# Integrating genomic and spatial insights on Mtb transmission into policy-relevant models

> **NIH NIH K01** · JOHNS HOPKINS UNIVERSITY · 2024 · $121,500

## Abstract

PROJECT SUMMARY/ABSTRACT
Tuberculosis (TB) remains a leading cause of global mortality. Treatment is efficacious and low cost for most
people, but clinic-based (“passive”) case detection leaves millions of people with TB undiagnosed every year.
To accelerate progress against TB, we must bring detection and prevention to communities – but community-
based interventions, such as systematic screening and TB preventive treatment (TPT), are resource intensive
at scale. Evidence on how to target community-based TB interventions for greatest impact and efficiency is
therefore urgently needed. Novel data sources – including aggregated cell phone records, genomic
sequencing, and empiric estimates of cost – are emerging as critical tools for understanding and fighting TB in
high-burden settings. Scientists who can analyze these data using rigorous and reproducible techniques while
applying a health policy lens will be essential to our future success against the world’s leading infectious killer.
The goal of this project is to identify ways to make community-level TB screening and prevention more
impactful and cost-effective by better characterizing the spatial dynamics of TB and Mycobacterium
tuberculosis (Mtb) transmission. My training aims include: (1) develop expertise in spatial epidemiology and the
analysis of mobility data; (2) obtain training in molecular epidemiology; and (3) gain skills to link these data
sources to mathematical models. These aims link to three research aims: (1) refine estimates of subnational
TB prevalence by synthesizing emerging sources of data; (2) use genomic data to identify key populations
among whom Mtb transmission is concentrated; and (3) project the impact and cost-effectiveness of targeted
TB screening and prevention approaches. This research will leverage both aggregated cell phone mobility data
and genomic, spatial, mobility, and epidemiologic data from three studies in Uganda and South Africa with
unique designs that lend themselves well toward describing the spatial scale of Mtb transmission. To achieve
success as an independent investigator, I also plan to obtain experience with field-based epidemiological
studies and gain familiarity with the clinical management of TB in high-burden settings.
I am a junior faculty member in the Division of Infectious Diseases at the Johns Hopkins School of Medicine
with a quantitative background in infectious disease modeling and health policy. My long-term career goal is to
become an independent researcher who informs more evidence-based TB policymaking by integrating diverse
sources of data into mathematical models. During this award period, I will be mentored by a team whose
expertise spans TB epidemiology and clinical care, molecular epidemiology, infectious disease dynamics,
human mobility, and mechanistic and statistical disease modeling.
Collectively, this research and career development plan will provide a pathway to a career as an independent
investigator situated at th...

## Key facts

- **NIH application ID:** 10866050
- **Project number:** 1K01AI182503-01
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Theresa Ryckman
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $121,500
- **Award type:** 1
- **Project period:** 2024-09-01 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10866050

## Citation

> US National Institutes of Health, RePORTER application 10866050, Integrating genomic and spatial insights on Mtb transmission into policy-relevant models (1K01AI182503-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10866050. Licensed CC0.

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