swifco-rs documentation

Overview

The ASF wild boar model is a compilation of a spatially explicit, stochastic, individual-based demographic model for wild boars (Sus scrofa) in a structured landscape of habitat area. Superimposed is a transmission and disease course model for the ASFV.

Purpose

The purpose of model is to investigate various diagnostic profiles over time in an ASFV affected wild boar population. The model was originally aimed at an assessment of ASF spread in Estonian wild boar populations and the evaluation of reporting data from field surveys. Transmission of ASF infection is operated by direct contacts within groups of socialising wild boar hosts and with carcasses deposited in the habitat landscape.

Entities, state variables and scales

The model comprises three entities: spatial habitat units, connecting edges between these units, and wild boar individuals.

All processes take place on a raster map of spatial habitat units. Each cell represents a functional classification of a landscape denoting habitat quality. The cells of the model landscape typically represent about 9 km2 (3 km x 3 km) to 4 km2 (2 km x 2 km), encompassing a boar group’s core home range (Leaper et al. 1999). State variables comprise wild boar habitat quality of the grid cells. At run time, habitat quality is interpreted as breeding capacity, i.e. the number of female boars that are allowed to have offspring (explicit density regulation; [Jedrzejewska_1997]). Habitat quality may be applied to implement an external data set of spatial wild boar density distribution, i.e. by reversely adjusted breeding capacity. Habitat cells are connected by edges to the neighbouring eight cells. Connecting edges represent space between core habitat areas that is shared among neighbouring herds. Each habitat cell and each connecting edge handles a list of infectious wild boar carcasses. The third model entities are the individual wild boars. State variables of host individuals are the age in weeks (where one week represents the approximate ASF infectious period in wild boar [Blome_2012]), resulting in three age-classes: piglet (< 8 months ± 6 weeks), sub-adult (< 2 years ± 6 weeks) and adult. Accordingly, an age class transition event is stochastic. Each host individual has a location, which denotes its home range cell on the raster grid as well as its family group. Further, the individual host animal comprises an epidemiological status (susceptible, non-lethally infected, lethally infected, or immune after recovery or due to transient maternal antibodies). Sub-adult wild boar may disperse during the dispersal period (i.e., early summer) dependent on their demographic status (disperser or non-disperser).

Process overview and scheduling

The model proceeds in weekly time steps. Processes of each time step are performed as applicable: virus release, infection, dispersal of sub-adults, reproduction, ageing, mortality, hunting (for surveillance and depopulation), and control measures. Sub-models are executed in the given order. In the first week of each year, mortality probabilities are assigned stochastically to the age classes representing annual fluctuations in boar living conditions; and boars are assigned to breed or not, according to the carrying capacity of their home range cell.

Design concepts

Wild boar population dynamics emerge from individual behaviour, defined by age- dependent seasonal reproduction and mortality probabilities and age- and density-dependent dispersal behaviour, all including stochasticity. The epidemic course emerges stochastically from within group transmission of the infection, individual disease courses, spatial distribution and decay of infectious carcasses, contact to carcasses as well as wild boar dispersal. Stochasticity is included by representing demographic and behavioural parameters as probabilities or probability distributions. Annual fluctuations of living conditions are realised by annually varying mortality rates. Stochastic realisation of individual infection and disease courses are modelled explicitly.

Details

Input

External inputs or driving variables are typically included in the model setup via submodels with callback in their name. Those submodels are used to process external sources using Python code and then integrate the resulting data into the model dynamics.

Submodels

The following Python modules provide the submodels, observers and reporters which are combined to run simulation experiments via Python scripts. Their internals are further elaborated in the Rust documentation.

References

Bieber_Ruf_2005

Bieber, C. and Ruf, T. (2005). Population dynamics in wild boar Sus scrofa: ecology, elasticity of growth rate and implications for the management of pulsed resource consumers. Journal of Applied Ecology 42, 1203-1213

Blome_2012

Blome, S., Gabriel, C., Dietze, K., Breithaupt, A. and Beer, M. (2012). High virulence of African swine fever virus Caucasus isolate in European wild boars of all ages. Emerg. Infect. Diseases 18, 708

Depner_2000

Depner, K., Müller, T., Lange, E., Staubach, C. and Teuffert, J. (2000). Transient classical swine fever virus infection in wild boar piglets partially protected by maternal antibodies. Deutsche Tierärztliche Wochenschrift 107, 66-68

EFSA_2012

EFSA (2012). Scientific Opinion on foot and mouth disease in Thrace. The EFSA Journal 10, 2635. http://doi.org/10.2903/j.efsa.2012.2635

Focardi_1996

Focardi, S., Toso, S. and Pecchioli, E. (1996). The population modelling of fallow deer and wild boar in a Mediterranean ecosystem. Forest Ecology and Management 88, 7-14

