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.

Public Health Relevance

We have made substantial progress in our abilities to track and predict infectious disease epidemics at the population level, but prediction has never been used at the individual level to guide treatment at point-of-care. The individual treatment for all infectious disease syndromes at point-of-care is presently empirical, because laboratory testing is reactive and time delayed. We here propose a novel framework to use rigorous prediction strategies to guide point-of-care treatment for infectious diseases, and to validate this approach under real-world conditions.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Research Project (R01)
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Special Emphasis Panel (ZRG1)
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Brown, Liliana L
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Pennsylvania State University
Schools of Medicine
United States
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