Predictive Personalized Public Health (P3H): A Novel Paradigm to Treat Infectious Disease

预测性个性化公共卫生（P3H）：治疗传染病的新范式

基本信息

批准号：
10699327
负责人：
STEVEN J SCHIFF
金额：
$ 172.85万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-05 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10699327
关键词：
Acute Address Algorithms Assimilations Bacterial Meningitis Bacteriology Big Data Cessation of life Characteristics Cholera Climate Clinical Trials Communicable Diseases Complex Country Data Decision Making Developed Countries Developing Countries Diagnostic Diarrhea Dimensions Disease Disease Outbreaks Economics Engineering Environmental Risk Factor Epidemic Equilibrium Exanthema Exercise Fever Frequencies Genus staphylococcus Geography HIV Hand Health Policy Hospitals Hybrids Individual Infant Infection Infrastructure Investments Laboratories Leadership Leptospirosis Machine Learning Malaria Measles Medical Medical emergency Melioidosis Meningitis Methods Microbiology Modeling Molecular Morbidity - disease rate Names Newborn Infant Paralysed Patient-Focused Outcomes Patients Physicians Population Prediction of Response to Therapy Preventive Process Public Health Rain Reaction Resistance Resources Role Sepsis Specimen Syndrome System Techniques Technology Testing Time Training Uganda Work acute infection antigen test antimicrobial authority climate data cost cost outcomes data fusion design effective therapy fighting flu global health improved individual patient infant infection infectious disease model infectious disease treatment innovation insight microbial mortality novel open source pathogen personalized medicine personalized predictions point of care predictive modeling prospective resistance gene spreading factor surveillance data

项目摘要

Challenge, Innovation and Impact: In recent years, we have demonstrated that it is feasible to predict epidemic disease outbreaks from retrospective seasonal and geographical case data and to show that we can take climate factors into account in our predictive models. We are moving closer to real-time prediction at the population level. But we have never used prediction at point-of-care for treating the individual patient. Presently, personalized medicine uses delayed results of laboratory testing of individuals. For infectious disease, most of such testing has targeted the pathogen in the host-pathogen interaction. The role of laboratory testing is to modify therapy after a variable period of time delay. Personalized medicine today is reactive. Complicating matters further, many infectious epidemic diseases are strongly dependent on environmental factors and climate. Lastly, we want to name the pathogens we are fighting, but we really need to know the resistance characteristics to select therapy for patients effectively. Both speciation and resistance can now be determined from molecular data, which can be integrated into point-of-care treatment predictions. We here propose a radically different approach to the treatment of infectious diseases. Our hypothesis is that the alternative to time-delayed and expensive laboratory analysis of specimens from individual patients, is to use predictive modeling to forecast point-of-care treatment. Time-delayed personalized testing can be conducted as surveillance, and that data used for real-time prediction to guide point-of-care treatment. We will introduce predictive personalized public health (P3H) policy at the individual patient level, with the potential to substantially improve patient outcomes compared with our present reactive approaches. Our key rationale is to expand population infectious disease predictive modeling in order to achieve prediction for treatment at point-of-care. Our primary insight is that we can reposition the delayed reactive personalized testing from the urgent medical decision-making process, and into a predictive modeling framework. The gaps and opportunities in technology that we will address are four-fold. First, we will employ individual case geospatial mapping at a fine scale to take into account infection spread and environmental factors. Second, our ability to perform pan-microbial analysis using molecular techniques is now feasible. Third, modeling our novel fusion of data has no simple low-dimensional solution – but machine learning technologies are now capable of handling such big data assimilation, model discovery and prediction. Fourth, our proposal is not an academic exercise. We have a partnership with the economic planners within a developing country to design and implement our new methods. We will prospectively tune and validate our algorithms in real-time. Our deliverable will be an open-source framework ready for clinical trials testing and adaptation to the public health infrastructure in any country.

