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Maximizing Investigators' Research Award (R35)

最大化研究者研究奖（R35）

基本信息

批准号：
10630947
负责人：
Laura Forsberg White
金额：
$ 41.25万
依托单位：
BOSTON UNIVERSITY MEDICAL CAMPUS
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-01 至 2026-05-31
项目状态：
未结题

项目摘要

PROJECT SUMMARY The “Tools for Transmission of Agents and Conditions (TRAC)” program will synergize statistical and mathematical modeling work in three areas of application: 1) Tuberculosis (TB) incidence and transmission; 2) monitoring substance use disorder (SUD) patterns; and 3) SARS CoV-2 transmission modeling. These three conditions are major public health problems, with TB being the leading cause of infectious disease death globally, SUD causing more deaths in the United States than HIV/AIDS in its peak, and SARS CoV-2 causing a pandemic with societal disruption and mortality exceeding anything we have experienced in the last century. We need improved analytical tools that leverage existing data to monitor these diseases, infer transmission hot spots, determine the efficacy of interventions, and understand the burden of these conditions. This program will bring together an expert group of quantitative researchers with skills that are readily applied to these problems. We also leverage our strong collaborations with clinician researchers and public health officials to ensure that the methods we develop are addressing important questions and consistent with our current understanding of these diseases. By creating a program to facilitate communication between these experts, we will enable greater innovation in modeling key aspects of these diseases and create exciting methodological synergies across diseases. Our team is well positioned to incorporate data from emerging technologies, including high throughput sequencing data to determine TB risk signatures and inform transmission links for TB and SARS CoV-2. Our expertise in machine learning, a broad range of statistical methodologies, and mathematical modeling will enable us to leverage the rich information in large databases that are emerging to better understand SUD patterns and identify risk signatures. We will also build infrastructure with our partners to make the analytical tools that we develop more accessible to public health practitioners and other researchers. The impact of this work is to develop a suite of analytical tools that leverage rapidly emerging rich data sets to improve our understanding of disease transmission patterns, monitor changing dynamics of these conditions, and understand intervention strategies that are most effective. This work will inform public health practice for these diseases and create reproducible tools that can be used in an ongoing way.

项目总结

项目成果

期刊论文数量（12）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Antibody escape, the risk of serotype formation, and rapid immune waning: Modeling the implications of SARS-CoV-2 immune evasion.

抗体逃逸，血清型形成的风险和快速免疫减弱：建模SARS-COV-2免疫逃避的含义。

DOI：
10.1371/journal.pone.0292099
发表时间：
2023
期刊：
PloS one
影响因子：
3.7
作者：
通讯作者：

Reproducible Science Is Vital for a Stronger Evidence Base During the COVID-19 Pandemic.

在 COVID-19 大流行期间，可重复的科学对于建立更强有力的证据基础至关重要。

DOI：
10.1111/gean.12314
发表时间：
2021
期刊：
Geographical analysis
影响因子：
3.6
作者：
Sy,KarlaThereseL;White,LauraF;Nichols,BrookeE
通讯作者：
Nichols,BrookeE

Heterogeneity in Vaccinal Immunity to SARS-CoV-2 Can Be Addressed by a Personalized Booster Strategy.

DOI：
10.3390/vaccines11040806
发表时间：
2023-04-06
期刊：
Vaccines
影响因子：
7.8
作者：
Stoddard M;Yuan L;Sarkar S;Mangalaganesh S;Nolan RP;Bottino D;Hather G;Hochberg NS;White LF;Chakravarty A
通讯作者：
Chakravarty A

Beyond the new normal: Assessing the feasibility of vaccine-based suppression of SARS-CoV-2.

DOI：
10.1371/journal.pone.0254734
发表时间：
2021
期刊：
PloS one
影响因子：
3.7
作者：
Stoddard M;Sarkar S;Yuan L;Nolan RP;White DE;White LF;Hochberg NS;Chakravarty A
通讯作者：
Chakravarty A

Estimation of local time-varying reproduction numbers in noisy surveillance data.

噪声监视数据中局部时变繁殖数的估计。