Impact of the shedding level on transmission of persistent infections in Mycobacterium avium subspecies paratuberculosis (MAP)

Noa Slater, Rebecca Mans Mitchell, Robert H. Whitlock, Terry Fyock, Abani Kumar Pradhan, Elena Knupfer, Ynte Hein Schukken, Yoram Louzoun

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Super-shedders are infectious individuals that contribute a disproportionate amount of infectious pathogen load to the environment. A super-shedder host may produce up to 10 000 times more pathogens than other infectious hosts. Super-shedders have been reported for multiple human and animal diseases. If their contribution to infection dynamics was linear to the pathogen load, they would dominate infection dynamics. We here focus on quantifying the effect of super-shedders on the spread of infection in natural environments to test if such an effect actually occurs in Mycobacterium avium subspecies paratuberculosis (MAP). We study a case where the infection dynamics and the bacterial load shed by each host at every point in time are known. Using a maximum likelihood approach, we estimate the parameters of a model with multiple transmission routes, including direct contact, indirect contact and a background infection risk. We use longitudinal data from persistent infections (MAP), where infectious individuals have a wide distribution of infectious loads, ranging upward of three orders of magnitude. We show based on these parameters that the effect of super-shedders for MAP is limited and that the effect of the individual bacterial load is limited and the relationship between bacterial load and the infectiousness is highly concave. A 1000-fold increase in the bacterial contribution is equivalent to up to a 2-3 fold increase in infectiousness.

Original languageEnglish
Article number38
JournalVeterinary Research
Issue number1
StatePublished - 29 Feb 2016

Bibliographical note

Funding Information:
The authors acknowledge the support of the Within-host modeling of MAP infections Working Group at the National Institute for Mathematical and Biological Synthesis, sponsored by the National Science Foundation, the U.S. Department of Homeland Security, and the U.S. Department of Agriculture through NSF Award DBI-1300426, with additional support from The University of Tennessee, Knoxville. We acknowledge the long term funding of the Regional Dairy Quality Management Alliance through a collaborative contract with the USDA agricultural research services. The support by the Johne’s Disease Integrated Program is acknowledged. Financial Support: USDA JDIP, USDA NIFA Grant #2010-05149 (RMM), NIMBioS. We thank Miriam Beller for the text editing of the current manuscript.

Publisher Copyright:
© 2016 Slater et al.


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