Optimal resource allocation in HIV self-testing secondary distribution among Chinese MSM: data-driven integer programming models
Human immunodeficiency virus self-testing (HIVST) is an innovative and effective strategy important to the expansion of HIV testing coverage. Several innovative implementations of HIVST have been developed and piloted among some HIV high-risk populations like men who have sex with men (MSM) to meet...
Uložené v:
| Vydané v: | Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences Ročník 380; číslo 2214; s. 20210128 |
|---|---|
| Hlavní autori: | , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
England
10.01.2022
|
| Predmet: | |
| ISSN: | 1471-2962, 1471-2962 |
| On-line prístup: | Zistit podrobnosti o prístupe |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Human immunodeficiency virus self-testing (HIVST) is an innovative and effective strategy important to the expansion of HIV testing coverage. Several innovative implementations of HIVST have been developed and piloted among some HIV high-risk populations like men who have sex with men (MSM) to meet the global testing target. One innovative strategy is the secondary distribution of HIVST, in which individuals (defined as indexes) were given multiple testing kits for both self-use (i.e.self-testing) and distribution to other people in their MSM social network (defined as alters). Studies about secondary HIVST distribution have mainly concentrated on developing new intervention approaches to further increase the effectiveness of this relatively new strategy from the perspective of traditional public health discipline. There are many points of HIVST secondary distribution in which mathematical modelling can play an important role. In this study, we considered secondary HIVST kits distribution in a resource-constrained situation and proposed two data-driven integer linear programming models to maximize the overall economic benefits of secondary HIVST kits distribution based on our present implementation data from Chinese MSM. The objective function took expansion of normal alters and detection of positive and newly-tested 'alters' into account. Based on solutions from solvers, we developed greedy algorithms to find final solutions for our linear programming models. Results showed that our proposed data-driven approach could improve the total health economic benefit of HIVST secondary distribution. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'. |
|---|---|
| AbstractList | Human immunodeficiency virus self-testing (HIVST) is an innovative and effective strategy important to the expansion of HIV testing coverage. Several innovative implementations of HIVST have been developed and piloted among some HIV high-risk populations like men who have sex with men (MSM) to meet the global testing target. One innovative strategy is the secondary distribution of HIVST, in which individuals (defined as indexes) were given multiple testing kits for both self-use (i.e.self-testing) and distribution to other people in their MSM social network (defined as alters). Studies about secondary HIVST distribution have mainly concentrated on developing new intervention approaches to further increase the effectiveness of this relatively new strategy from the perspective of traditional public health discipline. There are many points of HIVST secondary distribution in which mathematical modelling can play an important role. In this study, we considered secondary HIVST kits distribution in a resource-constrained situation and proposed two data-driven integer linear programming models to maximize the overall economic benefits of secondary HIVST kits distribution based on our present implementation data from Chinese MSM. The objective function took expansion of normal alters and detection of positive and newly-tested 'alters' into account. Based on solutions from solvers, we developed greedy algorithms to find final solutions for our linear programming models. Results showed that our proposed data-driven approach could improve the total health economic benefit of HIVST secondary distribution. