<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT04437706</url>
  </required_header>
  <id_info>
    <org_study_id>Pro00105430</org_study_id>
    <nct_id>NCT04437706</nct_id>
  </id_info>
  <brief_title>Network-Targeted Strategies for Efficient Community SARS-CoV-2 (COVID-19) Sampling</brief_title>
  <acronym>Snowball</acronym>
  <official_title>Network-Targeted Strategies for Efficient Community SARS-CoV-2 (COVID-19) Sampling</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Duke University</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Duke University</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      The primary objective is to use &quot;network targeted sampling design&quot; to detect active and/or&#xD;
      undiagnosed cases of COVID-19 in the community and determine the spread or distribution of 1)&#xD;
      active infection, and 2) past exposure. The hypothesis is that there are many undiagnosed&#xD;
      and/or asymptomatic people in the community who may be unknowingly spreading the virus or&#xD;
      have been exposed and have antibodies. We propose to implement respondent-driven sampling&#xD;
      (RDS) which leverages effort on the part of seed or index cases to recruit contacts for&#xD;
      participation.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Purpose and objective: The purpose of this study is to use community sampling to detect&#xD;
      active, undiagnosed COVID-19 cases and/or determine the spread or distribution of active&#xD;
      infection. The objective is to use a network targeted sampling design to direct testing to&#xD;
      yield a higher proportion of results which indicate active infection and possibly&#xD;
      differentiating between venues or communities where transmission is active and undiagnosed.&#xD;
&#xD;
      Study activities: A person who tests positive for COVID-19 will be given a set of &quot;tokens&quot; to&#xD;
      give to contacts that will entitle these contacts to make an appointment to receive a test&#xD;
      for COVID-19.&#xD;
&#xD;
      Population groups: The population group will include people with index cases of COVID-19 and&#xD;
      their contacts for the past 14 days. As these contacts are tested and receive positive&#xD;
      results, they will be given tokens to hand out to their contacts over the past 14 days. The&#xD;
      network of positive cases will &quot;blossom&quot; to reveal community transmission and asymptomatic&#xD;
      cases, thus giving researchers an indication of disease prevalence.&#xD;
&#xD;
      Data analysis: At the completion of each epoch and at the end of the study, we will scale the&#xD;
      social networks up and conduct network analysis using SAS and the igraph package in R. These&#xD;
      analyses will be applied in an ongoing manner to guide selection of seeds in subsequent&#xD;
      epochs to ensure representativeness and to guide selection of alters to encourage longer&#xD;
      referral chain lengths.&#xD;
&#xD;
      Risk/safety issues: The primary risks include discomfort from the nasal swab and risks from&#xD;
      the venous blood draw used in testing and the potential loss of confidentiality. All efforts&#xD;
      will be made to securely manage the data to ensure participant confidentiality.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">December 11, 2020</start_date>
  <completion_date type="Actual">July 25, 2022</completion_date>
  <primary_completion_date type="Actual">July 25, 2022</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Ecologic or Community</observational_model>
    <time_perspective>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Number of Participants with Active Infection</measure>
    <time_frame>12 months</time_frame>
    <description>Number of participants with active COVID-19 infection</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Number of Participants with Antibodies Indicating Past Exposure</measure>
    <time_frame>12 months</time_frame>
    <description>Number of participants with antibodies indicating past exposure to SARS-CoV-2</description>
  </primary_outcome>
  <number_of_groups>1</number_of_groups>
  <enrollment type="Actual">602</enrollment>
  <condition>COVID-19</condition>
  <condition>SARS-CoV 2</condition>
  <arm_group>
    <arm_group_label>Participants</arm_group_label>
    <description>Participants completing COVID-19 testing</description>
  </arm_group>
  <biospec_retention>Samples With DNA</biospec_retention>
  <biospec_descr>
    <textblock>
      Blood and pharyngeal swabs&#xD;
    </textblock>
  </biospec_descr>
  <eligibility>
    <study_pop>
      <textblock>
        Adults (18+ years) in the community who have tested positive for COVID-19 or have been in&#xD;
        close contact with someone who is known to be COVID-19 positive&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Positive COVID-19 test result -or- receipt of a coupon from a previous study&#xD;
             participant&#xD;
&#xD;
          -  Resident of Durham County (for seed participant only)&#xD;
&#xD;
          -  Willing to undergo COVID-19 test nasopharyngeal swab for RT-PCR testing and blood draw&#xD;
             for COVID-19 antibody testing&#xD;
&#xD;
          -  Age 18+&#xD;
&#xD;
          -  Speak and understand English&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Currently receiving inpatient treatment for COVID-19&#xD;
&#xD;
          -  Unable to consent&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Dana Pasquale, PhD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Duke University</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Margaret Pendzich</name>
      <address>
        <city>Durham</city>
        <state>North Carolina</state>
        <zip>27710</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <results_reference>
    <citation>Pasquale DK, Welsh W, Olson A, Yacoub M, Moody J, Barajas Gomez BA, Bentley-Edwards KL, McCall J, Solis-Guzman ML, Dunn JP, Woods CW, Petzold EA, Bowie AC, Singh K, Huang ES. Scalable Strategies to Increase Efficiency and Augment Public Health Activities During Epidemic Peaks. J Public Health Manag Pract. 2023 Jun 28. doi: 10.1097/PHH.0000000000001780. Online ahead of print.</citation>
    <PMID>37379511</PMID>
  </results_reference>
  <verification_date>November 2021</verification_date>
  <study_first_submitted>June 17, 2020</study_first_submitted>
  <study_first_submitted_qc>June 17, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">June 18, 2020</study_first_posted>
  <last_update_submitted>July 12, 2023</last_update_submitted>
  <last_update_submitted_qc>July 12, 2023</last_update_submitted_qc>
  <last_update_posted type="Actual">July 14, 2023</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>network analysis</keyword>
  <keyword>contact tracing</keyword>
  <keyword>respondent driven sampling</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>COVID-19</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </patient_data>
  <provided_document_section>
    <provided_document>
      <document_type>Informed Consent Form</document_type>
      <document_has_protocol>No</document_has_protocol>
      <document_has_icf>Yes</document_has_icf>
      <document_has_sap>No</document_has_sap>
      <document_date>May 17, 2022</document_date>
      <document_url>https://ClinicalTrials.gov/ProvidedDocs/06/NCT04437706/ICF_000.pdf</document_url>
    </provided_document>
  </provided_document_section>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT04437706
org study id: Pro00105430
nct id: NCT04437706
lead sponsor: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
The primary objective is to use "network targeted sampling design" to detect active and/or
      undiagnosed cases of COVID-19 in the community and determine the spread or distribution of 1)
      active infection, and 2) past exposure. The hypothesis is that there are many undiagnosed
      and/or asymptomatic people in the community who may be unknowingly spreading the virus or
      have been exposed and have antibodies. We propose to implement respondent-driven sampling
      (RDS) which leverages effort on the part of seed or index cases to recruit contacts for
      participation.
Purpose and objective: The purpose of this study is to use community sampling to detect
      active, undiagnosed COVID-19 cases and/or determine the spread or distribution of active
      infection. The objective is to use a network targeted sampling design to direct testing to
      yield a higher proportion of results which indicate active infection and possibly
      differentiating between venues or communities where transmission is active and undiagnosed.

