The rapid spread of African swine fever (ASF) in recent years has once again raised awareness of the need to improve our preparedness in preventing and managing outbreaks, for which modelling-based forecasts can play an important role. This is even more important in the case of a disease such as ASF, involving several types of hosts, characterised by a high case-fatality rate and for which there is currently no treatment or vaccine. Within the framework of the ASF challenge, we proposed a modelling approach based on a stochastic mechanistic model and an inference procedure to estimate key transmission parameters from provided data (incomplete and noisy) and generate forecasts for unobserved time horizons. The model is partly data driven and composed of two modules, corresponding to epidemic and demographic dynamics in domestic pig and wild boar (WB) populations, interconnected through the networks of animal trade and/or spatial proximity. The inference consists in an iterative procedure, alternating between the two models and based on a criterion optimisation. Estimates of transmission and detection parameters appeared to be of similar magnitude for each of the three periods of the challenge, except for the transmission rates in WB population through contact with infectious individuals and carcasses, higher during the first period. The predicted number of infected domestic pig farms was in overall agreement with the data. The proportion of positive tested WB was overestimated, but with a trend close to that observed in the data. Comparison of the spatial simulated and observed distributions of detected cases also showed an overestimation of the spread of the pathogen within WB metapopulation. Beyond the quantitative results and the inherent difficulties of real-time forecasting, we built a modelling framework that is flexible enough to accommodate changes in transmission processes and control measures that may occur during an epidemic emergency.

Robust epidemiological knowledge and predictive modelling tools are needed to address challenging objectives, such as: understanding epidemic drivers; forecasting epidemics; and prioritising control measures. Often, multiple modelling approaches can be used during an epidemic to support effective decision making in a timely manner. Modelling challenges contribute to understanding the pros and cons of different approaches and to fostering technical dialogue between modellers. In this paper, we present the results of the first modelling challenge in animal health – the ASF Challenge – which focused on a synthetic epidemic of African swine fever (ASF) on an island. The modelling approaches proposed by five independent international teams were compared. We assessed their ability to predict temporal and spatial epidemic expansion at the interface between domestic pigs and wild boar, and to prioritise a limited number of alternative interventions. We also compared their qualitative and quantitative spatio-temporal predictions over the first two one-month projection phases of the challenge. Top-performing models in predicting the ASF epidemic differed according to the challenge phase, host species, and in predicting spatial or temporal dynamics. Ensemble models built using all team-predictions outperformed any individual model in at least one phase. The ASF Challenge demonstrated that accounting for the interface between livestock and wildlife is key to increasing our effectiveness in controlling emerging animal diseases, and contributed to improving the readiness of the scientific community to face future ASF epidemics. Finally, we discuss the lessons learnt from model comparison to guide decision making.

Leveraging artificial intelligence (AI) approaches in animal health (AH) makes it possible to address highly complex issues such as those encountered in quantitative and predictive epidemiology, animal/human precision-based medicine, or to study host x pathogen interactions. AI may contribute (i) to diagnosis and disease case detection, (ii) to more reliable predictions and reduced errors, (iii) to representing more realistically complex biological systems and rendering computing codes more readable to non-computer scientists, (iv) to speeding-up decisions and improving accuracy in risk analyses, and (v) to better targeted interventions and anticipated negative effects. In turn, challenges in AH may stimulate AI research due to specificity of AH systems, data, constraints, and analytical objectives. Based on a literature review of scientific papers at the interface between AI and AH covering the period 2009–2019, and interviews with French researchers positioned at this interface, the present study explains the main AH areas where various AI approaches are currently mobilised, how it may contribute to renew AH research issues and remove methodological or conceptual barriers. After presenting the possible obstacles and levers, we propose several recommendations to better grasp the challenge represented by the AH/AI interface. With the development of several recent concepts promoting a global and multisectoral perspective in the field of health, AI should contribute to defract the different disciplines in AH towards more transversal and integrative research.

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. 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. 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. 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.

