Tuberculosis in households with infectious cases in Kampala city: Harnessing health data science for new insights on TB transmission and treatment response (DS-IAFRICA-TB)

坎帕拉市感染病例家庭中的结核病:利用健康数据科学获得有关结核病传播和治疗反应的新见解 (DS-IAFRICA-TB)

基本信息

项目摘要

Abstract: Tuberculosis (TB) is prevalent in Uganda, and overlaps with an already high burden of HIV/TB coinfection. While almost all hospital-based TB cases in Kampala city, the capital of Uganda, have clear TB symptoms, 30% or more of the people with undiagnosed TB, identified through active case finding, are asymptomatic for TB; moreover, the host risk factors for TB in Kampala cannot be distinguished from risk factors associated with the environment. Complicating this further is the fact that anti-TB treatment failure rates are higher in Uganda by several order of magnitude, compared to global estimates (17% vs. 10%). These TB-specific challenges depict only a fraction of the complexity underlying the disease, especially in endemic settings with a high burden of HIV/AIDS. Data science methods, especially Artificial Intelligence (AI) and/or Machine Learning algorithms, can unravel such complexity and untangle factors of the host, pathogen and environment underlying TB, which hitherto, have been difficult to explain or predict with conventional approaches. In this proposal, we will harness health data science and elucidate factors underlying transmission of TB in a household, as well as anti-TB treatment failure. We will leverage the computational infrastructure at Makerere, and available demographic, clinical and laboratory data sets from TB patients and their contacts, and develop AI/Machine Learning algorithms that identify: (1) Patients at baseline (month 0) who would not sputum and/or culture convert at months 2 and 5, hence are at risk of failing TB treatment, (2) Contacts of index-TB cases who are at risk of developing household TB disease, as well as contacts who could be resistant to TB infection despite persistent and/or multiple exposure to M. tuberculosis in a household. Answering these aims provides the required evidence that data science methods are effective at early identification of potential TB cases and high-cost patients, hence contribute to halting of TB transmission in the community.
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项目成果

期刊论文数量(0)
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会议论文数量(0)
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David Patrick Kateete其他文献

Enterococcus and Eggerthella species are enriched in the gut microbiomes of COVID-19 cases in Uganda
  • DOI:
    10.1186/s13099-025-00678-4
  • 发表时间:
    2025-02-04
  • 期刊:
  • 影响因子:
    4.000
  • 作者:
    Carolina Agudelo;David Patrick Kateete;Emmanuel Nasinghe;Rogers Kamulegeya;Christopher Lubega;Monica Mbabazi;Noah Baker;Kathryn Y. Lin;Chang C. Liu;Arthur Shem Kasambula;Edgar Kigozi;Kevin Komakech;John Mukisa;Kassim Mulumba;Patricia Mwachan;Brenda Sharon Nakalanda;Gloria Patricia Nalubega;Julius Nsubuga;Diana Sitenda;Henry Ssenfuka;Giana T. Cirolia;Jeshua T. Gustafson;Ruohong Wang;Moses Luutu Nsubuga;Fahim Yiga;Sarah A. Stanley;Bernard Ssentalo Bagaya;Alison Elliott;Moses Joloba;Ashley R. Wolf
  • 通讯作者:
    Ashley R. Wolf
Isoniazid preventive therapy modulates Mycobacterium tuberculosis-specific T-cell responses in individuals with latent tuberculosis and type 2 diabetes
  • DOI:
    10.1038/s41598-025-95386-z
  • 发表时间:
    2025-03-26
  • 期刊:
  • 影响因子:
    3.900
  • 作者:
    Phillip Ssekamatte;Diana Sitenda;Rose Nabatanzi;Marjorie Nakibuule;Davis Kibirige;Andrew Peter Kyazze;David Patrick Kateete;Bernard Ssentalo Bagaya;Obondo James Sande;Reinout van Crevel;Stephen Cose;Irene Andia Biraro
  • 通讯作者:
    Irene Andia Biraro
Machine learning-based prediction of antibiotic resistance in Mycobacterium tuberculosis clinical isolates from Uganda
  • DOI:
    10.1186/s12879-024-10282-7
  • 发表时间:
    2024-12-05
  • 期刊:
  • 影响因子:
    3.000
  • 作者:
    Sandra Ruth Babirye;Mike Nsubuga;Gerald Mboowa;Charles Batte;Ronald Galiwango;David Patrick Kateete
  • 通讯作者:
    David Patrick Kateete
Phylogenetic groups and antimicrobial susceptibility patterns of uropathogenic Escherichia coli clinical isolates from patients at Mulago National Referral Hospital, Kampala, Uganda
乌干达坎帕拉穆拉戈国家转诊医院患者临床分离的尿路致病性大肠杆菌的系统发育群体和抗菌药物敏感性模式
  • DOI:
    10.12688/f1000research.20930.1
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Paul Katongole;Daniel Bulwadda Kisawuzi;Henry Kyobe Bbosa;David Patrick Kateete;Christine Florence Najjuka
  • 通讯作者:
    Christine Florence Najjuka

David Patrick Kateete的其他文献

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