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Hayes BH, Vergne T, Andraud M, Rose N. Mathematical modeling at the livestock-wildlife interface: scoping review of drivers of disease transmission between species. Front Vet Sci 2023; 10:1225446. [PMID: 37745209 PMCID: PMC10511766 DOI: 10.3389/fvets.2023.1225446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 08/17/2023] [Indexed: 09/26/2023] Open
Abstract
Modeling of infectious diseases at the livestock-wildlife interface is a unique subset of mathematical modeling with many innate challenges. To ascertain the characteristics of the models used in these scenarios, a scoping review of the scientific literature was conducted. Fifty-six studies qualified for inclusion. Only 14 diseases at this interface have benefited from the utility of mathematical modeling, despite a far greater number of shared diseases. The most represented species combinations were cattle and badgers (for bovine tuberculosis, 14), and pigs and wild boar [for African (8) and classical (3) swine fever, and foot-and-mouth and disease (1)]. Assessing control strategies was the overwhelming primary research objective (27), with most studies examining control strategies applied to wildlife hosts and the effect on domestic hosts (10) or both wild and domestic hosts (5). In spatially-explicit models, while livestock species can often be represented through explicit and identifiable location data (such as farm, herd, or pasture locations), wildlife locations are often inferred using habitat suitability as a proxy. Though there are innate assumptions that may not be fully accurate when using habitat suitability to represent wildlife presence, especially for wildlife the parsimony principle plays a large role in modeling diseases at this interface, where parameters are difficult to document or require a high level of data for inference. Explaining observed transmission dynamics was another common model objective, though the relative contribution of involved species to epizootic propagation was only ascertained in a few models. More direct evidence of disease spill-over, as can be obtained through genomic approaches based on pathogen sequences, could be a useful complement to further inform such modeling. As computational and programmatic capabilities advance, the resolution of the models and data used in these models will likely be able to increase as well, with a potential goal being the linking of modern complex ecological models with the depth of dynamics responsible for pathogen transmission. Controlling diseases at this interface is a critical step toward improving both livestock and wildlife health, and mechanistic models are becoming increasingly used to explore the strategies needed to confront these diseases.
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Affiliation(s)
- Brandon H. Hayes
- IHAP, Université de Toulouse, INRAE, ENVT, Toulouse, France
- Ploufragan-Plouzané-Niort Laboratory, The French Agency for Food, Agriculture and the Environment (ANSES), Ploufragan, France
| | | | - Mathieu Andraud
- Ploufragan-Plouzané-Niort Laboratory, The French Agency for Food, Agriculture and the Environment (ANSES), Ploufragan, France
| | - Nicolas Rose
- Ploufragan-Plouzané-Niort Laboratory, The French Agency for Food, Agriculture and the Environment (ANSES), Ploufragan, France
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Ezanno P, Picault S, Bareille S, Beaunée G, Boender GJ, Dankwa EA, Deslandes F, Donnelly CA, Hagenaars TJ, Hayes S, Jori F, Lambert S, Mancini M, Munoz F, Pleydell DRJ, Thompson RN, Vergu E, Vignes M, Vergne T. The African swine fever modelling challenge: Model comparison and lessons learnt. Epidemics 2022; 40:100615. [PMID: 35970067 DOI: 10.1016/j.epidem.2022.100615] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 06/29/2022] [Accepted: 07/20/2022] [Indexed: 11/26/2022] Open
Abstract
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.
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Affiliation(s)
| | | | - Servane Bareille
- INRAE, Oniris, BIOEPAR, 44300 Nantes, France; INRAE, ENVT, IHAP, Toulouse, France
| | | | | | | | | | - Christl A Donnelly
- Department of Statistics, University of Oxford, Oxford, United Kingdom; Department of Infectious Disease Epidemiology, Faculty of Medicine, School of Public Health, Imperial College London, United Kingdom
| | | | - Sarah Hayes
- Department of Infectious Disease Epidemiology, Faculty of Medicine, School of Public Health, Imperial College London, United Kingdom
| | - Ferran Jori
- CIRAD, INRAE, Université de Montpellier, ASTRE, 34398 Montpellier, France
| | - Sébastien Lambert
- Centre for Emerging, Endemic and Exotic Diseases, Department of Pathobiology and Population Sciences, Royal Veterinary College, University of London, United Kingdom
| | - Matthieu Mancini
- INRAE, Oniris, BIOEPAR, 44300 Nantes, France; INRAE, ENVT, IHAP, Toulouse, France
| | - Facundo Munoz
- CIRAD, INRAE, Université de Montpellier, ASTRE, 34398 Montpellier, France
| | - David R J Pleydell
- CIRAD, INRAE, Université de Montpellier, ASTRE, 34398 Montpellier, France
| | - Robin N Thompson
- Mathematics Institute and Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
| | - Elisabeta Vergu
- Université Paris-Saclay, INRAE, MaIAGE, 78350 Jouy-en-Josas, France
| | - Matthieu Vignes
- School of Mathematical and Computational Sciences, Massey University, Palmerston North, New Zealand
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