The effect of risk-based trading and within-herd measures on Mycobacterium avium subspecies paratuberculosis spread within and between Irish dairy herds

Johne’s disease (bovine paratuberculosis) is an endemic disease caused by Mycobacterium avium subspecies paratuberculosis (Map). Map is transmitted between herds primarily through movement of infected but undetected animals. Within infected herds, possible control strategies include improving herd hygiene by reducing calf exposure to faeces from cows, reducing stress in cows resulting in a longer latently infected period where shedding is minimal, or culling highly test-positive cows soon after detection. Risk-based trading can be a strategy to reduce the risk that Map spreads between herds. Our objective was to assess whether within-herd measures combined with risk-based trading could effectively control Map spread within and between dairy cattle herds in Ireland. We used a stochastic individual-based and between-herd mechanistic epidemiological model to simulate Map transmission. Movement and herd demographic data were available from 1st January 2009–31st December 2018. In total, 13,353 herds, with 4,494,768 dairy female animals, and 72,991 bulls were included in our dataset. The movement dataset consisted of 2,304,149 animal movements. For each herd, a weekly indicator was calculated that reflected the probability that the herd was free from infection. The indicator value increased when a herd tested negative, decreased when animals were introduced into a herd, and became 0 when a herd tested positive. Based on this indicator value, four Johne’s assurance statuses were distinguished: A) ≥ 0.7 – 1.0, B) ≥ 0.3 – 0.0 – < 0.3, and D) 0.0. A is the highest and D the lowest Johne’s assurance status. With risk-based trading some of the observed movements between herds were redirected based on Johne’s assurance status with the aim of reducing the risk that a non-infected herd acquired an infected animal. Risk-based trading effectively reduced the increase in herd prevalence over a 10-year-period in Ireland: from 50% without risk-based trading to 42% with risk-based trading in the metapopulation only, and 26% when external purchases were risk-based as well. However, for risk-based trading to be effective, a high percentage of dairy herds had to participate. The most important within-herd measures were improved herd hygiene and early culling of highly infectious cows. These measures reduced both herd and within-herd prevalence compared to the reference scenario. Combining risk-based trading with within-herd measures reduced within-herd prevalence even more effectively.Department of Agriculture, Food and the Marin

To Vaccinate or Not: Impact of Bovine Viral Diarrhoea in French Cow-Calf Herds

Bovine viral diarrhoea (BVD) remains an issue despite control programs implemented worldwide. Virus introduction can occur through contacts with neighbouring herds. Vaccination can locally protect exposed herds. However, virus spread depends on herd characteristics, which may impair vaccination efficiency. Using a within-herd epidemiological model, we compared three French cow-calf farming systems named by their main breed: Charolaise, Limousine, and Blonde d’Aquitaine. We assessed vaccination strategies of breeding females assuming two possible protections: against infection or against vertical transmission. Four commercial vaccines were considered: Bovilis®, Bovela®, Rispoval®, and Mucosiffa®. We tested various virus introduction frequency in a naïve herd. We calculated BVD economic impact and vaccination reward. In Charolaise, BVD economic impact was 113€ per cow over 5 years after virus introduction. Irrespective of the vaccine and for a high enough risk of introduction, the yearly expected reward was 0.80€ per invested euro per cow. Vaccination should not be stopped before herd exposure has been decreased. In contrast, the reward was almost nil in Blonde d’Aquitaine and Limousine. This highlights the importance of accounting for herd specificities to assess BVD impact and vaccination efficiency. To guide farmers’ vaccination decisions against BVD, we transformed this model into a French decision support tool

The dynamic interplay between pathogen spread at farm scale and farmer’s health management decisions

International audienc

A generic framework to model infection dynamics in a metapopulation of cattle herds

International audienceDeveloping epidemiological models at a regional scale requires coupling within-farm epidemiological models, leading to complex structure. Such systems also require large computational resources. Our objective is to propose a generic framework to represent pathogenic spread in a metapopulation that could be used for any endemic host/pathogen system, as long as a herd epidemiological model is available. Two implementations have been tested and applied to a pathogen: Mycobacterium avium subsp. paratuberculosis. One of them is optimized for distributed computing

