EMULSION: Transparent and flexible multiscale stochastic models in human, animal and plant epidemiology

Harnessing uncertainty: A deep mechanistic approach for cautious diagnostic and forecast of Bovine Respiratory Disease

Bovine Respiratory Disease (BRD) is a prevalent infectious disease of respiratory tract in cattle, presenting challenges in accurate diagnosis and forecasting due to the complex interactions of multiple risk factors. Common methods, including mathematical epidemiological models and data-driven approaches such as machine learning models, face limitations such as difficult parameter estimation or the need for data across all potential outcomes, which is challenging given the scarcity and noise in observing BRD processes. In response to these challenges, we introduce a novel approach known as the Bayesian Deep Mechanistic method. This method couples a data-driven model with a mathematical epidemiological model while accounting for uncertainties within the processes. By utilising 265 lung ultrasound videos as sensor data from 163 animals across 9 farms in France, we trained a Bayesian deep learning model to predict the infection status (infected or non-infected) of an entire batch of 12 animals, also providing associated confidence levels. These predictions, coupled with their confidence levels, were used to filter out highly uncertain diagnoses and diffuse uncertainties into the parameterisation of a mathematical epidemiological model to forecast the progression of infected animals. Our findings highlight that considering the confidence levels (or uncertainties) of predictions enhances both the diagnosis and forecasting of BRD. Uncertainty-aware diagnosis reduced errors to 32 %, outperforming traditional automatic diagnosis. Forecast relying on veterinarian diagnoses, considered the most confident, had a 23 % error, whilst forecast taking into account diagnosis uncertainties had a close 27.2 % error. Building upon uncertainty-awareness, our future research could explore integrating multiple types of sensor data, such as video surveillance, audio recordings, and environmental parameters, to provide a comprehensive evaluation of animal health, employing multi-modal methods for processing this diverse data.

Leveraging artificial intelligence and software engineering methods in epidemiology for the co-creation of decision-support tools based on mechanistic models

Epidemiological modeling is a key lever for infectious disease control and prevention on farms. It makes it possible to understand the spread of pathogens, but also to compare intervention scenarios even in counterfactual situations. However, the actual capability of decision makers to use mechanistic models to support timely interventions is limited. This study demonstrates how artificial intelligence (AI) techniques can make mechanistic epidemiological models more accessible to farmers and veterinarians, and how to transform such models into user-friendly decision-support tools (DST). By leveraging knowledge representation methods, such as the textual formalization of model components through a domain-specific language (DSL), the co-design of mechanistic models and DST becomes more efficient and collaborative. This facilitates the integration of explicit expert knowledge and practical insights into the modeling process. Furthermore, the utilization of AI and software engineering enables the automation of web application generation based on existing mechanistic models. This automation simplifies the development of DST, as tool designers can focus on identifying users' needs and specifying expected features and meaningful presentations of outcomes, instead of wasting time in writing code to wrap models into web apps. To illustrate the practical application of this approach, we consider the example of Bovine Respiratory Disease (BRD), a tough challenge in fattening farms where young beef bulls often develop BRD shortly after being allocated into pens. BRD is a multi-factorial, multi-pathogen disease that is difficult to anticipate and control, often resulting in the massive use of antimicrobials to mitigate its impact on animal health, welfare, and economic losses. The DST developed from an existing mechanistic BRD model empowers users, including farmers and veterinarians, to customize scenarios based on their specific farm conditions. It enables them to anticipate the effects of various pathogens, compare the epidemiological and economic outcomes associated with different farming practices, and decide how to balance the reduction of disease impact and the reduction of antimicrobial usage (AMU). The generic method presented in this article illustrates the potential of artificial intelligence (AI) and software engineering methods to enhance the co-creation of DST based on mechanistic models in veterinary epidemiology. The corresponding pipeline is distributed as an open-source software. By leveraging these advancements, this research aims to bridge the gap between theoretical models and the practical usage of their outcomes on the field.

