Advancing the health and well-being of animals and people


Fellow: Rebecca Smith

Mentor: Yrjo Grohn
Contact Information: Email: rls57@cornell.edu; Phone: 607-253-4136
Sponsor: National Institute of Health
Grant Number: 1K01RR032078-01
Title: Mathematical and Statistical Modeling of Mycobacterial Disease Control
Annual Direct Cost: $68,799
Project Period: 04/01/11-03/31/16

DESCRIPTION (provided by applicant):

CANDIDATE: Dr. Rebecca Smith DVM MS, a veterinary epidemiologist, is currently enrolled in a Ph.D. program in the field of Clinical Biomedical Sciences at Cornell University College of Veterinary Medicine. Her work has been recognized to be unique and ground-breaking in linking disease modeling, statistics, and veterinary medicine.

ENVIRONMENT: Dr. Smith’s mentor, Dr. Yrjo Gröhn, is a highly respected veterinary epidemiologist, chair of Cornell University’s Department of Population Medicine and Diagnostic Sciences. Dr. Gröhn has expertise in disease modeling and statistical analysis of disease and has mentored many graduate students, postdoctoral fellows, and visiting scholars in these fields. Dr. Smith will also be working with and mentored by a multidisciplinary executive committee, including Dr. Robert Strawderman (Professor of Biological Statistics and Computational Biology), Dr. Loren Tauer (Chair of the Department of Applied Economics and Management), and Dr. David Russell (Chair of the Department of Microbiology and Immunology).

TRAINING PLAN: The K01 award will support Dr. Smith’s post-doctoral training and subsequent work as a Research Assistant. During this time, she will develop new mathematical and statistical models for mycobacterial diseases and transition into independence as a researcher.

PROPOSAL: Dr. Smith will apply techniques developed for mathematical and statistical models of Mycobacterium avium subsp. paratuberculosis to three other mycobacterial diseases. A model for M. bovis spread within herds will be designed and analyzed to inform US eradication protocol. An existing model for M. tuberculosis will be used to economically and socially optimize control strategies, and will be used as the basis for a reverse jump Markov Chain Monte Carlo (rjMCMC) model that will be able to estimate vaccine and control program efficacy from field trials. A model will be designed and analyzed for M. leprae infection and parameterized using rjMCMC techniques; the finished model will be used to optimize control strategies. These models will reveal new areas of possible research at the intersection between human and animal health.