Optimal surveillance strategies for bovine tuberculosis in a low-prevalence country

Kimberly Vanderwaal,Federico Fernandez,Andres Perez,Eva A Enns,Catalina Picasso,Scott Wells,Meggan Craft,Julio Alvarez,Andres Gil

doi:10.1038/s41598-017-04466-2

Kimberly Vanderwaal, Federico Fernandez + Show 7 more

Open Access

https://doi.org/10.1038/s41598-017-04466-2

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Abstract

Bovine tuberculosis (bTB) is a chronic disease of cattle that is difficult to control and eradicate in part due to the costly nature of surveillance and poor sensitivity of diagnostic tests. Like many countries, bTB prevalence in Uruguay has gradually declined to low levels due to intensive surveillance and control efforts over the past decades. In low prevalence settings, broad-based surveillance strategies based on routine testing may not be the most cost-effective way for controlling between-farm bTB transmission, while targeted surveillance aimed at high-risk farms may be more efficient for this purpose. To investigate the efficacy of targeted surveillance, we developed an integrated within- and between-farm bTB transmission model utilizing data from Uruguay’s comprehensive animal movement database. A genetic algorithm was used to fit uncertain parameter values, such as the animal-level sensitivity of skin testing and slaughter inspection, to observed bTB epidemiological data. Of ten alternative surveillance strategies evaluated, a strategy based on eliminating testing in low-risk farms resulted in a 40% reduction in sampling effort without increasing bTB incidence. These results can inform the design of more cost-effective surveillance programs to detect and control bTB in Uruguay and other countries with low bTB prevalence.

Highlights

In many countries, bovine tuberculosis causes substantial economic losses due to costly surveillance, culling of infected animals, and imposition of movement restrictions in affected regions[1, 2]
Between-farm transmission occurred via animal movements, which were represented with the observed dynamic movement network over 5.5 years, and via a spatial transmission kernel to capture localized transmission
To explore the potential for targeted surveillance within this low prevalence country, we designed a network-based transmission model that integrated within- and between-farm transmission processes and optimized parameter values with real-world data to emulate the epidemiological dynamics specific to Uruguay

Summary

Introduction

Bovine tuberculosis (bTB) causes substantial economic losses due to costly surveillance, culling of infected animals, and imposition of movement restrictions in affected regions[1, 2]. Animal traceability systems, which have been implemented in many countries, provide an ideal opportunity to empirically assess movement-related bTB risk and to simulate the potential between-farm spread of bTB through the cattle industry[11,12,13,14]. Network-based modeling approaches are challenging for bTB in part due to the chronic nature of the disease, characterized by long latent periods, low within-farm transmission rates, and limitations of diagnostic tests. Despite considerable investment in a test-and-cull program for the control of bTB, the incidence of the disease has increased since 2008 (~4 farms per year in the early 2000s to ~22 per year in 2012–2014), raising concern among stakeholders and animal health agencies[11]. An empirical assessment of risk factors for bTB, combined with computational network-based models to simulate the relative effectiveness of risk-based control measures, is critical for the development of risk-based surveillance in Uruguay

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