A generic framework to model pathogen spreading in a metapopulation

International audienceEndemic infectious livestock diseases impact animal health and welfare, and food safety. Pathogens spread between farms mainly due to animal movements (purchases/sales) and neighboring relationships. The risk of spreading depends on the within-farm proportions of infected animals, which varies within and between farms over time. A modelling approach is relevant to represent such a complex biological system, permitting the ex-ante evaluation of control strategies under various scenarios. Developing epidemiological models at a regional scale requires to couple within-farm epidemiological models, leading to complex models and to the need for large computational resources, especially when stochastic processes are involved. The objective is to find the best generic framework in terms of computational performance to represent pathogen spread in a cattle metapopulation. Three requirements should be fulfil: (1) a common interface should be used to run population dynamics, and within- and between-herd infection dynamics; (2) a common data structure should be used for animal movements; (3) the shared interface and structure should be easy to understand, to be usable by persons with various skill levels in modelling. Two implementations are available in this framework: synchronized or desynchronized methods. In the first case, herd dynamics evolve simultaneously. At each time step, dynamics are simulated for all the herds. The second case was conceived to be used with distributed computing. Herd dynamics evolve independently from each other as long as no purchase occurs. As a purchase corresponds to a sell, the destination herd has to wait until an animal is available and its infection status known. For the latter case, neighboring contacts cannot be modelled as herd dynamics are not synchronized. These implementations have been tested on a single processor, then will be tested on a computing grid. We have investigated the computing load according to herd size, herd number, number of years, and distribution of animal movements. When on average one movement occurred per year and per herd, the desynchronized method was slower than the synchronized one. On the contrary, if only a few herds (1/5) exchange animals, for the same total number of movements, the desynchronized method was faster. We successfully applied the synchronized framework to Mycobacterium avium subsp. paratuberculosis, which is spread by animal movements. The next step is to evaluate it on a grid, before using it for other pathogens with other spread characteristic

A generic framework to model pathogen spreading in a metapopulation

International audienceEndemic infectious livestock diseases impact animal health and welfare, and food safety. Pathogens spread between farms mainly due to animal movements (purchases/sales) and neighboring relationships. The risk of spreading depends on the within-farm proportions of infected animals, which varies within and between farms over time. A modelling approach is relevant to represent such a complex biological system, permitting the ex-ante evaluation of control strategies under various scenarios. Developing epidemiological models at a regional scale requires to couple within-farm epidemiological models, leading to complex models and to the need for large computational resources, especially when stochastic processes are involved. The objective is to find the best generic framework in terms of computational performance to represent pathogen spread in a cattle metapopulation. Three requirements should be fulfil: (1) a common interface should be used to run population dynamics, and within- and between-herd infection dynamics; (2) a common data structure should be used for animal movements; (3) the shared interface and structure should be easy to understand, to be usable by persons with various skill levels in modelling. Two implementations are available in this framework: synchronized or desynchronized methods. In the first case, herd dynamics evolve simultaneously. At each time step, dynamics are simulated for all the herds. The second case was conceived to be used with distributed computing. Herd dynamics evolve independently from each other as long as no purchase occurs. As a purchase corresponds to a sell, the destination herd has to wait until an animal is available and its infection status known. For the latter case, neighboring contacts cannot be modelled as herd dynamics are not synchronized. These implementations have been tested on a single processor, then will be tested on a computing grid. We have investigated the computing load according to herd size, herd number, number of years, and distribution of animal movements. When on average one movement occurred per year and per herd, the desynchronized method was slower than the synchronized one. On the contrary, if only a few herds (1/5) exchange animals, for the same total number of movements, the desynchronized method was faster. We successfully applied the synchronized framework to Mycobacterium avium subsp. paratuberculosis, which is spread by animal movements. The next step is to evaluate it on a grid, before using it for other pathogens with other spread characteristic

Spread and control of enzootic cattle diseases: a data-driven multiscale modelling framework to prioritize complex regional strategies