Restrictions on mobility are a key component of infectious disease controls, preventing the spread of infections to as yet unexposed areas, or to regions which have previously eliminated outbreaks. However, even under the most severe restrictions, some travel must inevitably continue, at the very minimum to retain essential services. For COVID-19, most countries imposed severe restrictions on travel at least as soon as it was clear that containment of local outbreaks would not be possible. Such restrictions are known to have had a substantial impact on the economy and other aspects of human health, and so quantifying the impact of such restrictions is an essential part of evaluating the necessity for future implementation of similar measures. In this analysis, we built a simulation model using National statistical data to record patterns of movements to work, and implement levels of mobility recorded in real time via mobile phone apps. This model was fitted to the pattern of deaths due to COVID-19 using approximate Bayesian inference. Our model is able to recapitulate mortality considering the number of deaths and datazones (DZs, which are areas containing approximately 500-1000 residents) with deaths, as measured across 32 individual council areas (CAs) in Scotland. Our model recreates a trajectory consistent with the observed data until 1st of July. According to the model, most transmission was occurring ‘locally’ (i.e. in the model, 80% of transmission events occurred within spatially defined ‘communities’ of approximately 100 individuals). We show that the net effect of the various restrictions put into place in March can be captured by a reduction in transmission down to 12% of its pre-lockdown rate effective 28th March. By comparing different approaches to reducing transmission, we show that, while the timing of COVID-19 restrictions influences the role of the transmission rate on the number of COVID-related deaths, early reduction in long distance movements does not reduce death rates significantly. As this movement of individuals from more infected areas to less infected areas has a minimal impact on transmission, this suggests that the fraction of population already immune in infected communities was not a significant factor in these early stages of the national epidemic even when local clustering of infection is taken into account. The best fit model also shows a considerable influence of the health index of deprivation (part of the ‘index of multiple deprivations’) on mortality. The most likely value has the CA with the highest level of health-related deprivation to have on average, a 2.45 times greater mortality rate due to COVID-19 compared to the CA with the lowest, showing the impact of health-related deprivation even in the early stages of the COVID-19 national epidemic.

Controlling enzootic diseases, which generate a large cumulative burden and are often unregulated, is needed for sustainable farming, competitive agri-food chains, and veterinary public health. We discuss the benefits and challenges of mechanistic epidemiological modelling for livestock enzootics, with particular emphasis on the need for interdisciplinary approaches. We focus on issues arising when modelling pathogen spread at various scales (from farm to the region) to better assess disease control and propose targeted options. We discuss in particular the inclusion of farmers’ strategic decision-making, the integration of within-host scale to refine intervention targeting, and the need to ground models on data.

Stochastic mechanistic epidemiological models largely contribute to better understand pathogen emergence and spread, and assess control strategies at various scales (from withinhost to transnational scale). However, developing realistic models which involve multi-disciplinary knowledge integration faces three major challenges in predictive epidemiology: lack of readability once translated into simulation code, low reproducibility and reusability, and long development time compared to outbreak time scale. We introduce here emulsion, an artificial intelligence-based software intended to address those issues and help modellers focus on model design rather than programming. emulsion defines a domain-specific language to make all components of an epidemiological model (structure, processes, parameters…) explicit as a structured text file. This file is readable by scientists from other fields (epidemiologists, biologists, economists), who can contribute to validate or revise assumptions at any stage of model development. It is then automatically processed by emulsion generic simulation engine, preventing any discrepancy between model description and implementation. The modelling language and simulation architecture both rely on the combination of advanced artificial intelligence methods (knowledge representation and multi-level agent-based simulation), allowing several modelling paradigms (from compartment- to individual-based models) at several scales (up to metapopulation). The flexibility of emulsion and its capability to support iterative modelling are illustrated here through examples of progressive complexity, including late revisions of core model assumptions. emulsion is also currently used to model the spread of several diseases in real pathosystems. emulsion provides a command-line tool for checking models, producing model diagrams, running simulations, and plotting outputs. Written in Python 3, emulsion runs on Linux, MacOS, and Windows. It is released under Apache-2.0 license. A comprehensive documentation with installation instructions, a tutorial and many examples are available from: https://sourcesup.renater.fr/emulsion-public.

The cloud has emerged as an attractive platform for resource-intensive scientific applications, such as epidemic simulators. However, building and executing such applications in the cloud presents multiple challenges, including exploiting elasticity, handling failures, and simplifying multi-cloud deployment. To address these challenges, this paper proposes a novel, service-based framework called DiFFuSE that enables simulation applications with a bag-of-tasks structure to fully exploit cloud platforms. The paper describes how the framework is applied to restructure two legacy applications, simulating the spread of bovine viral diarrhea virus and Mycobac- terium avium subsp. paratuberculosis, into elastic, cloud-native applications. Experimental results show that the framework enhances application performance and allows exploring different cost-performance trade-offs while supporting automatic failure handling and elastic resource acquisition from multiple clouds.

To explore the regional spread of endemic pathogens, investigations are required both at within and between population levels. The bovine viral diarrhoea virus (BVDV) is such a pathogen, spreading among cattle herds mainly due to trade movements and neighbourhood contacts, and causing an endemic disease with economic consequences. To assess the contribution of both transmission routes on BVDV regional and local spread, we developed an original epidemiological model combining data-driven and mechanistic approaches, accounting for heterogeneous within-herd dynamics, animal movements and neighbourhood contacts. Extensive simulations were performed over 9 years in an endemic context in a French region with high cattle density. The most uncertain model parameters were calibrated on summary statistics of epidemiological data, highlighting that neighbourhood contacts and within-herd transmission should be high. We showed that neighbourhood contacts and trade movements complementarily contribute to BVDV spread on a regional scale in endemically infected and densely populated areas, leading to intense fade-out/colonization events: neighbourhood contacts generate the vast majority of outbreaks (72%) but mostly in low immunity herds and correlated to a rather short presence of persistently infected animals (P); trade movements generate fewer infections but could affect herds with higher immunity and generate a prolonged presence of P. Both movements and neighbourhood contacts should be considered when designing control or eradication strategies for densely populated region.

