Abstract

To monitor a state of disease freedom and to ensure a timely detection of new introductions of disease, surveillance programmes need be evaluated prior to implementation. We present a strategy to evaluate surveillance of Mycobacterium avium subsp. paratuberculosis (MAP) using simulated testing of bulk milk in an infectious disease spread model. MAP is a globally distributed, chronic infectious disease with substantial animal health impact. Designing surveillance for this disease poses specific challenges because methods for surveillance evaluation have focused on estimating surveillance system sensitivity and probability of freedom from disease and do not account for spread of disease or complex and changing population structure over long periods. The aims of the study were to 1. define a model that describes the spread of MAP within and between Swedish herds; 2. define a method for simulation of imperfect diagnostic testing in this framework; 3. to compare surveillance strategies to support surveillance design choices. The results illustrate how this approach can be used to identify differences between the probability of detecting disease in the population based on choices of the number of herds sampled and the use of risk-based or random selection of these herds. The approach was also used to assess surveillance to detect introduction of disease and to detect a very low prevalence endemic state. The use of bulk milk sampling was determined to be an effective method to detect MAP in the population with as few as 500 herds tested per year if the herd-level prevalence was 0.2 %. However, detection of point introductions in the population was unlikely in the 13-year simulation period even if as many as 2000 herds were tested per year. Interestingly, the use of a risk-based selection strategy was found to be a disadvantage to detect MAP given the modelled disease dynamics.

Keywords

Surveillance evaluation; Surveillance evaluation; Infectious disease modelling; John's disease; Bulk milk

Published in

Preventive Veterinary Medicine
2020, Volume: 183, article number: 105152
Publisher: ELSEVIER

SLU Authors

• Frössling, Jenny

  • Department of Applied Animal Science and Welfare, Swedish University of Agricultural Sciences
    
  • National Veterinary Institute (SVA)

UKÄ Subject classification

Clinical Science
Animal and Dairy Science


Publication identifier

DOI: https://doi.org/10.1016/j.prevetmed.2020.105152

Permanent link to this page (URI)

https://res.slu.se/id/publ/109008