Modelling the effect of test‐and‐slaughter strategies to control bovine tuberculosis in endemic high prevalence herds

Picasso-Risso, Catalina; Álvarez, Julio; VanderWaal, Kimberly; Kinsley, Amy C.; Gil, Andrés Delgado; Wells, Scott J.; Pérez, Andrés

doi:10.1111/tbed.13774

Cited by 7 publications

(10 citation statements)

References 35 publications

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“…Reactive animals ultimately move into the infectious state (

) at a rate of

, where

is the average duration of the reactive state,

. The final compartment represents animals that become infectious (

) while remaining detectable by antemortem diagnostic tests [ 22 , 33 ].…”

Section: Methodsmentioning

confidence: 99%

“…Since we do not have field data for RS or Brazil to calculate the parameters, we used parameters reported in models and studies of TB at the farm level. For the within-premises transmission coefficient rate,

(per cow and year), we alternatively used the following values: 2.23 [ 35 ], 2.76 [ 12 , 22 , 33 , 36 , 37 , 38 , 39 ], and 5.2 [ 12 , 22 , 37 , 38 ]. For the other transmission rates, we assumed

= 41 days [ 33 , 40 , 41 ] and

= 6 months [ 12 , 33 , 36 , 38 , 41 ].…”

Section: Methodsmentioning

confidence: 99%

“…The following values were considered for the parameters governing disease transmission based on available literature:

(1 to 5.2) [ 12 , 22 , 33 , 35 ],

(1/14.1 to 1/45) [ 12 , 22 , 33 , 39 , 42 ], and

(1/180 to 1/630) [ 12 , 22 , 33 , 38 , 39 , 42 ]. For the local sensitivity analysis, different herd sizes (10 to 1000 animals) were also considered.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, to accurately estimate between-premises spread of TB, transmission models must run over long time periods and incorporate within-premises dynamics, including changes in within-premises prevalence over time to provide comprehensive quantitative representations of disease transmission pathways [ 12 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

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Use of Network Analysis and Spread Models to Target Control Actions for Bovine Tuberculosis in a State from Brazil

Cárdenas

Pozo

Lopes³

et al. 2021

Microorganisms

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Livestock movements create complex dynamic interactions among premises that can be represented, interpreted, and used for epidemiological purposes. These movements are a very important part of the production chain but may also contribute to the spread of infectious diseases through the transfer of infected animals over large distances. Social network analysis (SNA) can be used to characterize cattle trade patterns and to identify highly connected premises that may act as hubs in the movement network, which could be subjected to targeted control measures in order to reduce the transmission of communicable diseases such as bovine tuberculosis (TB). Here, we analyzed data on cattle movement and slaughterhouse surveillance for detection of TB-like lesions (TLL) over the 2016–2018 period in the state of Rio Grande do Sul (RS) in Brazil with the following aims: (i) to characterize cattle trade describing the static full, yearly, and monthly snapshots of the network contact trade, (ii) to identify clusters in the space and contact networks of premises from which animals with TLL originated, and (iii) to evaluate the potential of targeted control actions to decrease TB spread in the cattle population of RS using a stochastic metapopulation disease transmission model that simulated within-farm and between-farm disease spread. We found heterogeneous densities of premises and animals in the study area. The analysis of the contact network revealed a highly connected (~94%) trade network, with strong temporal trends, especially for May and November. The TLL cases were significantly clustered in space and in the contact network, suggesting the potential for both local (e.g., fence-to-fence) and movement-mediated TB transmission. According to the disease spread model, removing the top 7% connected farms based on degree and betweenness could reduce the total number of infected farms over three years by >50%. In conclusion, the characterization of the cattle network suggests that highly connected farms may play a role in TB dissemination, although being close to infected farms was also identified as a risk factor for having animals with TLL. Surveillance and control actions based on degree and betweenness could be useful to break the transmission cycle between premises in RS.