挑战、创新和影响：近年来，我们已经证明可以预测从回顾性季节性和地理案例数据中了解流行病爆发，并表明我们可以在我们的预测模型中考虑气候因素，我们正在接近实时预测。但我们从未在护理点使用预测来治疗个体患者。目前，个性化医疗使用个体实验室检测的延迟结果来检测传染性。疾病，大多数此类测试都针对宿主与病原体相互作用中的病原体。实验室测试是在一段可变的时间延迟后修改治疗，今天的个性化医疗是。使事情变得更加复杂的是，许多传染病都强烈依赖于这种疾病。最后，我们想要命名我们正在对抗的病原体，但我们确实需要。了解耐药特征，以便有效地为患者选择治疗方案。现在可以根据分子数据确定，并将其整合到护理点治疗预测中。我们在此提出一种完全不同的治疗传染病的方法。假设是，对来自样本的耗时且昂贵的实验室分析的替代方案个体患者的方法是使用预测模型来预测即时治疗。可以进行个性化测试作为监测，并使用数据进行实时预测以指导护理点治疗。我们将在个体患者层面引入预测性个性化公共卫生（P3H）政策，与我们目前的反应性方法相比，有可能显着改善患者的治疗结果。我们的关键理由是扩大人群传染病预测模型以实现预测我们的主要见解是我们可以重新定位延迟反应的个性化。从紧急医疗决策过程进行测试，并进入预测建模框架。我们将解决的技术机遇有四个方面：首先，我们将采用个案。精细绘制地理空间地图，考虑感染传播和环境因素。我们使用分子技术进行泛微生物分析的能力现在是可行的。第三，对我们的模型进行建模。新颖的数据融合没有简单的低维解决方案 - 但机器学习技术现在第四，我们的建议不是一个能够处理此类大数据同化、模型发现和预测的能力。我们与发展中国家的经济规划者合作进行设计。并实施我们的新方法。我们将前瞻性地实时调整和验证我们的算法。可交付成果将是一个开源框架，可供临床试验测试和适应公共卫生任何国家的基础设施。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Congenital Cytomegalovirus Infection Burden and Epidemiologic Risk Factors in Countries With Universal Screening: A Systematic Review and Meta-analysis.

DOI：
10.1001/jamanetworkopen.2021.20736
发表时间：
2021-08-02
期刊：
JAMA network open
影响因子：
13.8
作者：
Ssentongo P;Hehnly C;Birungi P;Roach MA;Spady J;Fronterre C;Wang M;Murray-Kolb LE;Al-Shaar L;Chinchilli VM;Broach JR;Ericson JE;Schiff SJ
通讯作者：
Schiff SJ

Short-term and Long-term Rates of Postacute Sequelae of SARS-CoV-2 Infection: A Systematic Review.

DOI：
10.1001/jamanetworkopen.2021.28568
发表时间：
2021-10-01
期刊：
JAMA network open
影响因子：
13.8
作者：
Groff D;Sun A;Ssentongo AE;Ba DM;Parsons N;Poudel GR;Lekoubou A;Oh JS;Ericson JE;Ssentongo P;Chinchilli VM
通讯作者：
Chinchilli VM

Pan-African evolution of within- and between-country COVID-19 dynamics.

DOI：
10.1073/pnas.2026664118
发表时间：
2021-07-13
期刊：
Proceedings of the National Academy of Sciences of the United States of America
影响因子：
11.1
作者：
Ssentongo P;Fronterre C;Geronimo A;Greybush SJ;Mbabazi PK;Muvawala J;Nahalamba SB;Omadi PO;Opar BT;Sinnar SA;Wang Y;Whalen AJ;Held L;Jewell C;Muwanguzi AJB;Greatrex H;Norton MM;Diggle PJ;Schiff SJ
通讯作者：
Schiff SJ

mbQTL: an R/Bioconductor package for microbial quantitative trait loci (QTL) estimation.

DOI：
10.1093/bioinformatics/btad565
发表时间：
2023-09-02
期刊：
Bioinformatics (Oxford, England)
影响因子：
0
作者：
通讯作者：

Stabilizing the return to normal behavior in an epidemic.

在流行病中稳定恢复正常行为。

DOI：
10.1101/2023.03.13.23287222
发表时间：
2023
期刊：
medRxiv : the preprint server for health sciences
影响因子：
0
作者：
Berry,Tyrus;Ferrari,Matthew;Sauer,Timothy;Greybush,StevenJ;Ebeigbe,Donald;Whalen,AndrewJ;Schiff,StevenJ
通讯作者：
Schiff,StevenJ