.g., Random Forest classifier, extreme gradient boosting (XGBoost), and gradient boosting) was created and trained using the training dataset. Next, predicted outcomes for the testing dataset were obtained. Next, a classification report and confusion matrix were generated to evaluate the classifier performance. Next, the total numbers of positive cases and negative cases in the test dataset were obtained. Next, a set of feature importance values (e.g., weights) were calculated.

[0097] Table 1 shows relative performance metrics obtained using three different supervised machine learning classifiers (Random Forest classifier, extreme gradient boosting (XGBoost), and gradient boosting). The Random Forest classifier had the following top 5 weighted features: fever, cough, body temperature, respiratory rate, and loss of taste. The XGBoost classifier had the following top 5 weighted features: fever, cough, hypoxemia, body temperature, and loss of taste. The gradient boosting classifier had the following top 5 weighted features: fever, cough, body temperature, respiratory rate, and loss of taste. M® Name Precision Recall fl Score

[0098] Table 1: Performance comparison across machine learning classifier models [0099] FIG. 5A shows the percentage of test savings that is achieved using simple sample pooling (gray), using intelligent sample pooling based on methods and systems of the present disclosure (purple), and an upper bound achieved using intelligent sample pooling (dashed). As shown in FIG. 5A, the percentage of test savings achieved using simple pooling diminishes rapidly as the prevalence rate of the disease increases, and simple pooling is not viable above a prevalence rate of about 25%; further, a greater percentage of test savings is achieved using intelligent sample pooling based on methods and systems of the present disclosure, as compared to using simple sample pooling, across all prevalence rates of disease.

[0100] FIG. 5B shows the percentage increase in diagnostic testing capacity that is achieved using simple sample pooling (gray), using intelligent sample pooling based on methods and systems of the present disclosure (purple), and an upper bound achieved using intelligent sample pooling (dashed). As shown in FIG. 5B, the percentage increase in diagnostic testing capacity achieved using simple pooling diminishes rapidly as the prevalence rate of the disease increases, and simple pooling is not viable above a prevalence rate of about 25%; further, a greater percentage increase in diagnostic testing capacity is achieved using intelligent sample pooling based on methods and systems of the present disclosure, as compared to using simple sample pooling, across all prevalence rates of disease. Therefore, using intelligent sample pooling based on methods and systems of the present disclosure, a consistent increase in testing capacity is achieved in all prevalence environments, but also a pool prevalence below 30% is strategically and actively maintained. Example 3: Number of diagnostic tests needed with intelligent sample pool testing

[0101] Using methods and systems of the present disclosure, efficient sample pooling is performed using for increasing diagnostic testing capacity of COVID-19. FIG. 6 shows a relative number of diagnostic tests that is required versus the sample pool size (ranging from 1 sample to 20 samples per pool), at a prevalence rate (PR) of 1% (light blue), 5% (orange), 10% (gray), 20% (yellow), and 30% (dark blue). The relative number of tests is calculated by mimicking the utilization of resources. The relative number of tests is minimized based on two approaches. First, the best sample pool size or sample pooling strategy is selected. By referring to the resulting graph, the sample pool size that provides the lowest number of diagnostic tests is determined. Second, the prevalence rate (PR) is reduced. Lower prevalence rates within sample pools yield a lower number of diagnostic tests required, thereby improving upon the first strategy of selecting the best sample pool size or sample pooling strategy. Using methods and systems of the present disclosure, the prevalence rate of sample pools is reduced through intelligent segmentation using machine learning methodologies.

Example 4: Increase in diagnostic testing capacity at high clinical sensitivity [0102] A population of 40,000 clinical test samples (e.g., which corresponds to Quest Diagnostic’s daily testing capacity) was simulated, assuming a disease prevalence rate of 10%, a clinical sensitivity of 90%, and a clinical specificity of 90%. Using methods and systems of the present disclosure, an optimal pool size of 4 samples per pool was selected, and a 40% reduction in diagnostic test kit utilization was achieved, along with a clinical sensitivity of 80% and a clinical specificity of 96% (as shown in FIG. 7). Therefore, these results represent an increase in Quest Diagnostic’s testing capacity by 16,000 diagnostic tests, without changing their initial kit availability.

[0103] While preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. It is not intended that the invention be limited by the specific examples provided within the specification. While the invention has been described with reference to the aforementioned specification, the descriptions and illustrations of the embodiments herein are not meant to be construed in a limiting sense. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. Furthermore, it shall be understood that all aspects of the invention are not limited to the specific depictions, configurations or relative proportions set forth herein which depend upon a variety of conditions and variables. It should be understood that various altematives to the embodiments of the invention described herein may be employed in practicing the invention. It is therefore contemplated that the invention shall also cover any such alternatives, modifications, variations or equivalents. It is intended that the following claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby.