International audienceControlling enzootic livestock diseases is a major challenge for sustainable farming systems and veterinary public health. Because the diversity of farming systems and between-herd contacts influence pathogen spread, concerted large-scale interventions are needed. Particular attention should be paid to animal trade movements forming complex networks linking farms, which represent a major transmission pathway. Our objective is to provide a modelling framework for prioritizing regional control strategies of cattle enzootics. We developed data-driven multiscale stochastic epidemiological models describing detailed within-herd demography and infection dynamics coupled through between-herd contacts. This framework was applied to paratuberculosis (PTb; spread through trade), and bovine viral diarrhoea (BVD; spread through trade and neighbouring contacts). We modelled 12,857 dairy herds located in Brittany (France), using comprehensive datasets (2005-2013) on herd size, location, demography, and trade. Simulated control strategies implemented at different intensity levels combined biosecurity (test-&-cull, hygiene) and tests at purchase in all or targeted herds. According to our findings, only high intensity of measure implementation enabled to limit the spread of PTb at within and between-herd scales. For BVD, systematic tests at purchase largely reduced prevalence, but within-herd control was needed to reach eradication. Our study highlights the key challenge of controlling cattle enzootics, as a balance between efficacy and effort. In the front of multiple criteria to optimize, we provided a flexible and efficient tool to help animal health managers in defining relevant regional control strategies, accounting for specificities of the contact network and farm characteristic

A mechanistic model of tsetse fly population dynamics in space and time calibrated on observed data in Senegal

A mechanistic model of tsetse fly population dynamics in space and time calibrated on observed data in Senegal. 8. Workshop Dynamical Systems Applied to Biology and Natural Sciences (DSABNS

Modelling transmission of Mycobacterium avium subspecies paratuberculosis between Irish dairy cattle herds

Bovine paratuberculosis is an endemic disease caused by Mycobacterium avium subspecies paratuberculosis (Map). Map is mainly transmitted between herds through movement of infected but undetected animals. Our objective was to investigate the effect of observed herd characteristics on Map spread on a national scale in Ireland. Herd characteristics included herd size, number of breeding bulls introduced, number of animals purchased and sold, and number of herds the focal herd purchases from and sells to. We used these characteristics to classify herds in accordance with their probability of becoming infected and of spreading infection to other herds. A stochastic individual-based model was used to represent herd demography and Map infection dynamics of each dairy cattle herd in Ireland. Data on herd size and composition, as well as birth, death, and culling events were used to characterize herd demography. Herds were connected with each other through observed animal trade movements. Data consisted of 13 353 herds, with 4 494 768 dairy female animals, and 72 991 breeding bulls. We showed that the probability of an infected animal being introduced into the herd increases both with an increasing number of animals that enter a herd via trade and number of herds from which animals are sourced. Herds that both buy and sell a lot of animals pose the highest infection risk to other herds and could therefore play an important role in Map spread between herds.Department of Agriculture, Food and the Marin

An integrated epidemio-economic modelling framework of the complex interplay between pathogen spread and disease management: control of BVD within beef herds as a case study

The dynamics of animal diseases is usually modelled thanks to an epidemiological modelling framework where on-farm health practices are omitted. Optimal control decisions are mainly drawn by modelling the effect of mandatory practices, which is of public interest for the control and eradication of major epidemic diseases. However, most endemic infectious animal diseases do not rely on public health control, although generating significant adverse economic consequences in livestock. Pathogen spread and the magnitude of associated economic consequences are directly linked to efforts undertaken by farmers themselves.Our objective was to propose a mathematical modelling framework integrating individual health decisions into dynamic stochastic epidemiological models, in order to highlight the feedback loops occurring between the modification over time of the epidemiological situation and farmers’ decision process. These feedback loops exist for a single herd, but also more widely for a group of herds interacting at a regional level. This work is applied to the control of Bovine Viral Diarrhea at the scale of a beef cattle herd, using an existing epidemiological model [1] and considering vaccination as control measure. We represented decision at each time step (here of one year) as a balance between expected farm income when performing vs. not performing vaccination. Since the epidemiological model is stochastic, a methodology was needed to compare model outcomes in both cases. We simulated a sufficient number of repetitions for each scenario to compare predictions using Wilcoxon-Mann-Whitney statistical test, which allows comparing mean values without normality assumptions on the tested variable. To account for the limited information available to farmer about the subsequent actual realization, one of the simulated repetitions was assumed to truly occur. Therefore, predicted farm health decisions are not necessarily optimal. The vaccination choice performed on time t influences epidemiological dynamics between t and t+1, and thus the next decision to be made on time t+1.The proposed approach makes it possible to characterize the dynamics in terms of vaccination decisions (existence of vaccination cycle), as well as disease dynamics at the farm level. Simulation results reveal that BVD epidemiological dynamics at farm level highly depends on farmers’ health choices over time, as well as on the randomness of biological processes