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Principal Investigator: Theresa Ryckman
Organization: JOHNS HOPKINS UNIVERSITY
Fiscal Year: 2024
Award: $121,500
Funding agency: National Institute of Allergy and Infectious Diseases
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 the intersection of “big data”, disease modeling, and policy analysis who generates
evidence that can help reduce the immense and persistent global burden of TB.
Terms: <Acceleration><Approaches to prevention><Area><Award><Bayesian Modeling><Bayesian adaptive designs><Bayesian adaptive models><Bayesian belief network><Bayesian belief updating model><Bayesian framework><Bayesian hierarchical model><Bayesian network model><Bayesian nonparametric models><Bayesian spatial data model><Bayesian spatial image models><Bayesian spatial models><Bayesian statistical models><Bayesian tracking algorithms><Big Data><BigData><COVID-19><CV-19><Cell Aggregation><Cell Phone><Cellular Phone><Cellular Telephone><Censuses><Cessation of life><Characteristics><Clinic><Clinical Management><Communicable Diseases><Communities><Coronavirus Infectious Disease 2019><Country><County><Data><Data Analytics><Data Sources><Death><Detection><Development><Development Plans><Development and Research><Disease><Disorder><Effectiveness><Epidemic><Epidemiologic Research><Epidemiologic Studies><Epidemiological Studies><Epidemiological data><Epidemiology><Epidemiology Research><Epidemiology data><Faculty><Familiarity><Funding><Future><Genomics><Geographic Area><Geographic Locations><Geographic Region><Geographical Location><Goals><Health Policy><Household><Human><Imagery><Incidence><Infection><Infectious Disease Pathway><Infectious Diseases><Infectious Disorder><Institution><Interruption><Intervention><Intervention Strategies><Investigation><Investigators><Knowledge><Lead><Link><Location><M tb><M tuberculosis><M tuberculosis infection><M. tb><M. tb infection><M. tuberculosis><M. tuberculosis infection><M.tb infection><M.tuberculosis infection><MTB infection><Math Models><Mentors><Mentorship><Mobile Phones><Modeling><Modern Man><Molecular Epidemiology><Mycobacterium tuberculosis><Mycobacterium tuberculosis (MTB) infection><Mycobacterium tuberculosis infection><NGS Method><NGS system><Notification><Pathway interactions><Pattern><Pb element><Persons><Policies><Policy Analyses><Policy Analysis><Policy Making><Population><Position><Positioning Attribute><Prevalence><Preventative treatment><Prevention><Prevention approach><Preventive treatment><Public Health><R & D><R&D><Records><Reproducibility><Research><Research Personnel><Research Resources><Researchers><Resolution><Resources><Scientist><Source><South Africa><Survey Instrument><Surveys><TB infection><Techniques><Testing><Training><Transmission><Tuberculosis><Uganda><Uncertainty><access to health care><access to healthcare><accessibility of health care><accessibility to health care><accessibility to healthcare><career><career development><case finding><clinical care><community intervention><community level intervention><community spread><community transmission><community-based intervention><community-level spread><community-level transmission><coronavirus disease 2019><coronavirus disease-19><coronavirus infectious disease-19><cost><cost effective><cost effectiveness><cost estimate><cost estimation><design><designing><developmental><diagnostic assay><disease model><disorder model><disseminated TB><disseminated tuberculosis><doubt><epidemiologic><epidemiologic data><epidemiologic investigation><epidemiological><epidemiology study><evidence base><experience><fighting><genomic data><genomic data-set><genomic dataset><geographic site><health care access><health care availability><health care policy><health care service access><health care service availability><healthcare access><healthcare accessibility><healthcare availability><healthcare policy><healthcare service access><healthcare service availability><heavy metal Pb><heavy metal lead><high risk><iPhone><improved><infection due to Mycobacterium tuberculosis><infectious disease model><innovate><innovation><innovative><insight><interventional strategy><lens><lenses><mathematic model><mathematical model><mathematical modeling><medical college><medical schools><member><mortality><mtb><next gen sequencing><next generation sequencing><nextgen sequencing><novel><pathway><policy evaluation><programs><research and development><resolutions><response><school of medicine><screening><screenings><skills><smart phone><smartphone><spatial epidemiology><success><tool><transmission process><tuberculosis infection><tuberculous spondyloarthropathy>