Simulating real-life scenarios to better understand the spread of diseases under different contexts

Current statistical models to simulate pandemics miss the most relevant information about the close atomic interactions between individuals which is the key aspect of virus spread. Thus, they lack a proper visualization of such interactions and their impact on virus spread. In the field of computer graphics, and more specifically in computer animation, there have been many crowd simulation models to populate virtual environments. However, the focus has typically been to simulate reasonable paths between random or semi-random locations in a map, without any possibility of analyzing specific individual behavior. We propose a crowd simulation framework to accurately simulate the interactions in a city environment at the individual level, with the purpose of recording and analyzing the spread of human diseases. By simulating the whereabouts of agents throughout the day by mimicking the actual activities of a population in their daily routines, we can accurately predict the location and duration of interactions between individuals, thus having a model that can reproduce the spread of the virus due to human-to-human contact. Our results show the potential of our framework to closely simulate the virus spread based on real agent-to-agent contacts. We believe that this could become a powerful tool for policymakers to make informed decisions in future pandemics and to better communicate the impact of such decisions to the general public.

Climate change impacts on plant pathogens, food security and paths forward

Plant disease outbreaks pose significant risks to global food security and environmental sustainability worldwide, and result in the loss of primary productivity and biodiversity that negatively impact the environmental and socio-economic conditions of affected regions. Climate change further increases outbreak risks by altering pathogen evolution and host-pathogen interactions and facilitating the emergence of new pathogenic strains. Pathogen range can shift, increasing the spread of plant diseases in new areas. In this Review, we examine how plant disease pressures are likely to change under future climate scenarios and how these changes will relate to plant productivity in natural and agricultural ecosystems. We explore current and future impacts of climate change on pathogen biogeography, disease incidence and severity, and their effects on natural ecosystems, agriculture and food production. We propose that amendment of the current conceptual framework and incorporation of eco-evolutionary theories into research could improve our mechanistic understanding and prediction of pathogen spread in future climates, to mitigate the future risk of disease outbreaks. We highlight the need for a science-policy interface that works closely with relevant intergovernmental organizations to provide effective monitoring and management of plant disease under future climate scenarios, to ensure long-term food and nutrient security and sustainability of natural ecosystems.

Modeling the effects of farming practices on bovine respiratory disease in a multi-batch cattle fattening farm

Bovine Respiratory Disease (BRD) affects young bulls, causing animal welfare and health concerns as well as economical costs. BRD is caused by an array of viruses and bacteria and also by environmental and abiotic factors. How farming practices influence the spread of these causal pathogens remains unclear. Our goal was to assess the impact of zootechnical practices on the spread of three causal agents of BRD, namely the bovine respiratory syncytial virus (BRSV), Mannheimia haemolytica and Mycoplasma bovis. In that extent, we used an individual based stochastic mechanistic model monitoring risk factors, infectious processes, detection and treatment in a farm possibly featuring several batches simultaneously. The model was calibrated with three sets of parameters relative to each of the three pathogens using data extracted from literature. Separated batches were found to be more effective than a unique large one for reducing the spread of pathogens, especially for BRSV and M.bovis. Moreover, it was found that allocating high risk and low risk individuals into separated batches participated in reducing cumulative incidence, epidemic peaks and antimicrobial usage, especially for M. bovis. Theses findings rise interrogations on the optimal farming practices in order to limit BRD occurrence and pave the way to models featuring coinfections and collective treatments p { line-height: 115%; margin-bottom: 0.25 cm; background: transparent}a:link { color: #000080; text-decoration: underline}a.cjk:link { so-language: zxx}a.ctl:link { solanguage: zxx}.

The DECIDE project: from surveillance data to decision-support for farmers and veterinarians

Farmers, veterinarians and other animal health managers in the livestock sector are currently missing sufficient information on prevalence and burden of contagious endemic animal diseases. They need adequate tools for risk assessment and prioritization of control measures for these diseases. The DECIDE project develops data-driven decision-support tools, which present (i) robust and early signals of disease emergence and options for diagnostic confirmation; and (ii) options for controlling the disease along with their implications in terms of disease spread, economic burden and animal welfare. DECIDE focuses on respiratory and gastro-intestinal syndromes in the three most important terrestrial livestock species (pigs, poultry, cattle) and on reduced growth and mortality in two of the most important aquaculture species (salmon and trout). For each of these, we (i) identify the stakeholder needs; (ii) determine the burden of disease and costs of control measures; (iii) develop data sharing frameworks based on federated data access and meta-information sharing; (iv) build multivariate and multi-level models for creating early warning systems; and (v) rank interventions based on multiple criteria. Together, all of this forms decision-support tools to be integrated in existing farm management systems wherever possible and to be evaluated in several pilot implementations in farms across Europe. The results of DECIDE lead to improved use of surveillance data and evidence-based decisions on disease control. Improved disease control is essential for a sustainable food chain in Europe with increased animal health and welfare and that protects human health.