Vibrio aestuarianus is a bacterium related to mortality outbreaks in Pacific oysters, Crassostrea gigas, in France, Ireland, and Scotland since 2011. Knowledge about its transmission dynamics is still lacking, impairing guidance to prevent and control the related disease spread. Mathematical modeling is a relevant approach to better understand the determinants of a disease and predict its dynamics in imperfectly observed pathosystems. We developed here the first marine epidemiological model to estimate the key parameters of V. aestuarianus infection at a local scale in a small and closed oyster population under controlled laboratory conditions. Using a compartmental model accounting for free-living bacteria in seawater, we predicted the infection dynamics using dedicated and model-driven collected laboratory experimental transmission data. We estimated parameters and showed that waterborne transmission of V. aestuarianus is possible under experimental conditions, with a basic reproduction number R0 of 2.88 (95% CI: 1.86; 3.35), and a generation time of 5.5 days. Our results highlighted a bacterial dose–dependent transmission of vibriosis at local scale. Global sensitivity analyses indicated that the bacteria shedding rate, the concentration of bacteria in seawater that yields a 50% chance of catching the infection, and the initial bacterial exposure dose W0 were three critical parameters explaining most of the variation in the selected model outputs related to disease spread, i.e., R0, the maximum prevalence, oyster survival curve, and bacteria concentration in seawater. Prevention and control should target the exposure of oysters to bacterial concentration in seawater. This combined laboratory–modeling approach enabled us to maximize the use of information obtained through experiments. The identified key epidemiological parameters should be better refined by further dedicated laboratory experiments. These results revealed the importance of multidisciplinary approaches to gain consistent insights into the marine epidemiology of oyster diseases.

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.

Johne’s disease (paratuberculosis), a worldwide enzootic disease of cattle caused by Mycobacterium avium subsp. paratuberculosis (Map), mainly introduced into farms by purchasing infected animals, has a large economic impact for dairy producers. Since diagnostic tests used in routine are poorly sensitive, observing Map spread in the field is hardly possible, whereas there is a need for evaluating control strategies. Our objective was to provide a modelling framework to compare the efficacy of regional control strategies combining internal biosecurity measures and testing of traded animals, against Map spread in a metapopulation of dairy cattle herds. We represented 12,857 dairy herds located in Brittany (France), based on data from 2005 to 2013, used to calibrate herd sizes and demographic rates and to define trade events in a multiscale model of Map infection dynamics. By clustering and categorical descriptive analysis of intensive simulations of this model, based on a numerical experimental design, a large panel of control measures was explored. Their efficacy was assessed on model outputs such as the prevalence and probability of extinction at the metapopulation level. In addition, we proposed a scoring for the effort required to implement control measures and prioritized control strategies based on their theoretical epidemiological efficacy. Our results clearly indicate that eradication cannot be achieved on the mid term using available control measures. However, we identified relevant combinations of measures that lead to the control of Map spread with realistic level of implementation and coverage. The study highlights the challenge of controlling paratuberculosis in an endemically infected region as related to the poor test characteristics and frequent trade movements. Our model lays the foundations for a flexible and efficient tool to help collective animal health managers in defining relevant control strategies at a regional scale, accounting for local specificities in terms of contact network and farms’ characteristics.

We present a simple and efficient microeconomic model incorporating generic components for trade of cattle at the level of agricultural holdings, using supply-and-demand processes as a basis for animal movements. By combining within-node dynamics of stocks with stochastic jumps describing animal exchanges between nodes, our model reproduces the dynamical network of animal trade between holdings. Variants of the model, either closely calibrated on the data, or based on mechanistic economical assumptions, are considered. In addition to mathematical investigation of the average dynamical behaviour, model performances are assessed on three datasets (including or not intermediary trade operators such as marketplaces and assembly centres), covering 5 years of cattle movement in the département of Finistère (France), as a case study. Model outputs are compared with data regarding the average size of traded batches per holding and the length of temporal trade chains with the potential to transmit disease across the market. We observe an overall good agreement with the data, with variations between models, depending on the criteria (aggregated or time-varying) and datasets considered. These findings highlight the impact of high-volume nodes such as markets and assembly centres on trade flows, as well as the importance of correctly reproducing temporal features of dynamical trade networks. Our study represents one of the first attempts of building dynamical models of livestock trade networks, incorporating simple economic mechanisms, proving to be useful for analysing and predicting cattle trade movements. Future work in this direction might lead to a more detailed analysis of the subnetworks (e.g. beef, dairy) of this complex market, as well as a better understanding of the economic drivers underlying cattle movement, allowing the improvement of predictions of its temporal features, especially in the context of outbreaks.