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.Human immunodeficiency virus self-testing (HIVST) is an innovative and effective strategy important to the expansion of HIV testing coverage. Several innovative implementations of HIVST have been developed and piloted among some HIV high-risk populations like men who have sex with men (MSM) to meet the global testing target. One innovative strategy is the secondary distribution of HIVST, in which individuals (defined as indexes) were given multiple testing kits for both self-use (i.e.self-testing) and distribution to other people in their MSM social network (defined as alters). Studies about secondary HIVST distribution have mainly concentrated on developing new intervention approaches to further increase the effectiveness of this relatively new strategy from the perspective of traditional public health discipline. There are many points of HIVST secondary distribution in which mathematical modelling can play an important role. In this study, we considered secondary HIVST kits distribution in a resource-constrained situation and proposed two data-driven integer linear programming models to maximize the overall economic benefits of secondary HIVST kits distribution based on our present implementation data from Chinese MSM. The objective function took expansion of normal alters and detection of positive and newly-tested 'alters' into account. Based on solutions from solvers, we developed greedy algorithms to find final solutions for our linear programming models. Results showed that our proposed data-driven approach could improve the total health economic benefit of HIVST secondary distribution. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'. Human immunodeficiency virus self-testing (HIVST) is an innovative and effective strategy important to the expansion of HIV testing coverage. Several innovative implementations of HIVST have been developed and piloted among some HIV high-risk populations like men who have sex with men (MSM) to meet the global testing target. One innovative strategy is the secondary distribution of HIVST, in which individuals (defined as indexes) were given multiple testing kits for both self-use (i.e.self-testing) and distribution to other people in their MSM social network (defined as alters). Studies about secondary HIVST distribution have mainly concentrated on developing new intervention approaches to further increase the effectiveness of this relatively new strategy from the perspective of traditional public health discipline. There are many points of HIVST secondary distribution in which mathematical modelling can play an important role. In this study, we considered secondary HIVST kits distribution in a resource-constrained situation and proposed two data-driven integer linear programming models to maximize the overall economic benefits of secondary HIVST kits distribution based on our present implementation data from Chinese MSM. The objective function took expansion of normal alters and detection of positive and newly-tested 'alters' into account. Based on solutions from solvers, we developed greedy algorithms to find final solutions for our linear programming models. Results showed that our proposed data-driven approach could improve the total health economic benefit of HIVST secondary distribution. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'. |
| Author | Cheng, Mengyuan Jing, Fengshi Zhang, Qingpeng Zhou, Yi Huang, Shanzi Tang, Weiming Xie, Yewei Ni, Yuxin Ong, Jason J |
| Author_xml | – sequence: 1 givenname: Fengshi orcidid: 0000-0002-6747-6527 surname: Jing fullname: Jing, Fengshi organization: School of Data Science, City University of Hong Kong, Hong Kong SAR, People's Republic of China – sequence: 2 givenname: Qingpeng orcidid: 0000-0002-6819-0686 surname: Zhang fullname: Zhang, Qingpeng organization: School of Data Science, City University of Hong Kong, Hong Kong SAR, People's Republic of China – sequence: 3 givenname: Jason J orcidid: 0000-0001-5784-7403 surname: Ong fullname: Ong, Jason J organization: Central Clinical School, Monash University, Melbourne, Australia – sequence: 4 givenname: Yewei orcidid: 0000-0002-9280-5812 surname: Xie fullname: Xie, Yewei organization: Duke Global Health Institute, Duke University, Durham, NC, USA – sequence: 5 givenname: Yuxin surname: Ni fullname: Ni, Yuxin organization: University of North Carolina Project-China, Guangzhou, People's Republic of China – sequence: 6 givenname: Mengyuan surname: Cheng fullname: Cheng, Mengyuan organization: University of North Carolina Project-China, Guangzhou, People's Republic of China – sequence: 7 givenname: Shanzi surname: Huang fullname: Huang, Shanzi organization: Zhuhai Center for Diseases Control and Prevention, Zhuhai, People's Republic of China – sequence: 8 givenname: Yi surname: Zhou fullname: Zhou, Yi organization: Faculty of Medicine, Macau University of Science and Technology, Macau SAR, People's Republic of China – sequence: 9 givenname: Weiming orcidid: 0000-0002-9026-707X surname: Tang fullname: Tang, Weiming organization: Division of Infectious Diseases, Department of Medicine, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34802269$$D View this record in MEDLINE/PubMed |
| BookMark | eNpNkDlPAzEQhS0EIhBoKZFLmg22s5fpUMQlEaXgaFdjezYY7drB9iKl46ezXBLVzOg9fZr3Dsmu8w4JOeFsxpmsz0NMMBNM8Bnjot4hBzyveCZkKXb_7RNyGOMrY5yXhdgnk3leMyFKeUA-Vptke-howOiHoJFC13kNyXpHraO3d880YtdmCWOybj0e2jsDYUuNjSlYNXxbofejuHixDiPS5cPyghpIkJlg3_GLlHCNgW6CXwfo-y9S7w128YjstdBFPP6dU_J0ffW4uM3uVzd3i8v7TBdzmTKp5rLVomRC1XquK5WDNDBKWipdK1XJVpkCWuCmzUvFAKs8h7woShSS51pMydkPd3zhbRjDNL2NGrsOHPohNiOa1VzWVTFaT3-tg-rRNJswNhS2zV9r4hM-J3Pt |
