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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：
10241253
负责人：
STEVEN J SCHIFF
金额：
$ 165.05万
依托单位：
PENNSYLVANIA STATE UNIV HERSHEY MED CTR
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 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10241253
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，我们已经证明预测是可行的