Advancing the health and well-being of animals and people

Fellow: Rebecca Mitchell
Mentor: Ynte Schukken

Department of Population Medicine and Diagnostic Sciences
Contact Information: Email:; Phone: (607) 255-8202
Sponsor: USDA-National Institute of Food and Agriculture (NIFA)
Grant Number: 2011-67012-30725
Title: Modeling Transmission Dynamics of Mycobacterium avium subsp. paratuberculosis to Improve Control Strategies on Commercial U.S. Dairy Farms
Annual Direct Cost: $62,957
Project Period: 09/01/2011-08/31/2013

DESCRIPTION (provided by applicant): Mycobacterium avium subspecies paratuberculosis (MAP) is a chronic infection of cows and sheep which is common to most dairy farms in the United States (68% in the most recent national survey). Animals usually do not test positive for MAP until they are adults, even though we believe the majority of animals are infected at a very young age. Clinical disease causes progressive diarrhea, wasting and decrease in milk production in dairy cattle. Understanding MAP transmission cycles allows us to better design control strategies which are cost effective for farmers. In this project, a seven-year longitudinal dataset of samples from three northeastern US dairy farms will be analyzed to better understand how MAP is being transmitted between animals on the farms. We will look for associations between exposure to highly infectious adult animals and becoming infected: calves from infected dams, clusters of infected animals that were born on the same date, animals exposed to highly infectious animals as adults. We will test whether animals are commonly infected with more than one MAP strain (simulataneously, or throughout life) by testing composite samples from individual animals via dilution. This method will also be used to determine MAP diversity in positive environmental samples. Currently we assume that most farms have one strain of MAP on the farm, or that there is no competition between MAP strains. However, this project will be the first to examine long-term dynamics of MAP on farms with multiple strains present at the strain level. Based on the findings from the longitudinal dataset, we will construct mathematical models of MAP transmission that allow us to trace individual animals and assign duration of infection along specific distributions. Findings from this work will be disseminated in both scientific papers and meetings. These findings will aid veterinarians in advising farmers on best management practices to decrease clinical disease on US dairy farms.