| CitedBy_id | crossref_primary_10_3389_fdgth_2025_1618781 crossref_primary_10_3390_a17080362 crossref_primary_10_1080_15381501_2025_2488774 crossref_primary_10_2196_50656 |
| ContentType | Journal Article |
| DBID | CGR CUY CVF ECM EIF NPM 7X8 |
| DOI | 10.1098/rsta.2021.0128 |
| DatabaseName | Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
| DatabaseTitle | MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | no_fulltext_linktorsrc |
| Discipline | Engineering Mathematics Sciences (General) Physics |
| EISSN | 1471-2962 |
| ExternalDocumentID | 34802269 |
| Genre | Journal Article |
| GeographicLocations | China |
| GeographicLocations_xml | – name: China |
| GroupedDBID | --- -~X 0R~ 18M 2WC 4.4 5VS AACGO AANCE ABFAN ABPLY ABTLG ABYWD ACGFO ACIWK ACMTB ACNCT ACQIA ACTMH ADBBV AEUPB AFVYC ALMA_UNASSIGNED_HOLDINGS ALMYZ BTFSW CGR CUY CVF DIK EBS ECM EIF F5P H13 HH5 HZ~ JLS JSG JST KQ8 MRS MV1 NPM NSAHA O9- OK1 OP1 P2P RHF RRY TN5 TR2 V1E W8F XSW YNT ~02 7X8 |
| ID | FETCH-LOGICAL-c539t-9b39fc2602b8c3c7b4a9da539c9bc8bb79fbd5afa1df46b0ae744a4556e2914c2 |
| IEDL.DBID | 7X8 |
| ISICitedReferencesCount | 7 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000720844400007&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1471-2962 |
| IngestDate | Thu Oct 02 09:15:10 EDT 2025 Wed Feb 19 02:28:07 EST 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2214 |
| Keywords | secondary distribution HIV self-testing integer programming greedy algorithm mathematical optimization |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c539t-9b39fc2602b8c3c7b4a9da539c9bc8bb79fbd5afa1df46b0ae744a4556e2914c2 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0002-9026-707X 0000-0002-6819-0686 0000-0001-5784-7403 0000-0002-6747-6527 0000-0002-9280-5812 |
| OpenAccessLink | https://royalsocietypublishing.org/doi/full/10.1098/rsta.2021.0128 |
| PMID | 34802269 |
| PQID | 2600819875 |
| PQPubID | 23479 |
| ParticipantIDs | proquest_miscellaneous_2600819875 pubmed_primary_34802269 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-01-10 |
| PublicationDateYYYYMMDD | 2022-01-10 |
| PublicationDate_xml | – month: 01 year: 2022 text: 2022-01-10 day: 10 |
| PublicationDecade | 2020 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England |
| PublicationTitle | Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences |
| PublicationTitleAlternate | Philos Trans A Math Phys Eng Sci |
| PublicationYear | 2022 |
| SSID | ssj0011652 |
| Score | 2.4027958 |
| Snippet | Human immunodeficiency virus self-testing (HIVST) is an innovative and effective strategy important to the expansion of HIV testing coverage. Several... |
| SourceID | proquest pubmed |
| SourceType | Aggregation Database Index Database |
| StartPage | 20210128 |
| SubjectTerms | China HIV Infections - diagnosis HIV Infections - epidemiology Homosexuality, Male Humans Male Resource Allocation Self-Testing Sexual and Gender Minorities |
| Title | Optimal resource allocation in HIV self-testing secondary distribution among Chinese MSM: data-driven integer programming models |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/34802269 https://www.proquest.com/docview/2600819875 |
| Volume | 380 |
| WOSCitedRecordID | wos000720844400007&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NT9swFLcGDIkdGJ9bYSBP4gAH08RxHJsLQtMQSGtXiQ_1VsVfqBJNu6QgcdufPj_XBS5Ik7gksaJYVp79s_3e8--H0IHRfmKxiSJwJ4zljkirGJGUW8FznWSB7Pn2V9Htin5f9qLDrYlplXNMDEBtxhp85G0gUhewQ85PJ38IqEZBdDVKaCygpcwvZSClq-i_RBFSHhR3Ug_AhEpOn0kbRRuOjfjdIU2PAaHfXl6Gaeb883sbuIZW4wITn816xDr6YKsN9OkV7aAvdZ65WpsNtBySQOFpPQ70Bh9GNuqjTfT3tweVka-xjo5-DKH6maMPDyt8cXmLG3vvyBT4Oqo7X_BbbFPWT9gAKW_U08JB1QiDXLdtLO5cdU4wZKcSUwPe4kBbYWsc88VGUFNQ6Wm20M35z-sfFyTKNhCdZ3JKpMqk0_53UCV0pgvFSmlK_0pLpYVShXTK5KUrU-MYV0lpC8ZKlufcUpkyTbfRYjWu7FeEaWaN8PbihjvmdCZULk1igaSfKiZcC32f22LghwXEOsrKjh-awYs1WujLzKCDyYy_Y5AxOF_M5c5_fL2LVigceEgg8e8bWnIeFOwe-qgfp8Om3g_9zV-7vc4_A27h7w |
| linkProvider | ProQuest |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Optimal+resource+allocation+in+HIV+self-testing+secondary+distribution+among+Chinese+MSM%3A+data-driven+integer+programming+models&rft.jtitle=Philosophical+transactions+of+the+Royal+Society+of+London.+Series+A%3A+Mathematical%2C+physical%2C+and+engineering+sciences&rft.au=Jing%2C+Fengshi&rft.au=Zhang%2C+Qingpeng&rft.au=Ong%2C+Jason+J&rft.au=Xie%2C+Yewei&rft.date=2022-01-10&rft.issn=1471-2962&rft.eissn=1471-2962&rft.volume=380&rft.issue=2214&rft.spage=20210128&rft_id=info:doi/10.1098%2Frsta.2021.0128&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1471-2962&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1471-2962&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1471-2962&client=summon |