Gaillard_1987

Gaillard, J.M., Vassant, J. and Klein, F. (1987). Some characteristics of the population dynamics of wild boar (Sus scrofa) in a hunted environment. Gibier Faune Sauvage 4, 31-47

Graf_2007

Graf, R.F., Kramer-Schadt, S. and Fernández, N. (2007). What you see is where you go? Modeling dispersal in mountainous landscapes. Landscape Ecology 22, 853-866

Guinat_2014

Guinat, C., Reis, A.L., Netherton, C.L., Goatley, L., Pfeiffer, D.U. and Dixon, L. (2014). Dynamics of African swine fever virus shedding and excretion in domestic pigs infected by intramuscular inoculation and contact transmission. Vet. Res. 45, 93

Jedrzejewska_1997

Jedrzejewska, B., Jedrzejewski, W., Bunevich, A.N., Milkowski, L. and Krasinski, Z.A. (1997). Factors shaping population densities and increase rates of ungulates in Bialowieza Primeval Forest (Poland and Belarus) in the 19th and 20th centuries. Acta Theriologica 42, 399-451

Jeltsch_1997

Jeltsch, F., Müller, M.S., Grimm, V. and Brandl, R. (1997). Pattern formation triggered by rare events: lessons from the spread of rabies. P. Roy. Soc. B 264, 495-503

Jezierski_1977

Jezierski, W. (1977). Longevity and mortality rate in a population of wild boar. Acta Theriologica 22, 337-348

Keuling_2013

Keuling, O., Baubet, E., Duscher, A., Ebert, C., Fischer, C., Monaco, A., Podgórski, T., Prévot, C., Ronnenberg, K., Sodeikat, G., Stier, N. and Thurfjell, H. (2013). Mortality rates of wild boar Sus scrofa L. in central Europe. European Journal of Wildlife Research, 59(6), 805-814 http://doi.org/10.1007/s10344-013-0733-8

Kramer-Schadt_2009

Kramer-Schadt, S., Fernández, N., Eisinger, D., Grimm, V. and Thulke, H.-H. (2009). Individual variations in infectiousness explain long-term disease persistence in wildlife populations. Oikos 118, 199-208. http://doi.org/10.1111/j.1600-0706.2008.16582.x

Lange_2017

Lange, M. and Thulke, H.-H. (2017). Elucidating transmission parameters of African swine fever through wild boar carcasses by combining spatio-temporal notification data and agent-based modelling. Stochastic Environmental Research and Risk Assessment, 31: 379-391. http://doi.org/10.1007/s00477-016-1358-8

Lange_2018

Lange, M., Guberti, V., Thulke, H.‐H. (2018). Understanding ASF spread and emergency control concepts in wild boar populations using individual‐based modelling and spatio‐temporal surveillance data. EFSA Supporting Publications 15 (11): EN‐1521. http://doi.org/10.2903/sp.efsa.2018.EN-1521

Pittiglio_2018

Pittiglio, C., Khomenko, S. and Beltran-Alcrudo, D. (2018). Wild boar mapping using population-density statistics: From polygons to high resolution raster maps. PLoS ONE, 13(5), 1–19. http://doi.org/10.1371/journal.pone.0193295

Pe_er_2013

Pe’er, G., Saltz, D., Münkemüller, T., Matsinos, Y.G. and Thulke, H.-H. (2013). Simple rules for complex landscapes: the case of hilltopping movements and topography. Oikos 122, 1483-1495

Probst_2017

Probst, C., Globig, A., Knoll, B., Conraths, F.J. and Depner, K. (2017). Behaviour of free ranging wild boar towards their dead fellows: potential implications for the transmission of African swine fever. R Soc Open Sci., 4(5):170054. http://doi.org/10.1098/rsos.170054

Prevot_2013

Prévot, C., Licoppe, A. Comparing red deer (Cervus elaphus L.) and wild boar (Sus scrofa L.) dispersal patterns in southern Belgium. Eur J Wildl Res 59, 795–803 (2013). https://doi.org/10.1007/s10344-013-0732-9

Ray_2014

Ray, R.R., Seibold, H. and Heurich, M. (2014). Invertebrates outcompete vertebrate facultative scavengers in simulated lynx kills in the Bavarian Forest National Park, Germany. Animal Biodiversity and Conservation 37, 77-88

Sodeikat_2003

Sodeikat, G. and Pohlmeyer, K. (2003). Escape movements of family groups of wild boar Sus scrofa influenced by drive hunts in Lower Saxony, Germany. Wildlife Biology 9, 43-49

To_go_2010

Toïgo, C., Servanty, S., Gaillard, J. M., Brandt, S., & Baubet, É. (2010). Mortalité turelle et mortalité liée à la chasse: le cas du sanglier. Faune sauvage, 288, 19-22

Indices and tables