课题基金 / 基金详情

Using data to improve public health: COVID-19 secondment

Using data to improve public health: COVID-19 secondment
利用数据改善公共卫生:COVID-19 借调
批准号:
MR/W021315/1
负责人:
Milla Kibble
金额:
$14.88万
依托单位:
依托单位国家:
英国
项目类别:
Fellowship
财政年份:
2021
资助国家:
英国
项目状态:
已结题
起止时间:
2021 至 --

项目摘要

项目成果

相似基金

相关文献

中文摘要
翻译
点击翻译按钮获取中文摘要
英文摘要
One of the core remits of the COVID-19 Longitudinal Health and Wellbeing National Core Study (LH&W NCS) is to understand the consequences of COVID-19 infection and the role of vaccination in mitigating risks. To gain a better understanding of immunological responses to COVID-19 infection and vaccination, serology data (data on COVID-19 antibody levels) was collected for over 30,000 participants from 11 longitudinal population studies between March and May 2021. In addition, even more in-depth immunological data was collected for a subset of one of these population cohorts, namely the Avon Longitudinal Study of Parents and Children (ALSPAC) - a birth cohort recruited in Bristol in the 1990s. There are many studies of seroprevalence (frequency of individuals with antibodies to COVID-19 in a population), and of immune response, largely recruiting patients discharged from hospital with a COVID-19 diagnosis, or volunteers. However, the current study embedding serology in existing population cohorts can address knowledge gaps including: understanding of milder or asymptomatic disease, lack of appropriate control groups for comparison, concerns regarding generalisability given the selective response to previous serological studies and limited objective pre-pandemic data to understand biological determinants of infection or immune response (e.g. metabolic and inflammatory status). The population cohorts form a rich resource in which to study immunological response as they represent key sectors of the population (spanning age groups, socioeconomic status and ethnicity - Table 1.), findings are demonstrably generalizable, and they have rich pre-pandemic biological and sociodemographic data. The aim of this project is to analyse immunological data sets within longitudinal population cohorts to investigate the biological and societal factors underlying the differential immunological responses to COVID-19 infection or vaccination. We will: (i) evaluate pre- and trans- pandemic health and sociodemographic factors associating with low antibody levels post vaccination; (ii) investigate differences between symptomatic and asymptomatic cases following natural infection; and (iii) examine biological explanations for differential vaccine immunological response. This work aims to help inform vaccination strategies and design as well as decisions regarding the easing of public health control measures.
期刊论文(5)
专著(0)
科研奖励(0)
会议论文
DOI: 10.7554/elife.80428
发表时间: 2023-01-24
期刊: eLife
影响因子: 7.7
作者: [Cheetham NJ, Kibble M, Wong A, Silverwood RJ, Knuppel A, Williams DM, Hamilton OKL, Lee PH, Bridger Staatz C, Di Gessa G, Zhu J, Katikireddi SV, Ploubidis GB, Thompson EJ, Bowyer RCE, Zhang X, Abbasian G, Garcia MP, Hart D, Seow J, Graham C, Kouphou N, Acors S, Malim MH, Mitchell RE, Northstone K, Major-Smith D, Matthews S, Breeze T, Crawford M, Molloy L, Kwong ASF, Doores K, Chaturvedi N, Duncan EL, Timpson NJ, Steves CJ]
通讯作者: Steves CJ
DOI: 10.1007/s10654-022-00962-6
发表时间: 2023-03
期刊: EUROPEAN JOURNAL OF EPIDEMIOLOGY
影响因子: 13.6
作者: [Bowyer, Ruth C. E., Huggins, Charlotte, Toms, Renin, Shaw, Richard J. J., Hou, Bo, Thompson, Ellen J. J., Kwong, Alex S. F., Williams, Dylan M. M., Kibble, Milla, Ploubidis, George B. B., Timpson, Nicholas J. J., Sterne, Jonathan A. C., Chaturvedi, Nishi, Steves, Claire J. J., Tilling, Kate, Silverwood, Richard J. J., CONVALESCENCE Study]
通讯作者: CONVALESCENCE Study
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
Data-driven Recommendation System Construction of an Online Medical Platform Based on the Fusion of Information
复杂数据下半参数转换模型及其在老年慢性病发展中的应用研究
  • 批准号:
    72101261
  • 项目类别:
    青年科学基金项目(C类)
  • 资助金额:
    30.0万元
  • 批准年份:
    2021
  • 负责人:
    孙韬
  • 依托单位:
Development of a Linear Stochastic Model for Wind Field Reconstruction from Limited Measurement Data
  • 批准号:
    --
  • 项目类别:
    --
  • 资助金额:
    40万元
  • 批准年份:
    2020
  • 负责人:
    Vikrant Gupta
  • 依托单位: