A stochastic simulation model to study respondent-driven recruitment
Respondent-driven detection is a chain recruitment method used to sample contact persons of infected persons in order to enhance case finding. It starts with initial individuals, so-called seeds, who are invited for participation. Afterwards, seeds receive a fixed number of coupons to invite individ...
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| Veröffentlicht in: | PloS one 2018-11, Vol.13 (11), p.e0207507-e0207507 |
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| creator | Stein, Mart L Buskens, Vincent van der Heijden, Peter G M van Steenbergen, Jim E Wong, Albert Bootsma, Martin C J Kretzschmar, Mirjam E E |
| description | Respondent-driven detection is a chain recruitment method used to sample contact persons of infected persons in order to enhance case finding. It starts with initial individuals, so-called seeds, who are invited for participation. Afterwards, seeds receive a fixed number of coupons to invite individuals with whom they had contact during a specific time period. Recruitees are then asked to do the same, resulting in successive waves of contact persons who are connected in one recruitment tree. However, often the majority of participants fail to invite others, or invitees do not accept an invitation, and recruitment stops after several waves. A mathematical model can help to analyse how various factors influence peer recruitment and to understand under which circumstances sustainable recruitment is possible. We implemented a stochastic simulation model, where parameters were suggested by empirical data from an online survey, to determine the thresholds for obtaining large recruitment trees and the number of waves needed to reach a steady state in the sample composition for individual characteristics. We also examined the relationship between mean and variance of the number of invitations sent out by participants and the probability of obtaining a large recruitment tree. Our main finding is that a situation where participants send out any number of coupons between one and the maximum number is more effective in reaching large recruitment trees, compared to a situation where the majority of participants does not send out any invitations and a smaller group sends out the maximum number of invitations. The presented model is a helpful tool that can assist public health professionals in preparing research and contact tracing using online respondent-driven detection. In particular, it can provide information on the required minimum number of successfully sent invitations to reach large recruitment trees, a certain sample composition or certain number of waves. |
| doi_str_mv | 10.1371/journal.pone.0207507 |
| format | Article |
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It starts with initial individuals, so-called seeds, who are invited for participation. Afterwards, seeds receive a fixed number of coupons to invite individuals with whom they had contact during a specific time period. Recruitees are then asked to do the same, resulting in successive waves of contact persons who are connected in one recruitment tree. However, often the majority of participants fail to invite others, or invitees do not accept an invitation, and recruitment stops after several waves. A mathematical model can help to analyse how various factors influence peer recruitment and to understand under which circumstances sustainable recruitment is possible. We implemented a stochastic simulation model, where parameters were suggested by empirical data from an online survey, to determine the thresholds for obtaining large recruitment trees and the number of waves needed to reach a steady state in the sample composition for individual characteristics. We also examined the relationship between mean and variance of the number of invitations sent out by participants and the probability of obtaining a large recruitment tree. Our main finding is that a situation where participants send out any number of coupons between one and the maximum number is more effective in reaching large recruitment trees, compared to a situation where the majority of participants does not send out any invitations and a smaller group sends out the maximum number of invitations. The presented model is a helpful tool that can assist public health professionals in preparing research and contact tracing using online respondent-driven detection. 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This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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It starts with initial individuals, so-called seeds, who are invited for participation. Afterwards, seeds receive a fixed number of coupons to invite individuals with whom they had contact during a specific time period. Recruitees are then asked to do the same, resulting in successive waves of contact persons who are connected in one recruitment tree. However, often the majority of participants fail to invite others, or invitees do not accept an invitation, and recruitment stops after several waves. A mathematical model can help to analyse how various factors influence peer recruitment and to understand under which circumstances sustainable recruitment is possible. We implemented a stochastic simulation model, where parameters were suggested by empirical data from an online survey, to determine the thresholds for obtaining large recruitment trees and the number of waves needed to reach a steady state in the sample composition for individual characteristics. 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Stein, Mart L</au><au>Buskens, Vincent</au><au>van der Heijden, Peter G M</au><au>van Steenbergen, Jim E</au><au>Wong, Albert</au><au>Bootsma, Martin C J</au><au>Kretzschmar, Mirjam E E</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A stochastic simulation model to study respondent-driven recruitment</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2018-11-15</date><risdate>2018</risdate><volume>13</volume><issue>11</issue><spage>e0207507</spage><epage>e0207507</epage><pages>e0207507-e0207507</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Respondent-driven detection is a chain recruitment method used to sample contact persons of infected persons in order to enhance case finding. It starts with initial individuals, so-called seeds, who are invited for participation. Afterwards, seeds receive a fixed number of coupons to invite individuals with whom they had contact during a specific time period. Recruitees are then asked to do the same, resulting in successive waves of contact persons who are connected in one recruitment tree. However, often the majority of participants fail to invite others, or invitees do not accept an invitation, and recruitment stops after several waves. A mathematical model can help to analyse how various factors influence peer recruitment and to understand under which circumstances sustainable recruitment is possible. We implemented a stochastic simulation model, where parameters were suggested by empirical data from an online survey, to determine the thresholds for obtaining large recruitment trees and the number of waves needed to reach a steady state in the sample composition for individual characteristics. We also examined the relationship between mean and variance of the number of invitations sent out by participants and the probability of obtaining a large recruitment tree. Our main finding is that a situation where participants send out any number of coupons between one and the maximum number is more effective in reaching large recruitment trees, compared to a situation where the majority of participants does not send out any invitations and a smaller group sends out the maximum number of invitations. The presented model is a helpful tool that can assist public health professionals in preparing research and contact tracing using online respondent-driven detection. In particular, it can provide information on the required minimum number of successfully sent invitations to reach large recruitment trees, a certain sample composition or certain number of waves.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>30440047</pmid><doi>10.1371/journal.pone.0207507</doi><orcidid>https://orcid.org/0000-0003-0502-7993</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Acquired immune deficiency syndrome Adolescent Adult Aged Aged, 80 and over AIDS Child Child, Preschool Communicable Diseases - drug therapy Communicable Diseases - epidemiology Communicable Diseases - pathology Composition Computer Simulation Contact tracing Disease control Empirical analysis Female Health research Health sciences Health surveillance HIV Homosexuality, Male Human immunodeficiency virus Humans Infectious diseases Internet Male Mathematical models Medical personnel Medicine and Health Sciences Middle Aged Pathogens Patient Selection Peer Group People and Places Physical Sciences Population Primary care Probability Public health Questionnaires Recruitment Research and Analysis Methods Respiratory distress syndrome Sampling Studies Seeds Simulation Social networks Social Sciences Sociology Stochasticity Studies Surveys and Questionnaires Trees Vaccines - therapeutic use Young Adult |
| title | A stochastic simulation model to study respondent-driven recruitment |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-08T08%3A16%3A34IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20stochastic%20simulation%20model%20to%20study%20respondent-driven%20recruitment&rft.jtitle=PloS%20one&rft.au=Stein,%20Mart%20L&rft.date=2018-11-15&rft.volume=13&rft.issue=11&rft.spage=e0207507&rft.epage=e0207507&rft.pages=e0207507-e0207507&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0207507&rft_dat=%3Cproquest_plos_%3E2135130167%3C/proquest_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2133833312&rft_id=info:pmid/30440047&rft_doaj_id=oai_doaj_org_article_9672ebf3f9f24ca496f8e4930dee3209&rfr_iscdi=true |