Reproduction of wild boar in a cropland and coastal wetland area: implications for management

Rosell, Carme; Navas, Fátima; Romero, Sergi

doi:10.32800/abc.2012.35.0209

Cited by 44 publications

(21 citation statements)

References 33 publications

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“…Fixed parameters included longevity (a data-based distribution), litter size (6), age at reproductive maturity (180 days), minimum time between conception and farrowing (90 days), gestation time (115 days), age of natal dispersal (∼Poisson(13 months) truncated between 10-24 months), dispersal distance (∼Weibull(2.5,0.5)), maximum size of family groups (10), incubation period for ASFv (∼Poisson(4 days) truncated at 1), infectious period for ASFv (∼Poisson(5 days) truncated at 1), and disease-induced mortality (assumed to be fixed at 100% lethality for infectious individuals). There were also fixed seasonal trends that varied monthly for conception probability and carcass persistence that were based on data [44-47]. For example, seasonal birth pulses influenced group size and dispersal events over time (because dispersal was age-dependent), but we assumed that average daily contact distances were constant throughout the year.…”

Section: Methodsmentioning

confidence: 99%

Social structure defines spatial transmission of African swine fever in wild boar

Pepin

Golnar

Podgórski

2020

Preprint

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191. African swine fever virus (ASFv) is endemic in wild boar in Eastern Europe, challenging 20 elimination in domestic swine. Estimates of the distances between transmission events 21 are crucial for predicting rates of disease spread to guide allocation of surveillance and 22 23 processes in hosts, but effects of these processes on spread are poorly understood, and 24 inferences often include only one process. 25 2. To understand effects of spatial and social processes on disease dynamics we developed 26 spatially-explicit transmission models with different assumptions about social and/or 27 spatial contact processes. We fit the models to ASFv surveillance data from Eastern 28 Poland from 2014-2015 and evaluated how inclusion of social structure affected 29 inference. 30 3. The model that accounted for social along with spatial processes provided better 31 inference of spatial spread and predicted that ~80% of transmission events are within the 32 same family group. 33 4. The models predicted dramatically different effective reproductive numbers, both in 34 magnitude and variation. 35 5. Specifying contact structure with spatial but not social processes can lead to very 36 different disease dynamics and inference of epidemiological parameters. Uncertainty in 37 these processes should be accounted for in predicting spatial spread in social species. 38 39 KEYWORDS: African swine fever, Effective reproduction number, Spatial transmission kernel, 40 Surveillance, Wild boar, 41 42 45 3 disease spread per transmission event and inform how surveillance, containment, or mitigation 46 strategies should be deployed [1]. For example, information on where cases may arise can inform 47 what spatial radius should be used for ring culling, ring vaccination, spatial quarantine or 48 intensive surveillance [2]. Without detailed genetic data or contact tracing data to reconstruct 49 transmission history, STKs are predominantly estimated indirectly by fitting disease transmission 50 models to available case data [1-3] providing valuable insight for developing intervention 51 strategies [4-7]. However, models often make simplifying assumptions based on the available 52 information that could have negative impacts on policy decisions if the models are not robust to 53 violation of these assumptions. Common assumptions in models for estimating STKs include 54 assuming a single introduction event and assuming observation of all transmission events [8]. 55Methods that account for these processes are important for providing more realistic predictions 56 of spatial spread in systems where these assumptions are violated. 57 Another common issue is that the potential scope of contact heterogeneities is often 58 simplified or lacking, such that uncertainty in model specification cannot be considered despite 59 its potential importance [9]. In non-vector-borne disease systems, key drivers of contact 60 heterogeneities include social [10] and spatial processes [2], yet our understanding of the relative 61 role of these processes in ...

