Estimating the contribution of subclinical tuberculosis disease to transmission: An individual patient data analysis from prevalence surveys.
Jon C EmeryPeter J DoddSayera BanuBeatrice FrascellaFrances L GardenKatherine C HortonShahed HossainIrwin LawFrank van LethGuy B MarksHoa Binh NguyenHai Viet NguyenIkushi OnozakiMaria Imelda D QuelapioAlexandra S RichardsNabila ShaikhEdine W TiemersmaRichard G WhiteKhalequ ZamanFrank CobelensRein M G J HoubenPublished in: eLife (2023)
JCE, KCH, ASR, NS, and RH have received funding from the European Research Council (ERC) under the Horizon 2020 research and innovation programme (ERC Starting Grant No. 757699) KCH is also supported by UK FCDO (Leaving no-one behind: transforming gendered pathways to health for TB). This research has been partially funded by UK aid from the UK government (to KCH); however, the views expressed do not necessarily reflect the UK government's official policies. PJD was supported by a fellowship from the UK Medical Research Council (MR/P022081/1); this UK-funded award is part of the EDCTP2 programme supported by the European Union. RGW is funded by the Wellcome Trust (218261/Z/19/Z), NIH (1R01AI147321-01), EDTCP (RIA208D-2505B), UK MRC (CCF17-7779 via SET Bloomsbury), ESRC (ES/P008011/1), BMGF (OPP1084276, OPP1135288 and INV-001754), and the WHO (2020/985800-0).
Keyphrases
- cross sectional
- data analysis
- healthcare
- public health
- mycobacterium tuberculosis
- emergency department
- randomized controlled trial
- magnetic resonance
- clinical trial
- risk assessment
- computed tomography
- hiv infected
- study protocol
- artificial intelligence
- case report
- dengue virus
- zika virus
- contrast enhanced
- deep learning