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Elucidating transmission parameters of African swine fever through wild boar carcasses by combining spatio-temporal notification data and agent-based modelling

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Abstract

Mechanistic epidemiological modelling has a role in predicting the spatial and temporal spread of emerging disease outbreaks and purposeful application of control treatment in animal populations. Especially in the case of infectious diseases newly emerging in an ecological habitat, lack of knowledge may hamper direct parameterisation of model algorithms. Along with experimental studies observational data is usually based on case notifications. These data are widely acknowledged as having “biological precision” due to e.g. convenient sampling procedures, host or human activity patterns or diagnostic limitations under field conditions. Nevertheless, the data comprises the complex spatio-temporal distribution patterns of the infection. In the literature, this data value is non-systematically used to inform model development although the need for and value of the data is well recognised. Here we address the newly emerging epidemic of African swine fever spreading in Eurasian wild boar using an existing spatio-temporally explicit individual-based model of wild boar. The disease etiology required the implementation of a sub-model regarding transmission by carcasses left after infected individuals have died. However, the experimental evidence about the mechanism involved in carcass-mediated spread of the infection still has to be established. We propose a mechanistic quantitative procedure to optimise calibration of several uncertain parameters based on the spatio-temporal model output from the simulation environment and the spatio-temporal case data of infectious disease notifications. The best agreement with the spatio-temporal spreading pattern was achieved by parameterisation that suggests ubiquitous accessibility to carcasses but with marginal chance of being contacted by conspecifics e.g., avoidance behaviour. The parameter estimation procedure is fully general and applicable to problems where spatio-temporal explicit data recording and spatial-explicit dynamic modelling was performed.

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Acknowledgements

The authors gratefully acknowledge C. Staubach (FLI, Germany), A. Viltrop (EMU, Estonia), A. Gogin (EFSA, Italy) and S. Khomenko (FAO, Italy) for their immense support with data validation, conversion and management.

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  1. Department of Ecological Modelling, PG Ecological Epidemiology, Helmholtz Centre for Environmental Research - UFZ, Permoserstr. 15, 04318, Leipzig, Germany

    Martin Lange & Hans-Hermann Thulke

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  1. Martin Lange
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  2. Hans-Hermann Thulke
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Correspondence to Martin Lange.

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Lange, M., Thulke, HH. Elucidating transmission parameters of African swine fever through wild boar carcasses by combining spatio-temporal notification data and agent-based modelling. Stoch Environ Res Risk Assess 31, 379–391 (2017). https://doi.org/10.1007/s00477-016-1358-8

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