      Study activities: A person who tests positive for COVID-19 will be given a set of "tokens" to
      give to contacts that will entitle these contacts to make an appointment to receive a test
      for COVID-19.

      Population groups: The population group will include people with index cases of COVID-19 and
      their contacts for the past 14 days. As these contacts are tested and receive positive
      results, they will be given tokens to hand out to their contacts over the past 14 days. The
      network of positive cases will "blossom" to reveal community transmission and asymptomatic
      cases, thus giving researchers an indication of disease prevalence.

      Data analysis: At the completion of each epoch and at the end of the study, we will scale the
      social networks up and conduct network analysis using SAS and the igraph package in R. These
      analyses will be applied in an ongoing manner to guide selection of seeds in subsequent
      epochs to ensure representativeness and to guide selection of alters to encourage longer
      referral chain lengths.

      Risk/safety issues: The primary risks include discomfort from the nasal swab and risks from
      the venous blood draw used in testing and the potential loss of confidentiality. All efforts
      will be made to securely manage the data to ensure participant confidentiality.
observational model: Ecologic or Community
time perspective: Prospective
measure: Number of Participants with Active Infection
time frame: 12 months
description: Number of participants with active COVID-19 infection
measure: Number of Participants with Antibodies Indicating Past Exposure
time frame: 12 months
description: Number of participants with antibodies indicating past exposure to SARS-CoV-2
arm group label: Participants
description: Participants completing COVID-19 testing
Blood and pharyngeal swabs
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: Accepts Healthy Volunteers
last name: Dana Pasquale, PhD
role: Principal Investigator
affiliation: Duke University
facility: 
country: United States
citation: Pasquale DK, Welsh W, Olson A, Yacoub M, Moody J, Barajas Gomez BA, Bentley-Edwards KL, McCall J, Solis-Guzman ML, Dunn JP, Woods CW, Petzold EA, Bowie AC, Singh K, Huang ES. Scalable Strategies to Increase Efficiency and Augment Public Health Activities During Epidemic Peaks. J Public Health Manag Pract. 2023 Jun 28. doi: 10.1097/PHH.0000000000001780. Online ahead of print.
PMID: 37379511
responsible party type: Sponsor
mesh term: COVID-19
sharing ipd: No
provided document: 