Inferring ASF transmission in domestic pigs and wild boars using a paired model iterative approach

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.

Modelling the effects of antimicrobial metaphylaxis and pen size on bovine respiratory disease in high and low risk fattening cattle

Bovine respiratory disease (BRD) dramatically affects young calves, especially in fattening facilities, and is difficult to understand, anticipate and control due to the multiplicity of factors involved in the onset and impact of this disease. In this study we aimed to compare the impact of farming practices on BRD severity and on antimicrobial usage. We designed a stochastic individual-based mechanistic BRD model which incorporates not only the infectious process, but also clinical signs, detection methods and treatment protocols. We investigated twelve contrasted scenarios which reflect farming practices in various fattening systems, based on pen sizes, risk level, and individual treatment vs. collective treatment (metaphylaxis) before or during fattening. We calibrated model parameters from existing observation data or literature and compared scenario outputs regarding disease dynamics, severity and mortality. The comparison of the trade-off between cumulative BRD duration and number of antimicrobial doses highlighted the added value of risk reduction at pen formation even in small pens, and acknowledges the interest of collective treatments for high-risk pens, with a better efficacy of treatments triggered during fattening based on the number of detected cases.

The African swine fever modelling challenge: Objectives, model description and synthetic data generation

African swine fever (ASF) is an emerging disease currently spreading at the interface between wild boar and pig farms in Europe and Asia. Current disease control regulations, which involve massive culling with significant economic and animal welfare costs, need to be improved. Modelling enables relevant control measures to be explored, but conducting the exercise during an epidemic is extremely difficult. Modelling challenges enhance modellers' ability to timely advice policy makers, improve their readiness when facing emerging threats, and promote international collaborations. The ASF-Challenge, which ran between August 2020 and January 2021, was the first modelling challenge in animal health. In this paper, we describe the objectives and rules of the challenge. We then demonstrate the mechanistic multi-host model that was used to mimic as accurately as possible an ASF-like epidemic, provide a detailed explanation of the surveillance and intervention strategies that generated the synthetic data, and describe the different management strategies that were assessed by the competing modelling teams. We then outline the different technical steps of the challenge as well as its environment. Finally, we synthesize the lessons we learnt along the way to guide future modelling challenges in animal health.

Modeling Paratuberculosis Transmission in a Small Dairy Herd Typical of Slovenia Suggests That Different Models Should Be Used to Study Disease Spread in Herds of Different Sizes

This study aimed to investigate the possible dynamics of paratuberculosis or Johne’s disease in a typical Slovenian dairy herd of about 17 cows. Paratuberculosis is a worldwide endemic disease of cattle caused by Mycobacterium avium subsp. paratuberculosis (MAP) and is associated with significant economic losses. We developed a stochastic compartmental model with two pathways of disease progression, infections of adult cows and infections of young animals through horizontal and vertical transmission, and transmission through animal movements. The average proportions of subclinically and clinically infected cows were 4% and 0.47%, respectively. The prevalence within the herd, which included latently infected animals, averaged 7.13% and ranged from 0% to 70.59%. Under the given circumstances, the results showed a relatively high rate of spontaneous elimination (0.22 per herd per year) of the disease and a high rate of reinfection (0.18 per herd per year) facilitated by active animal trade. To our knowledge, this stochastic compartmental model is the first to be developed specifically to represent a small dairy herd and could apply to other countries with a similar structure of dairy farms. The results suggest that different models should be used to study MAP spread in herds of various sizes.

DTU-DADS-Aqua: A simulation framework for modelling waterborne spread of highly infectious pathogens in marine aquaculture

Simulation models are useful tools to predict and elucidate the effects of factors influencing the occurrence and spread of epidemics in animal populations, evaluate the effectiveness of different control strategies and ultimately inform decision-makers about mitigations to reduce risk. There is a paucity of simulation models to study waterborne transmission of viral and bacterial pathogens in marine environments. We developed a stochastic, spatiotemporal hybrid simulation model (DTU-DADS-Aqua) that incorporates a compartmental model for infection spread within net-pens, an agent-based model for infection spread between net-pens within and between sites and uses seaway distance to inform farm-site hydroconnectivity. The model includes processes to simulate infection transmission and control over surveillance, detection and depopulation measures. Different what-if scenarios can be explored according to the input data provided and user-defined parameter values, such as daily surveillance and depopulation capacities or increased animal mortality that triggers diagnostic testing to detect infection. The latter can be easily defined in a software application, in which results are summarized after each simulation. To demonstrate capabilities of the model, we simulated the spread of infectious salmon anaemia virus (ISAv) for realistic scenarios in a transboundary population of farmed Atlantic salmon (Salmo salar L.) in New Brunswick, Canada and Maine, United States. We assessed the progression of infection in the different simulated outbreak scenarios, allowing for variation in the control strategies adopted for ISAv. Model results showed that improved disease detection, coupled with increasing surveillance visits to farm-sites and increased culling capacity for depopulation of infected net-pens reduced the number of infected net-pens and outbreak duration but the number of ISA-infected farm sites was minimally affected. DTU-DADS-Aqua is a flexible modelling framework, which can be applied to study different infectious diseases in the aquatic environment, allowing the incorporation of alternative transmission and control dynamics. The framework is open-source and available at https://github.com/upei-aqua/DTU-DADS-Aqua.