Claims

CLAIMS WHAT IS CLAIMED IS:

1. A method, comprising:

(a) obtaining a plurality of: health data, contact tracing data, location data, movement data, or any combination thereof associated with a plurality of subjects; and

(b) processing the plurality of: health data, contact tracing data, location data, movement data, or any combination thereof with a trained computer algorithm to assign at least some individual subjects of the plurality of subjects to a pool from among a plurality of pools, wherein a number of pools of the plurality of pools is less than a number of subjects of the plurality of subjects.

2. The method of claim 1, further comprising outputting an electronic recommendation to create, for each of at least two given pools of the plurality of pools, a pooled sample by combining bodily samples or portions thereof obtained from subjects in the at least two given pools.

3. The method of claim 1, further comprising creating, for each of at least two given pools of the plurality of pools, a pooled sample by combining bodily samples or portions thereof obtained from subjects in the at least two given pools.

4. The method of claim 2 or 3, further comprising obtaining the bodily samples or portions thereof from the plurality of subjects.

5. The method of any one of claims 2-4, wherein the bodily samples are individually selected from the group consisting of: nasopharyngeal swab, oropharyngeal swab, blood, serum, plasma, vitreous, sputum, urine, stool, tears, perspiration, saliva, semen, mucosal excretions, mucus, spinal fluid, cerebrospinal fluid (CSF), pleural fluid, peritoneal fluid, amniotic fluid, lymph fluid, eye swab, cheek swab, vaginal swab, cervical swab, rectal swab, cells, and tissue.

6. The method of claim 5, further comprising isolating nucleic acids from the bodily samples, and creating, for a given pool of the plurality of pools, the pooled sample by combining at least some of nucleic acids isolated from bodily samples obtained from the subjects in the given pool.

7. The method of claim 6, further comprising enriching the nucleic acids for a plurality of genomic regions.

8. The method of claim 6 or 7, further comprising amplifying at least some of the nucleic acids.

9. The method of claim 8, wherein the amplification comprises selective amplification.

10. The method of claim 8, wherein the amplification comprises universal amplification.

11. The method of claim 7, wherein enriching the nucleic acids for the plurality of genomic regions comprises contacting the nucleic acids with a plurality of probes, each of the plurality of probes having sequence complementarity with at least a portion of a genomic region of the plurality of genomic regions.

12. The method of claim 11, wherein the plurality of genomic regions comprises genomic regions associated with a disease or disorder.

13. The method of claim 12, wherein the disease or disorder comprises coronavirus disease 2019 (COVID-19), human immunodeficiency virus (HIV), or malaria.

14. The method of claim 13, wherein the disease or disorder comprises COVID-19.

15. The method of any one of claims 3-14, further comprising performing a plurality of diagnostic tests on the plurality of pooled samples to obtain a plurality of diagnostic results associated with the plurality of pooled samples.

16. The method of claim 15, wherein the plurality of diagnostic tests are configured to detect a presence or absence of a disease or disorder based on analyzing at least the plurality of pooled samples.

17. The method of claim 16, wherein the disease or disorder comprises coronavirus disease 2019 (COVID-19).

18. The method of claim 16 or 17, further comprising, for a given pool among the plurality of pools, detecting the absence of the disease or disorder in each of the individual subjects of the given pool when the absence of the disease or disorder is detected based on analyzing the pooled sample corresponding to the given pool.

19. The method of claim 16 or 17, further comprising, for a given pool among the plurality of pools, testing each of the individual subjects of the given pool for the disease or disorder when the presence of the disease or disorder is detected based on analyzing the pooled sample corresponding to the given pool.

20. The method of claim 16 or 17, further comprising, for a given pool among the plurality of pools, testing each of a plurality of sub-pools of the given pool for the disease or disorder when the presence of the disease or disorder is detected based on analyzing the pooled sample corresponding to the given pool.

21. The method of any one of claims 16-20, further comprising detecting the presence or absence of the disease or disorder in the plurality of subjects with a clinical sensitivity of at least about 50%.

22. The method of claim 21, further comprising detecting the presence or absence of the disease or disorder in the plurality of subjects with a clinical sensitivity of at least about 70%.

23. The method of claim 22, further comprising detecting the presence or absence of the disease or disorder in the plurality of subjects with a clinical sensitivity of at least about 90%.

24. The method of any one of claims 16-20, further comprising detecting the presence or absence of the disease or disorder in the plurality of subjects with a clinical specificity of at least about 50%.

25. The method of claim 24, further comprising detecting the presence or absence of the disease or disorder in the plurality of subjects with a clinical specificity of at least about 70%.

26. The method of claim 25, further comprising detecting the presence or absence of the disease or disorder in the plurality of subjects with a clinical specificity of at least about 90%.

27. The method of any one of claims 16-20, further comprising detecting the presence or absence of the disease or disorder in the plurality of subjects with a positive predictive value (PPV) of at least about 50%.

28. The method of claim 27, further comprising detecting the presence or absence of the disease or disorder in the plurality of subjects with a positive predictive value (PPV) of at least about 70%.

