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

Picault, Sébastien; Huang, Yili; Sicard, Vianney; Arnoux, Sandie; Beaunée, Gaël; Ezanno, Pauline

doi:10.1371/journal.pcbi.1007342

Cited by 21 publications

(21 citation statements)

References 37 publications

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“…For example, mathematical models have been used to describe the epidemic spreading in temporal contact networks, which provides an opportunity for tracing outbreaks and contact among nodes over time (Guinat et al., 2016; Colman et al., 2019; Ferdousi et al, (2019; Sterchi et al., 2019). Some examples of software readily available to model epidemic on networks include EMULSION (Picault et al., 2019), GEMFsim (Sahneh et al., 2017), EpiModel (Jenness et al., 2018) and SimInf (Widgren et al., 2019), computationally and algorithm used are diverse, which make some more efficient or flexible than other. We chose to use SimInf, mainly because it was design to accommodate large networks, thus feasible to be use over national or sub‐national networks.…”

Section: Introductionmentioning

confidence: 99%

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

Machado¹,

Galvis²,

Lopes

et al. 2020

Transbound Emerg Dis

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Tracking animal movements over time can fundamentally determine the success of disease control interventions throughout targeting farms that are tightly connected. In commercial pig production, animals are transported between farms based on growth stages, thus it generates time-varying contact networks that will influence the dynamics of disease spread. Still, risk-based surveillance strategies are mostly based on a static network. In this study, we reconstructed the static and temporal pig networks of one Brazilian state from 2017 to 2018, comprising 351,519 movements and 48 million transported pigs. The static networks failed to capture time-respecting movement pathways. Therefore, we propose a time-dependent network susceptible-infected (SI) model to simulate the temporal spread of an epidemic over the pig network globally through the temporal movement of animals among farms, and locally with a stochastic compartmental model in each farm, configured to calculate the minimum number of target farms needed to achieve effective disease control. In addition, we propagated disease on the pig temporal network to calculate the cumulative contacts as a proxy of epidemic sizes and evaluated the impact of network-based disease control strategies. The results show that targeting the first 1,000 farms ranked by degree would be sufficient and feasible to diminish disease spread considerably. Our finding also suggested that assuming a worst-case scenario in which every movement transmit disease, pursuing farms by degree would limit the transmission to up to 29 farms over the two years period, which is lower than the number of infected farms for random surveillance, with epidemic sizes of 2,593 farms. The top 1,000 farms could benefit from enhanced biosecurity plans and improved surveillance, which constitute important next steps in strategizing targeted disease control .

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Section: Introductionmentioning

confidence: 99%

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

Machado¹,

Galvis²,

Lopes

et al. 2020

Transbound Emerg Dis

View full text Add to dashboard Cite

show abstract

“…The rise of computer technology allowed for development of computational tools to address biological issues that could not be unravelled in the past, providing decision-support system to stakeholders. The emergence of artificial intelligence in the field of epidemiology may be a key for unifying multiple paradigms into a single multiscale framework [ 109 ]. The understanding of how control measures applied at one scale impact the system at up- and downward scales is essential to have a global overview of their efficiencies.…”

Section: Discussionmentioning

confidence: 99%

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

Andraud

Rose

2020

Porc Health Manag

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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.

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“…Autonomous software agents enable to represent various levels of abstraction and organisation [ 55 ], helping modellers go more easily back and forth within small and larger scales, and ensure that all relevant mechanisms are adequately formalised at proper scales (i.e., scale-dependency of determinants and drivers in hierarchical living systems). Combining knowledge representation (through a DSL) and such a multi-level agent-based simulation architecture (e.g., in EMULSION, Figure 2 , [ 56 ]) enables to encompass several types of models (e.g., compartmental, individual-based) and scales (e.g., individual, population, territory), and it tackles simultaneously the recurring needs for transparency, reliability and flexibility in modelling contagious diseases. This approach should also facilitate in the future the production of support decision tools for veterinary and public health managers and stakeholders.…”

Section: Contribution Of Ai To Better Understand Animal Epidemiologicmentioning

confidence: 99%

Research perspectives on animal health in the era of artificial intelligence

Ezanno

Picault

Beaunée

et al. 2021

Vet Res

Self Cite

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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.

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

Cited by 21 publications

References 37 publications

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

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

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

Research perspectives on animal health in the era of artificial intelligence

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