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

www.nature.com/scientificreports OPEN Received: 22 December 2016 Accepted: 16 May 2017 Published: xx xx xxxx Optimal surveillance strategies for bovine tuberculosis in a lowprevalence country Kimberly VanderWaal1, Eva A. Enns2, Catalina Picasso1, Julio Alvarez1, Andres Perez1, Federico Fernandez3, Andres Gil4, Meggan Craft1 & Scott Wells1 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. In many countries, bovine tuberculosis (bTB) causes substantial economic losses due to costly surveillance, culling of infected animals, and imposition of movement restrictions in affected regions1, 2. The disease also represents a major public health concern, particularly in developing economies and rural regions due to transmission to farm workers and consumption of unpasteurized milk3. Prerequisite for the design and implementation of bTB surveillance systems is their ability to detect infection in cattle as early as possible to minimize spread and to mitigate costs of control and eradication4. Active bTB surveillance programs are costly and are complicated by limited sensitivity and specificity of diagnostic tests used to detect infected animals. In regions or countries with low prevalence, adopting risk-based (targeted) surveillance may improve the cost-effectiveness of bTB management compared to conventional surveillance strategies. Risk-based surveillance focuses on the subset of the population with a higher risk of infection, thus improving surveillance system sensitivity and reducing funding and labor investments5. A primary risk factor for bTB transmission is the introduction of infected cattle into herds through cattle movements6, 7. Spread of bTB via animal movements is particularly important in areas with low bTB incidence8–12. However, to optimize the implementation of surveillance and control measures, additional research is needed to clarify which herds and locations are associated with higher risk for disease introduction and transmission, and to develop methods to identify high-risk herds at an early stage of infection. 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 industry11–14. Social network analysis (SNA) has been used to characterize patterns of cattle movement, quantify the role of high-risk farms, and assess the vulnerability of livestock industries to epidemics in a variety of countries7, 15–21. For example, following the 2001 Foot-and-Mouth Disease (FMD) epidemic in the UK, between-herd cattle 1 Department of Veterinary Population Medicine, University of Minnesota, 1365 Gortner Avenue, St. Paul, MN, 55108, USA. 2Division of Health Policy and Management, School of Public Health, University of Minnesota, 420 Delaware Street SE, MMC 729, Minneapolis, MN, 55455, USA. 3Animal Health Bureau, Ministry of Livestock, Agriculture, and Fisheries, 1476 Constituyente, Montevideo, 11200, Uruguay. 4Facultad de Veterinaria, Universidad de la Republica, 1550 Alberto Lasplaces, Montevideo, 11100, Uruguay. Meggan Craft and Scott Wells contributed equally to this work. Correspondence and requests for materials should be addressed to K.V. (email: ) Scientific Reports | 7: 4140 | DOI:10.1038/s41598-017-04466-2 1 www.nature.com/scientificreports/ Figure 1. Graphical representation of the compartmental model used to represent within-herd transmission dynamics, including calves (c, top row) and adults (a, bottom row). Number of individuals in each infection class is indicated as susceptible (S), occult/exposed (O), reactive (to skin testing, R), and infectious (I). Total herd size is represented as N. β indicates the rate of transmission between infectious and susceptible individuals, and λ1 and λ2 represent the duration of the occult and reactive periods, respectively. Calves transition to adults after twelve months, which is equivalent to 1/12 on the monthly time scale of the model. movements were heavily scrutinized for their role in facilitating disease spread22–24. SNA provided a framework to assess the importance of these movements, develop mathematical models to predict the risk and severity of future outbreaks, and evaluate the efficacy of different surveillance strategies in preventing future epidemics22–24. However, 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. The prevalence of bTB within herds is often low and highly variable. This heterogeneity is likely to impact the probability of between-farm transmission. Therefore, to more accurately estimate between-farm spread of bTB, transmission models must run over long time periods and incorporate within-farm dynamics, including changes in within-farm prevalence over time. Few between-farm models exist for bTB, many of which do not account for within-farm dynamics7. However, recent integrated within- and between-herd bTB models have been developed for the UK and Italy to assess alternative surveillance strategies for those countries13, 14. Uruguay is a South American country with low bTB prevalence and a comprehensive animal traceability system. 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 agencies11. No wildlife reservoir has been identified within Uruguay, and all detected cases (...truncated)