Multi-species temporal network of livestock movements for disease spread

Introduction: The objective of this study is to show the importance of interspecies links and temporal network dynamics of a multi-species livestock movement network. Although both cattle and sheep networks have been previously studied, cattle-sheep multi-species networks have not generally been studied in-depth. The central question of this study is how the combination of cattle and sheep movements affects the potential for disease spread on the combined network. Materials and methods: Our analysis considers static and temporal representations of networks based on recorded animal movements. We computed network-based node importance measures of two single-species networks, and compared the top-ranked premises with the ones in the multi-species network. We propose the use of a measure based on contact chains calculated in a network weighted with transmission probabilities to assess the importance of premises in an outbreak. To ground our investigation in infectious disease epidemiology, we compared this suggested measure with the results of disease simulation models with asymmetric probabilities of transmission between species. Results: Our analysis of the temporal networks shows that the premises which are likely to drive the epidemic in this multi-species network differ from the ones in both the cattle and the sheep networks. Although sheep movements are highly seasonal, the estimated size of an epidemic is significantly larger in the multi-species network than in the cattle network, independently of the period of the year. Finally, we demonstrate that a measure based on contact chains allow us to identify around 30% of the key farms in a simulated epidemic, ignoring markets, whilst static network measures identify less than 10% of these farms. Conclusion: Our results ascertain the importance of combining species networks, as well as considering layers of temporal livestock movements in detail for the study of disease spread

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

Spread & control of bovine paratuberculosis in an enzootic cattle region: a multi-scale model to evaluate complex strategies combining biosecurity and test-at-purchase

National audienceBovine paratuberculosis is mainly spread between herds due to trade movements of infected and undetected animals. The prevalence worldwide being high at animal and herd levels, and infected animals being hard to detect using routine diagnostic tests, the disease spread cannot be easily observed in the field, whereas there is a need for assessing control strategies. Our objective is to better understand the spread of Mycobacterium avium subsp. paratuberculosis (Map) at a regional scale using a modelling approach, and to compare through intensive simulations complex control strategies combining biosecurity measures (early culling, hygiene improvement, calf management) and tests-at-purchase. We developed the first multi-scale mechanistic model of Map spread between dairy cattle herds, accounting for stochastic within-herd dynamics (demography and infection), indirect local transmission, and incorporating data on animal trade and on herd-specific size and management. We modeled all of the 12,857 dairy herds located in Brittany (France) having more than 15 dairy females. Data from 2005 to 2013 was used to calibrate each herd size and demographic rates, and to define trade events. We assumed initially 30% of the herds to be infected with a 10% within-herd prevalence on average. Each measure tested alone or in combination with tests at purchase succeeded in slowing down the regional Map spread, but not in decreasing the proportion of infected herds. More than two measures had to be combined to effectively reduce the herd-level prevalence. In such a case, only a moderate level of implementation of each measure was required, indicating the operational potential of such combined strategies. Our study highlights the challenge of controlling Map spread in an endemically infected region because of poor test characteristics and frequent trade movements. Our model is a flexible and efficient tool to help collective animal health managers in defining relevant control strategies at a regional scale, accounting for regional specificities in terms of contact network and farm characteristic

Mouvements commerciaux de bovins & propagation régionale de la paratuberculose

National audienc

A novel epidemiological model to better understand and predict the observed seasonal spread of Pestivirus in Pyrenean chamois populations

International audienceSeasonal variations in individual contacts give rise to a complex interplay between host demography and pathogen transmission. This is particularly true for wild populations, which highly depend on their natural habitat. These seasonal cycles induce variations in pathogen transmission. The seasonality of these biological processes should therefore be considered to better represent and predict pathogen spread. In this study, we sought to better understand how the seasonality of both the demography and social contacts of a mountain ungulate population impacts the spread of a pestivirus within, and the dynamics of, this population. We propose a mathematical model to represent this complex biological system. The pestivirus can be transmitted both horizontally through direct contact and vertically in utero. Vertical transmission leads to abortion or to the birth of persistently infected animals with a short life expectancy. Horizontal transmission involves a complex dynamics because of seasonal variations in contact among sexes and age classes. We performed a sensitivity analysis that identified transmission rates and disease-related mortality as key parameters. We then used data from a long-term demographic and epidemiological survey of the studied population to estimate these mostly unknown epidemiological parameters. Our model adequately represents the system dynamics, observations and model predictions showing similar seasonal patterns. We show that the virus has a significant impact on population dynamics, and that persistently infected animals play a major role in the epidemic dynamics. Modeling the seasonal dynamics allowed us to obtain realistic prediction and to identify key parameters of transmission

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

Which phenotypic traits of resistance should be improved in cattle to control paratuberculosis dynamics in a dairy herd: a modelling approach

Abstract Paratuberculosis is a worldwide disease causing production losses in dairy cattle herds. Variability of cattle response to exposure to Mycobacterium avium subsp. paratuberculosis (Map) has been highlighted. Such individual variability could influence Map spread at larger scale. Cattle resistance to paratuberculosis has been shown to be heritable, suggesting genetic selection could enhance disease control. Our objective was to identify which phenotypic traits characterising the individual course of infection influence Map spread in a dairy cattle herd. We used a stochastic mechanistic model. Resistance consisted in the ability to prevent infection and the ability to cope with infection. We assessed the effect of varying (alone and combined) fourteen phenotypic traits characterising the infection course. We calculated four model outputs 25 years after Map introduction in a naïve herd: cumulative incidence, infection persistence, and prevalence of infected and affected animals. A cluster analysis identified influential phenotypes of cattle resistance. An ANOVA quantified the contribution of traits to model output variance. Four phenotypic traits strongly influenced Map spread: the decay in susceptibility with age (the most effective), the quantity of Map shed in faeces by high shedders, the incubation period duration, and the required infectious dose. Interactions contributed up to 12% of output variance, highlighting the expected added-value of improving several traits simultaneously. Combinations of the four most influential traits decreased incidence to less than one newly infected animal per year in most scenarios. Future genetic selection should aim at improving simultaneously the most influential traits to reduce Map spread in cattle populations