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Section: Methodsmentioning

confidence: 99%

Social structure defines spatial transmission of African swine fever in wild boar

Pepin

Golnar

Podgórski

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Fixed parameters included longevity (a data-based distribution), litter size ( 6), age at reproductive maturity (180 days), the minimum time between conception and farrowing (90 days), gestation time (115 days), age of natal dispersal (∼Poisson(13 months) truncated between 10 and 24 months), dispersal distance (∼Weibull(2.5,0.5)), the maximum size of family groups (10), the incubation period for ASFv (∼Poisson(4 days) truncated at 1), the infectious period for ASFv (∼Poisson(5 days) truncated at 1) and disease-induced mortality (assumed to be fixed at 100% lethality for infectious individuals). There were also fixed seasonal trends that varied monthly for conception probability and carcass persistence that were based on data [45][46][47][48]. For example, seasonal birth pulses influenced group size and dispersal events over time (because dispersal was age-dependent), but we assumed that average daily contact distances were constant throughout the year.…”

Section: Attributes and Demographic Processesmentioning

confidence: 99%

Social structure defines spatial transmission of African swine fever in wild boar

Pepin

Golnar

Podgórski

2021

J. R. Soc. Interface.

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The spatial spread of infectious disease is determined by spatial and social processes such as animal space use and family group structure. Yet, the impacts of social processes on spatial spread remain poorly understood and estimates of spatial transmission kernels (STKs) often exclude social structure. Understanding the impacts of social structure on STKs is important for obtaining robust inferences for policy decisions and optimizing response plans. We fit spatially explicit transmission models with different assumptions about contact structure to African swine fever virus surveillance data from eastern Poland from 2014 to 2015 and evaluated how social structure affected inference of STKs and spatial spread. The model with social structure provided better inference of spatial spread, predicted that approximately 80% of transmission events occurred within family groups, and that transmission was weakly female-biased (other models predicted weakly male-biased transmission). In all models, most transmission events were within 1.5 km, with some rare events at longer distances. Effective reproductive numbers were between 1.1 and 2.5 (maximum values between 4 and 8). Social structure can modify spatial transmission dynamics. Accounting for this additional contact heterogeneity in spatial transmission models could provide more robust inferences of STKs for policy decisions, identify best control targets and improve transparency in model uncertainty.

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“…Other drivers include the mating season, when sexual contacts take place and aggressive interactions among males may become more frequent, too. In most of Europe, the main wild boar mating season takes place from October to January (Rosell, ), roughly coinciding with the main hunting season. Although the farrowing season may peak in spring, births (and hence mating) can take place in any time of the year (Albrycht et al., ), particularly if supplementary feeding is provided.…”

Section: Assessmentmentioning

confidence: 99%

Epidemiological analyses of African swine fever in the European Union (November 2017 until November 2018)

Boklund¹,

Cay²,

Depner³

et al. 2018

EFS2

122

123

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This update on the African swine fever (ASF) outbreaks in the EU demonstrated that out of all tested wild boar found dead, the proportion of positive samples peaked in winter and summer. For domestic pigs only, a summer peak was evident. Despite the existence of several plausible factors that could result in the observed seasonality, there is no evidence to prove causality. Wild boar density was the most influential risk factor for the occurrence of ASF in wild boar. In the vast majority of introductions in domestic pig holdings, direct contact with infected domestic pigs or wild boar was excluded as the route of introduction. The implementation of emergency measures in the wild boar management zones following a focal ASF introduction was evaluated. As a sole control strategy, intensive hunting around the buffer area might not always be sufficient to eradicate ASF. However, the probability of eradication success is increased after adding quick and safe carcass removal. A wider buffer area leads to a higher success probability; however it implies a larger intensive hunting area and the need for more animals to be hunted. If carcass removal and intensive hunting are effectively implemented, fencing is more useful for delineating zones, rather than adding substantially to control efficacy. However, segments of fencing will be particularly useful in those areas where carcass removal or intensive hunting is difficult to implement. It was not possible to demonstrate an effect of natural barriers on ASF spread. Human‐mediated translocation may override any effect of natural barriers. Recommendations for ASF control in four different epidemiological scenarios are presented.

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Reproduction of wild boar in a cropland and coastal wetland area: implications for management

Cited by 44 publications

References 33 publications

Social structure defines spatial transmission of African swine fever in wild boar

Social structure defines spatial transmission of African swine fever in wild boar

Social structure defines spatial transmission of African swine fever in wild boar

Epidemiological analyses of African swine fever in the European Union (November 2017 until November 2018)

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