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

> **NIH NIH R01** · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · 2021 · $1,650,504

## Abstract

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 th...

## Key facts

- **NIH application ID:** 10241253
- **Project number:** 5R01AI145057-04
- **Recipient organization:** PENNSYLVANIA STATE UNIV HERSHEY MED CTR
- **Principal Investigator:** STEVEN J SCHIFF
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,650,504
- **Award type:** 5
- **Project period:** 2018-09-05 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10241253

## Citation

> US National Institutes of Health, RePORTER application 10241253, Predictive Personalized Public Health (P3H): A Novel Paradigm to Treat Infectious Disease (5R01AI145057-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10241253. Licensed CC0.

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