SimInf for spatio-temporal data-driven modeling of African swine fever in Swedish wildboar
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1
National Veterinary Institute, Department of Disease Control and Epidemiology, Sweden
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2
Uppsala University, Division of Scientific Computing, Department of Information Technology, Sweden
Introduction
African swine fever (ASF), caused by the African swine fever virus (ASFV), is a devastating disease that results in up to 100% mortality in domestic pigs and wild boar (Blome et al., 2013). In order to design efficient control strategies for ASF in wild boar, a better understanding of the ASF transmission dynamics in the wild boar population is needed. In wildlife ecology, behavior, movements of wildlife and interactions between individuals and groups have been studied and have an influence on disease spread (Blackwell et al., 2016; Patterson et al., 2017; Podgorski et al., 2018a, 2018b). Our objective is to develop a flexible and efficient simulation software suitable to study the spread of ASF in a free-living wild boar population, including the transmission path via the contaminated environment, spread via infected carcasses, and free movement within overlapping home ranges. Additionally, to design a workflow which can combine information on spatial constraints from maps such as roads, villages, water and land use as well as prior knowledge on animal movement dynamics.
Methods
The SimInf framework was extended to complete this modeling because it is already well suited for high-performance data-driven disease spread simulations (Widgren et al., 2018a, 2018b). In the extended SimInf framework, detailed map raster data can be incorporated to inform, for example, animal habitat quality data and spatial constraints. Additionally, as group of animals move, they interact with the raster cells. The model is simulated as a continuous time Markov chain and consists of three parts: i) a compartment model for the disease and population dynamics, ii) a random walk for movement of animal groups, and iii) the interaction between groups and the raster. These processes are defined in a text-based model-specification syntax built into the SimInf R-package to make it possible to specify the spread of different diseases in wildlife populations. This approach makes it possible for the modeler to focus on exploring the research question instead of complex programming.
The process of disease spread within groups is the same as the original SimInf and is specified by transitions between disease states in a compartment model. These transitions can be informed by the current state in the group i.e. the number of infected and susceptible individuals and by the contamination of the environment.
The groups of animals in the model move together across raster cells. The rate that a group moves can be governed by available landuse data. The direction that a group moves is currently random but restricted by a matrix of adjacency of cells to one another, which can be manipulated to mimic constraints such as fences in the environment.
Infected individuals can contaminate the environment. An infected animal contaminates the raster cell it occupies or dies in. This contamination decays over time and contributes to susceptible animals becoming infected.
The extended SimInf offers a very efficient and highly flexible tool to incorporate real data in wildlife disease simulations. It will facilitate complex epidemiological research to better understand disease transmission and improve design of intervention strategies for endemic and emerging threats.
Conclusion
In this presentation we will introduce the ongoing development of the SimInf R-package to support data-driven spatio-temporal simulations of disease transmission in wildlife. We will show the results of a study of the spread of ASF in Swedish wild boar and the impact of introducing artificial barriers to control movements on spread of disease.
Acknowledgements
This work is financially supported by the Swedish research council, Formas.
References
Blackwell PG, Niu M, Lambert MS, LaPoint SD (2016) Exact Bayesian inference for animal movement in continuous time. Methods in Ecology and Evolution, 7(2):184–195.
Blome S, Gabriel C, Beer M (2013) Pathogenesis of African swine fever in domestic pigs and European wild boar. Virus research, 173(1):122–130.
Patterson TA, Parton A, Langrock R, Blackwell PG, Thomas L, King R (2017) Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges. Advances in Statistical Analysis, 101(4):399–438.
Podgórski T, Apollonio M, Keuling O (2018a) Contact rates in wild boar populations: Implications for disease transmission. The Journal of Wildlife Management.
Podgórski T, Śmietanka K (2018b) Do wild boar movements drive the spread of african swine fever? Transboundary and Emerging Diseases.
Widgren S, Eriksson R, Bauer P, Engblom S (2018a) Siminf: An R package for data-driven stochastic disease spread simulations. arXiv preprint arXiv:1605.01421. https://arxiv.org/abs/1605.01421
Widgren S, Eriksson R, Bauer P, Engblom S (2018b) SimInf: a framework for data-driven stochastic disease spread simulations. R package available on CRAN. https://CRAN.R-project.org/package=SimInf.
Keywords:
ASF,
R package,
Computational epidemiology,
Discrete event simualtion,
Stochastic Modeling
Conference:
GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data, Davis, United States, 8 Oct - 10 Oct, 2019.
Presentation Type:
Regular oral presentation
Topic:
Special topic on African Swine Fever (ASF)
Citation:
Widgren
S,
Rosendal
T,
Engblom
S and
Ståhl
K
(2019). SimInf for spatio-temporal data-driven modeling of African swine fever in Swedish wildboar.
Front. Vet. Sci.
Conference Abstract:
GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data.
doi: 10.3389/conf.fvets.2019.05.00002
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Received:
10 Jun 2019;
Published Online:
27 Sep 2019.
*
Correspondence:
DVM, PhD. Stefan Widgren, National Veterinary Institute, Department of Disease Control and Epidemiology, Uppsala, Sweden, stefan.widgren@sva.se