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.
- swifco_rs
- swifco_rs module
- swifco_rs.ageing module
- swifco_rs.analysis module
- swifco_rs.asf module
- swifco_rs.asf.disease_course module
- swifco_rs.asf.infection module
- swifco_rs.asf.mutation module
- swifco_rs.asf.release module
- swifco_rs.callback module
- swifco_rs.carcasses module
- swifco_rs.csf module
- swifco_rs.csf.disease_course module
- swifco_rs.csf.infection module
- swifco_rs.csf.release module
- swifco_rs.dispersal module
- swifco_rs.init_map module
- swifco_rs.init_pop module
- swifco_rs.inputs module
- swifco_rs.management module
- swifco_rs.mortality module
- swifco_rs.observers module
- swifco_rs.observers.asf module
- swifco_rs.observers.csf module
- swifco_rs.observers.dispersal module
- swifco_rs.observers.management module
- swifco_rs.observers.map module
- swifco_rs.observers.population module
- swifco_rs.reporters module
- swifco_rs.reporters.events module
- swifco_rs.reporters.grid module
- swifco_rs.reporters.grid.rgb_grid module
- swifco_rs.reporters.time_series module
- swifco_rs.reproduction module
- swifco_rs.terminate module
- swifco_rs.utils module
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