# Modelled estimation of population immunity for coccidioidomycosis and the role of immunologically naïve populations in the shifting epidemiology of coccidioidomycosis in California

> **NIH NIH K01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $118,754

## Abstract

PROJECT SUMMARY
This K01 award will support the career development of Dr. Jennifer Head, an Epidemiologist in the Division of
Environmental Health Sciences at the University of California, Berkeley. The candidate’s goal is to become a
leader in the development and application of mathematical and statistical approaches to understand the
transmission dynamics of emerging infectious diseases. This application proposes linked career development
and research activities to fill an important gap in understanding population immunity to coccidioidomycosis, an
emerging infectious disease caused by inhalation of soil-dwelling Coccidioides spores. While CDC estimates
nearly 150,000 cases of coccidioidomycosis go uncounted each year, direct methods for estimating population
immunity for coccidioidomycosis are unavailable. Absent estimates of prior immunity for coccidioidomycosis,
we are unable to identify populations experiencing higher rates of under-reporting, understand the role of low
population immunity in the emergence of the disease in new regions, nor explain disparities in infection risk
and case severity. This project aims to advance the candidate’s expertise and skills in computational methods
for robustly estimating prior immunity in populations and incorporating population immunity into models
examining key drivers of disease. The candidate will develop a quantitative Bayesian framework for estimating
prior immunity to coccidioidomycosis by geographic region in California (Aim 1). The candidate will extend the
framework to identify disparities in under-reporting by race and ethnicity, yielding a more accurate assessment
of disparities in disease incidence (Aim 2). Estimates of population immunity will be integrated into a statistical
framework capable of estimating key risk factors for infection in the presence of mobile immune and non-
immune populations (Aim 3). Through partnership with California Department of Public Health, the work will
leverage patient data on >94,000 geolocated cases reported since 2000. The 5-year training plan includes
primary mentorship from three experienced and committed faculty mentors at UC Berkeley with expertise in
biostatistics, infectious disease epidemiology and disease dynamics. Four additional mentors—top leaders in
fungal biology, immunology, demography, and social epidemiology—will support the candidate. Leadership of
the proposed research, along with a training plan involving coursework, and mentored grant writing and project
management, will advance the candidate’s training objectives to: 1) build skills in assessing the role of
population immunity in disease transmission; 2) develop expertise in measuring and integrating social factors
into transmission models; 3) develop skills to overcome computational barriers when working with large
datasets; and 4) strengthen collaborative partnerships and build leadership skills for directing transdisciplinary
research projects. UC Berkeley’s commitment to early ca...

## Key facts

- **NIH application ID:** 10898896
- **Project number:** 5K01AI173529-02
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Jennifer Head
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $118,754
- **Award type:** 5
- **Project period:** 2023-08-03 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10898896

## Citation

> US National Institutes of Health, RePORTER application 10898896, Modelled estimation of population immunity for coccidioidomycosis and the role of immunologically naïve populations in the shifting epidemiology of coccidioidomycosis in California (5K01AI173529-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10898896. Licensed CC0.

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