# Deconvolution and reconstruction of immune histories to enhance infectious disease prevention and vaccination strategies and optimize surveillance efforts

> **NIH NIH DP5** · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · 2020 · $398,750

## Abstract

PROJECT SUMMARY
Infectious diseases remain among the greatest threats to human health. Novel epidemics occur with increasing
frequency as ease of travel facilitates spread, and environmental changes alter underlying dynamics in
unpredictable ways. At the same time, vaccines are increasingly controlling many major human pathogens. Yet,
the potential of these advances can only be fully realized with a means to accurately measure and quantify the
landscape of infectious diseases across many pathogens and scales, from the individual to the global population,
and encompassing interactions and potential unintended consequences across related or unrelated pathogens.
 The overall objectives are to bridge novel developments in molecular biology and computational tools to
fundamentally improve infectious disease surveillance and research. Aim 1 will build on a previously reported
phage display system, and optimize it for infectious disease surveillance. PADERNS (Phage display for Antibody
repertoire Detection and profiling via pathogen Epitope RecognitioN for infectious disease and public health
Surveillance) will: enable serological surveillance for exposures to all human pathogens, including bacteria,
parasites and mosquitos vectors, simultaneously, from accessible samples, i.e. saliva and dried blood spots;
will discriminate exposures from closely related pathogens (i.e. Zika and Dengue) and estimate time since
infection or vaccination. Importantly, it will be optimized for use in low resource settings and at a fraction of the
cost of current technologies. Aim 2 will improve epidemic detection with development of Epi-TRACER (Epitope
based TRacking of Anonymous samples via Comprehensive Epitope Recogntion). Epi-TRACER will use
PADERNS repertoires from (1) to extract and construct epidemiologically powerful ‘virtual longitudinal cohorts’
from cross-sectional sample sets that contain ‘hidden’ serial samples (i.e. 80% of the US blood supply comes
from repeat donors). Because Epi-TRACER runs on PADERNS data, it will simultaneously enable reconstruction
of past, and early detection of current epidemics. Aim 3 will elucidate the life-histories of pathogen exposures
across ages (pre-birth to elderly), time, genders and geographies to: quantify in unprecedented detail pathogen
attack rates, heterologous effects of vaccines on off-target pathogens, and measure the longevity and waning of
antibodies, including maternally derived antibodies, to improving vaccination and control strategies.
 These aims will be accomplished through a multi-disciplinary approach involving molecular biology and
phage-display systems, robust longitudinal sample curation through collaborations, and, crucially, careful
development of mathematical and statistical models to link the biology to individual and population level
inference. Once complete, the tools, methods and data will be available to public health agencies and infectious
disease researchers, opening the way to a step change i...

## Key facts

- **NIH application ID:** 10018944
- **Project number:** 5DP5OD028145-02
- **Recipient organization:** HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH
- **Principal Investigator:** Michael J. Mina
- **Activity code:** DP5 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $398,750
- **Award type:** 5
- **Project period:** 2019-09-16 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10018944

## Citation

> US National Institutes of Health, RePORTER application 10018944, Deconvolution and reconstruction of immune histories to enhance infectious disease prevention and vaccination strategies and optimize surveillance efforts (5DP5OD028145-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10018944. Licensed CC0.

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