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Akhmetzhanov AR, Jung SM, Lee H, Linton NM, Yang Y, Yuan B, Nishiura H. Reconstruction and analysis of the transmission network of African swine fever in People's Republic of China, August 2018-September 2019. Epidemiol Infect 2024; 152:e27. [PMID: 38282573 PMCID: PMC10894904 DOI: 10.1017/s0950268824000086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 12/14/2023] [Accepted: 12/25/2023] [Indexed: 01/30/2024] Open
Abstract
Introduction of African swine fever (ASF) to China in mid-2018 and the subsequent transboundary spread across Asia devastated regional swine production, affecting live pig and pork product-related markets worldwide. To explore the spatiotemporal spread of ASF in China, we reconstructed possible ASF transmission networks using nearest neighbour, exponential function, equal probability, and spatiotemporal case-distribution algorithms. From these networks, we estimated the reproduction numbers, serial intervals, and transmission distances of the outbreak. The mean serial interval between paired units was around 29 days for all algorithms, while the mean transmission distance ranged 332 -456 km. The reproduction numbers for each algorithm peaked during the first two weeks and steadily declined through the end of 2018 before hovering around the epidemic threshold value of 1 with sporadic increases during 2019. These results suggest that 1) swine husbandry practices and production systems that lend themselves to long-range transmission drove ASF spread; 2) outbreaks went undetected by the surveillance system. Efforts by China and other affected countries to control ASF within their jurisdictions may be aided by the reconstructed spatiotemporal model. Continued support for strict implementation of biosecurity standards and improvements to ASF surveillance is essential for halting transmission in China and spread across Asia.
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Affiliation(s)
- Andrei R Akhmetzhanov
- Global Health Program & Institute of Epidemiology and Preventive Medicine, National Taiwan University College of Public Health, Taipei, Taiwan
- Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Sung-Mok Jung
- Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Hyojung Lee
- Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Natalie M Linton
- Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Yichi Yang
- Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Baoyin Yuan
- Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Hiroshi Nishiura
- School of Public Health, Kyoto University, Kyoto, Japan
- CREST, Japan Science and Technology Agency, Saitama, Japan
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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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Beaunée G, Deslandes F, Vergu E. Inferring ASF transmission in domestic pigs and wild boars using a paired model iterative approach. Epidemics 2023; 42:100665. [PMID: 36689877 DOI: 10.1016/j.epidem.2023.100665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 12/15/2022] [Accepted: 01/04/2023] [Indexed: 01/15/2023] Open
Abstract
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.
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Affiliation(s)
- G Beaunée
- Oniris, INRAE, BIOEPAR, 44300, Nantes, France.
| | - F Deslandes
- Université Paris-Saclay, INRAE, MaIAGE, 78350, Jouy-en-Josas, France
| | - E Vergu
- Université Paris-Saclay, INRAE, MaIAGE, 78350, Jouy-en-Josas, 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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