29. The method of claim 28, further comprising detecting the presence or absence of the disease or disorder in the plurality of subjects with a positive predictive value (PPV) of at least about 90%.

30. The method of any one of claims 16-20, further comprising detecting the presence or absence of the disease or disorder in the plurality of subjects with a negative predictive value (NPV) of at least about 50%.

31. The method of claim 30, further comprising detecting the presence or absence of the disease or disorder in the plurality of subjects with a negative predictive value (NPV) of at least about 70%.

32. The method of claim 31, further comprising detecting the presence or absence of the disease or disorder in the plurality of subjects with a negative predictive value (NPV) of at least about 90%.

33. The method of any one of claims 16-20, further comprising detecting the presence or absence of the disease or disorder in the plurality of subjects with an area under the curve (AUC) of at least about 0.60.

34. The method of claim 33, further comprising detecting the presence or absence of the disease or disorder in the plurality of subjects with an area under the curve (AUC) of at least about 0.70.

35. The method of claim 34, further comprising detecting the presence or absence of the disease or disorder in the plurality of subjects with an area under the curve (AUC) of at least about 0.80.

36. The method of claim 35, further comprising detecting the presence or absence of the disease or disorder in the plurality of subjects with an area under the curve (AUC) of at least about 0.90.

37. The method of claim 1, wherein the plurality of health data comprises a diagnosis of a disease or disorder, a prognosis of a disease or disorder, a risk of having a disease or disorder, a treatment history of a disease or disorder, a history of previous treatment for a disease or disorder, a history of prescribed medications, a history of prescribed medical devices, age, height, weight, sex, smoking status, one or more symptoms, and one or more vital signs.

38. The method of claim 37, wherein the one or more vital signs comprise one or more of: heart rate, heart rate variability, blood pressure, respiratory rate, blood oxygen concentration (Sp0₂), carbon dioxide concentration in respiratory gases, a hormone level, sweat analysis, blood glucose, body temperature, impedance, conductivity, capacitance, resistivity, electromyography, galvanic skin response, neurological signals, electroencephalography, electrocardiography, immunology markers, and other physiological measurements.

39. The method of claim 1, wherein the trained computer algorithm comprises a trained machine learning classifier.

40. The method of claim 39, wherein the trained machine learning classifier comprises an algorithm selected from the group consisting of: a support vector machine, a neural network, a random forest, a linear regression, a logistic regression, a Bayesian classifier, a boosted classifier, a gradient boosting algorithm, an adaptive boosting (AdaBoost) algorithm, and an extreme gradient boosting (XGBoost) algorithm.

41. The method of claim 1, wherein further comprising processing health data, contact tracing data, location data, or movement data of the individual subject with the trained computer algorithm to determine an expected prevalence of a disease or disorder, and assigning the individual subject of the plurality of subjects to the pool from among the plurality of pools based at least in part on the determined expected prevalence of the disease or disorder.

42. The method of claim 41, further comprising assigning the individual subject of the plurality of subjects to the pool from among the plurality of pools when the determined expected prevalence of the disease or disorder is less than a pre-determined prevalence threshold.

43. The method of claim 42, wherein the pre-determined prevalence threshold is about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, about 35%, about 40%, about 45%, or about 50%.

44. The method of claim 41, further comprising determining a maximum pool size based on the determined expected prevalence of the disease or disorder, and assigning the individual subject of the plurality of subjects to the pool from among the plurality of pools based on the maximum pool size.

45. The method of any one of claim 1-44, wherein the number of pools of the plurality of pools is reduced relative to the number of subjects of the plurality of subjects by at least 50%.

46. The method of claim 45, wherein the number of pools of the plurality of pools is reduced relative to the number of subjects of the plurality of subjects by at least 100%.

47. The method of claim 46, wherein the number of pools of the plurality of pools is reduced relative to the number of subjects of the plurality of subjects by at least 200%.

48. The method of claim 47, wherein the number of pools of the plurality of pools is reduced relative to the number of subjects of the plurality of subjects by at least 300%.

49. The method of any one of claims 1-48, further comprising, based on the detected presence or absence of the disease or disorder in the plurality of subjects, administering a therapeutically effective dose of a treatment to treat the disease or disorder detected in at least a subset of the plurality of subjects.

50. A computer system, comprising: a database that is configured to store a plurality of: health data, contact tracing data, location data, movement data, or any combination thereof associated with a plurality of subjects; and one or more computer processors operatively coupled to the database, wherein the one or more computer processors are individually or collectively programmed to: process the plurality of: health data, contact tracing data, location data, movement data, or any combination thereof with a trained computer algorithm to assign at least some individual subjects of the plurality of subjects to a pool from among a plurality of pools, wherein a number of pools of the plurality of pools is less than a number of subjects of the plurality of subjects.

51. A non-transitory computer-readable medium comprising machine-executable instructions which, upon execution by one or more computer processors, perform a method comprising: