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
- 批准号:10713181
- 负责人:
- 金额:$ 25万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-09-01 至 2026-05-31
- 项目状态:未结题
- 来源:
- 关键词:AgeAlgorithmsAntibioticsAntitubercular AgentsArtificial IntelligenceBody mass indexCapitalCaringCitiesClinicalColony-forming unitsCommunicable DiseasesCommunitiesComputersConsumptionDataData ScienceData ScientistData SetDatabasesDiagnosticDiseaseDrug resistanceEarly identificationEnsureEnvironmentEpidemiologistExposure toFecesGenderGroupingHIVHIV/AIDSHIV/TBHealth PrioritiesHigh-Throughput DNA SequencingHospitalsHouseholdImageIndividualIntegration Host FactorsInternationalInterventionInterviewerLaboratoriesMachine LearningMedicineMethodsModelingMolecularMycobacterium tuberculosisOutcomeParticipantPatientsPatternPeripheral Blood Mononuclear CellPersonsPharmaceutical PreparationsPhysiciansPopulationProcessQuestionnairesResearchResearch PersonnelResistanceRibosomal RNARiskRisk FactorsSamplingScienceScientistSputumStructureSymptomsTechniquesThoracic RadiographyTreatment FailureTreatment outcomeTreesTriageTuberculosisUgandaUniversitiesWhole BloodX-Ray Medical Imagingcase findingclinically relevantco-infectioncohortcomorbiditycomputer infrastructurecostcytokinedeep learning modeldiverse datafeature selectionhealth datahigh riskindexinginnovationinsightlearning algorithmlow income countrymachine learning algorithmmachine learning modelmicrobiomemultidisciplinarynon-compliancepathogensocialsocial factorssupervised learningsupport vector machinetransmission processtreatment responsetuberculosis treatmentunsupervised learningwhole genome
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.
摘要:
结核病(TB)在乌干达很流行,与艾滋病毒/结核病合并感染已经很高的负担重叠。而
在乌干达首都坎帕拉市,几乎所有的医院结核病病例都有明显的结核病症状,
通过主动病例发现发现的未确诊结核病患者中,有更多人没有结核病症状;
此外,坎帕拉结核病的宿主风险因素与与结核病相关的风险因素无法区分。
环境使这一情况进一步复杂化的是,乌干达的抗结核治疗失败率较高,
与全球估计数相比,这一比例高出几个数量级(17%对10%)。这些结核病特有的挑战描述了
只有一小部分的复杂性,潜在的疾病,特别是在地方性环境中的高负担,
艾滋病毒/艾滋病的数据科学方法,特别是人工智能(AI)和/或机器学习算法,可以
揭示了结核病的宿主、病原体和环境的复杂性,
迄今为止,难以用常规方法来解释或预测。在这个提议中,我们将利用
健康数据科学和阐明结核病在家庭中传播的潜在因素,以及抗结核病
治疗失败。我们将利用Makerere的计算基础设施和可用的人口统计,
结核病患者及其接触者的临床和实验室数据集,并开发人工智能/机器学习
识别:(1)基线(第0个月)时痰和/或培养物在第2个月时未转化的患者
2和5,因此有结核病治疗失败的风险,(2)有发展成结核病风险的索引结核病病例的接触者
家庭结核病,以及接触者谁可能耐结核感染,尽管持续和/或
多次暴露于M.肺结核在一个家庭实现这些目标提供了必要的证据,
数据科学方法在早期识别潜在结核病病例和高成本患者方面是有效的,
有助于阻止结核病在社区的传播。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
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
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
相似海外基金
DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
- 批准号:
EP/Y029089/1 - 财政年份:2024
- 资助金额:
$ 25万 - 项目类别:
Research Grant
CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
- 批准号:
2337776 - 财政年份:2024
- 资助金额:
$ 25万 - 项目类别:
Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
- 批准号:
2338816 - 财政年份:2024
- 资助金额:
$ 25万 - 项目类别:
Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
- 批准号:
2338846 - 财政年份:2024
- 资助金额:
$ 25万 - 项目类别:
Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
- 批准号:
2348261 - 财政年份:2024
- 资助金额:
$ 25万 - 项目类别:
Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
- 批准号:
2348346 - 财政年份:2024
- 资助金额:
$ 25万 - 项目类别:
Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
- 批准号:
2348457 - 财政年份:2024
- 资助金额:
$ 25万 - 项目类别:
Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
- 批准号:
2404989 - 财政年份:2024
- 资助金额:
$ 25万 - 项目类别:
Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
- 批准号:
2339310 - 财政年份:2024
- 资助金额:
$ 25万 - 项目类别:
Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
- 批准号:
2339669 - 财政年份:2024
- 资助金额:
$ 25万 - 项目类别:
Continuing Grant