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“…Reactive animals ultimately move into the infectious state (

) at a rate of

, where

is the average duration of the reactive state,

. The final compartment represents animals that become infectious (

) while remaining detectable by antemortem diagnostic tests [ 22 , 33 ].…”

Section: Methodsmentioning

confidence: 99%

(per cow and year), we alternatively used the following values: 2.23 [ 35 ], 2.76 [ 12 , 22 , 33 , 36 , 37 , 38 , 39 ], and 5.2 [ 12 , 22 , 37 , 38 ]. For the other transmission rates, we assumed

= 41 days [ 33 , 40 , 41 ] and

= 6 months [ 12 , 33 , 36 , 38 , 41 ].…”

Section: Methodsmentioning

confidence: 99%

“…The following values were considered for the parameters governing disease transmission based on available literature:

(1 to 5.2) [ 12 , 22 , 33 , 35 ],

(1/14.1 to 1/45) [ 12 , 22 , 33 , 39 , 42 ], and

(1/180 to 1/630) [ 12 , 22 , 33 , 38 , 39 , 42 ]. For the local sensitivity analysis, different herd sizes (10 to 1000 animals) were also considered.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Use of Network Analysis and Spread Models to Target Control Actions for Bovine Tuberculosis in a State from Brazil

Cárdenas

Pozo

Lopes³

et al. 2021

Microorganisms

Self Cite

View full text Add to dashboard Cite

show abstract

“…Animal tuberculosis (TB) is a widespread multi‐host disease caused by infection with Mycobacterium bovis and the closely related members of the M. tuberculosis complex (MTC) and leads to increased host mortality (Barasona et al., 2016). TB has a significant economic impact on the livestock industry due to test and slaughter schemes and movement restrictions (Gortázar et al., 2015; Picasso‐Risso et al., 2020; Schiller et al., 2011) and within the European Union, there has been funding and a long‐term policy to reduce and eradicate TB (Kubik et al., 2016). Other intervention measures include reducing indirect contacts among host species (Barasona et al., 2013; Wilber et al., 2019), vaccinating (Díez‐Delgado et al., 2018) and culling (Boadella et al., 2012; Tanner, White, Acevedo, et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

The impact of an African swine fever outbreak on endemic tuberculosis in wild boar populations: A model analysis

O’Neill

White

Ruiz‐Fons

et al. 2021

Transbound. Emerg. Dis.

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A mathematical model is developed and analysed to examine the impacts of African swine fever (ASF) introduction into a wild boar population that supports endemic animal tuberculosis (TB). TB is a widespread infectious disease caused by the Mycobacterium tuberculosis bacteria belonging to the Mycobacterium tuberculosis complex (MTC) that can persist in reservoir wildlife hosts. Wild boar (sus scrofa) are a key reservoir for MTC, and an increasing trend in wild boar density is expected to lead to an increase in TB prevalence with spill‐over to livestock. MTC infection is presently controlled through a variety of strategies, including culling. African swine fever (ASF) is a virulent, viral infection which affects wild boar and is spreading across Eurasia and Oceania. ASF infection leads to near 100% mortality at the individual level, can cause a dramatic decrease in population density and may therefore lead to TB control. We extend an established model that captures the key demographic and infection processes for TB in wild boar to consider the impact of ASF introduction on wild boar populations that support different levels of endemic TB. Our model results indicate that an ASF infection will reduce wild boar population density and lead to a decrease in the prevalence of TB. If ASF persists in the local host population the model predicts the long‐term decline of TB prevalence in wild boar. If ASF is eradicated, or fades‐out in the local host population, the model predicts a slower recovery of TB prevalence in comparison to wild boar density after an ASF epidemic. This may open a window of opportunity to apply TB management to maintain low TB prevalence.

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Systematic review and meta-analysis of tuberculosis in animals in Nigeria

Ahmad

Raji

Hassan

et al. 2023

Heliyon

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Modelling the effect of test‐and‐slaughter strategies to control bovine tuberculosis in endemic high prevalence herds

Cited by 7 publications

References 35 publications

Use of Network Analysis and Spread Models to Target Control Actions for Bovine Tuberculosis in a State from Brazil

Use of Network Analysis and Spread Models to Target Control Actions for Bovine Tuberculosis in a State from Brazil

The impact of an African swine fever outbreak on endemic tuberculosis in wild boar populations: A model analysis

Systematic review and meta-analysis of tuberculosis in animals in Nigeria

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