Mycobacterium avium subsp. paratuberculosis (Map) causes Johne’s disease, with large economic consequences for dairy cattle producers worldwide. Map spread between farms is mainly due to animal movements. Locally, herd size and management are expected to influence infection dynamics. To provide a better understanding of Map spread between dairy cattle farms at a regional scale, we describe the first spatio-temporal model accounting simultaneously for population and infection dynamics and indirect local transmission within dairy farms, and between-farm transmission through animal trade. This model is applied to Brittany, a French region characterized by a high density of dairy cattle, based on data on animal trade, herd size and farm management (birth, death, renewal, and culling) from 2005 to 2013 for 12 857 dairy farms. In all simulated scenarios, Map infection highly persisted at the metapopulation scale. The characteristics of initially infected farms strongly impacted the regional Map spread. Network-related features of incident farms influenced their ability to contaminate disease-free farms. At the herd level, we highlighted a balanced effect of the number of animals purchased: when large, it led to a high probability of farm infection but to a low persistence. This effect was reduced when prevalence in initially infected farms increased. Implications of our findings in the current enzootic situation are that the risk of infection quickly becomes high for farms buying more than three animals per year. Even in regions with a low proportion of infected farms, Map spread will not fade out spontaneously without the use of effective control strategies.

Seasonal 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.

Coastal and estuarine nursery grounds are essential habitats for sustaining flatfish stocks since only these shallow and productive areas provide the high food supply that allows maximizing juvenile growth and survival in most flatfish species. However, the main organic matter sources at the basis of benthic food webs might differ drastically between estuarine nursery grounds under strong freshwater influences, where food webs are mainly supported by continental organic matter, and coastal ecosystems under limited freshwater influence, where the local marine primary production is the main source of carbon for the benthos. To better understand the links between continental inputs to the coastal zone and stock maintenance in the highly prized common sole, Solea solea (L.), we investigated the variability in the organic matter sources supporting the growth of its young-of-the-year (YoY) in five contrasted estuarine and coastal nursery grounds under varying freshwater influence. Stable isotopes of carbon and nitrogen allowed tracing the origin of the organic matter exploited by YoY soles in the very first months following their benthic settlement, i.e. when most of the juvenile mortality occurs in the species. A mixing model was run to unravel and quantify the contribution of all major potential sources of organic matter to sole food webs, with a sensitivity analysis allowing assessment of the impact of various trophic enrichment factors on model outputs. This meta-analysis demonstrated a relative robustness of the estimation of the respective contributions of the various organic matter sources. At the nursery scale, the upstream increase in freshwater organic matter exploitation by YoY soles and its positive correlation with inter-annual variations in the river flow confirmed previous conclusions about the importance of organic matter from continental origin for juvenile production. However, inter-site differences in the organic matter sources exploited for growth showed that, although freshwater organic matter use is significant in all nursery sites, it is never dominant, with especially high contributions of local primary production by microphytobenthos or saltmarsh macrophytes to juvenile sole growth in tidal nursery ecosystems. These patterns stress the need for maintaining both the intensity of freshwater inputs to the coastal zone and of local autochthonous primary production (especially that of the intertidal microphytobenthos) to preserve the nursery function of coastal and estuarine ecosystems.

Animal trade movements form complex and dynamic networks of contacts between herds, and are a major pathway for pathogen spread. Bovine paratuberculosis, due to Mycobacterium avium subsp. paratuberculosis (Map), is a widespread endemic disease, transmitted between herds through trade movements of undetected infected animals. It has a strong economic impact, causing production losses and premature culling. Besides, this chronic disease is characterized by a long incubation period and poorly sensitive screening tests. Therefore, field observation of Map spread is barely possible and its control remains a major challenge. The objective of this PhD thesis is to better understand Map spread at a regional scale using a modeling approach, and to compare control strategies combining internal and external biosecurity measures. Our model is the first multiscale mechanistic model of Map spread between dairy cattle herds, considering stochastic intra-herd dynamics (demography and infection), explicit indirect transmission, and heterogeneity of herd characteristics and livestock trade movements based on field data. Our results provide the essential foundation for a better understanding of Map spread in an endemic area, highlighting the importance of wholesaler holdings. Applied to the Brittany region (France), the model allows assessing the effectiveness of a large panel of control measures used alone and in combination, highlighting the key role of calf management. Using Bayesian inference from epidemiological data allowed informing on the risk of introducing an infected animal through animal purchases and the within-herd transmission rate. The effectiveness of controlling Map will depend on an efficient coordination of interventions and on available diagnostic tools.