Research perspectives on animal health in the era of artificial intelligence

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 × 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.

Quantifying the dynamics of pig movements improves targeted disease surveillance and control plans

Tracking animal movements over time may fundamentally determine the success of disease control interventions. In commercial pig production growth stages determine animal transportation schedule, thus it generates time-varying contact networks showed to influence the dynamics of disease spread. In this study, we reconstructed pig networks of one Brazilian state from 2017 to 2018, comprising 351,519 movements and 48 million transported pigs. The static networks view did not capture time-respecting movement pathways. For this reason, we propose a time-dependent network approach. A susceptible-infected model was used to spread an epidemic over the pig network globally through the temporal between-farm networks, and locally by a stochastic model to account for within-farm dynamics. We propagated disease to calculate the cumulative contacts as a proxy of epidemic sizes and evaluate the impact of network-based disease control strategies in the absence of other intervention alternatives. The results show that targeting 1,000 farms ranked by degree would be sufficient and feasible to diminish disease spread considerably. Our modelling results indicated that independently from where initial infections were seeded (i.e. independent, commercial farms), the epidemic sizes and the number of farms needed to be targeted to effectively control disease spread were quite similar; indeed, this finding can be explained by the presence of contact among all pig operation types The proposed strategy limited the transmission the total number of secondarily infected farms to 29, over two simulated years. The identified 1,000 farms would benefit from enhanced biosecurity plans and improved targeted surveillance. Overall, the modelling framework provides a parsimonious solution for targeted disease surveillance when temporal movement data are available.

Modelling infectious viral diseases in swine populations: a state of the art

Mathematical modelling is nowadays a pivotal tool for infectious diseases studies, completing regular biological investigations. The rapid growth of computer technology allowed for development of computational tools to address biological issues that could not be unravelled in the past. The global understanding of viral disease dynamics requires to account for all interactions at all levels, from within-host to between-herd, to have all the keys for development of control measures. A literature review was performed to disentangle modelling frameworks according to their major objectives and methodologies. One hundred and seventeen articles published between 1994 and 2020 were found to meet our inclusion criteria, which were defined to target papers representative of studies dealing with models of viral infection dynamics in pigs. A first descriptive analysis, using bibliometric indexes, permitted to identify keywords strongly related to the study scopes. Modelling studies were focused on particular infectious agents, with a shared objective: to better understand the viral dynamics for appropriate control measure adaptation. In a second step, selected papers were analysed to disentangle the modelling structures according to the objectives of the studies. The system representation was highly dependent on the nature of the pathogens. Enzootic viruses, such as swine influenza or porcine reproductive and respiratory syndrome, were generally investigated at the herd scale to analyse the impact of husbandry practices and prophylactic measures on infection dynamics. Epizootic agents (classical swine fever, foot-and-mouth disease or African swine fever viruses) were mostly studied using spatio-temporal simulation tools, to investigate the efficiency of surveillance and control protocols, which are predetermined for regulated diseases. A huge effort was made on model parameterization through the development of specific studies and methodologies insuring the robustness of parameter values to feed simulation tools. Integrative modelling frameworks, from within-host to spatio-temporal models, is clearly on the way. This would allow to capture the complexity of individual biological variabilities and to assess their consequences on the whole system at the population level. This would offer the opportunity to test and evaluate in silico the efficiency of possible control measures targeting specific epidemiological units, from hosts to herds, either individually or through their contact networks. Such decision support tools represent a strength for stakeholders to help mitigating infectious diseases dynamics and limiting economic consequences.

How mechanistic modelling supports decision making for the control of enzootic infectious diseases

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.