A novel spatial and stochastic model to evaluate the within and between farm transmission of classical swine fever virus: II Validation of the model

Martínez‐López, Beatriz; Ivorra, Benjamín; Ngom, Daouda; Ramos, Ángel; Sánchez-Vizcaíno, José Manuel

doi:10.1016/j.vetmic.2011.08.008

Cited by 16 publications

(11 citation statements)

References 20 publications

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“…This work aimed to determine and describe ASFV infection dynamics in Sardinia using a combination of two different methodologies: disease spread models and regression methods. The disease spread model employed here (Be‐FAST) was previously used in other scenarios (Alkhamis et al., ; Ivorra et al., ; Martínez‐López et al., , , , ) for estimating the impact of foreign animal disease outbreaks in disease free territories. In this study, we applied the parameters previously used to simulate ASF in Bulgaria (Alkhamis et al., ) because there are strong similarities between pig production systems in Bulgaria and Sardinia.…”

Section: Discussionmentioning

confidence: 99%

“…A spatially explicit individual‐based stochastic spread model developed by our team called Be‐FAST (Between‐Farm‐Animal Spatial Transmission) was used for studying the transmission of ASFV in Sardinia. Be‐FAST has been fully described and validated (Ivorra, Martínez‐López, Sánchez‐Vizcaíno, & Ramos, ; Martínez‐López, Ivorra, Ngom, Ramos, & Sánchez‐Vizcaíno, and Martínez‐López, Ivorra, Ramos, & Sánchez‐Vizcaíno, ) and adapted to a wide range of epidemiological frameworks in order to model livestock diseases such as classical swine fever and foot‐and‐mouth disease (i.e., Martínez‐López et al., , ). Be‐FAST has been adapted to simulate ASFV spread in countries where backyard production is predominant, such as Bulgaria (Alkhamis et al., ).…”

Section: Methodsmentioning

confidence: 99%

“…As the MLRM presented the highest AUC-ROC, it was the model chosen to generate the risk maps for ASF occurrence in Sardinia (Alkhamis et al, 2014;Ivorra et al, 2014;Mart ınez-L opez et al, 2011Mart ınez-L opez et al, , 2012Mart ınez-L opez et al, , 2013Mart ınez-L opez et al, , 2014 for estimating the impact of foreign animal disease outbreaks in disease free territories. In this study, we applied the parameters previously used to simulate ASF in Bulgaria (Alkhamis et al, 2014) controversial because common sense would suggest that the approach of farmers towards the disease would vary with the type of pig farm (e.g., industrial versus backyard).…”

Section: Risk Maps Of Asf Occurrencementioning

confidence: 99%

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Understanding African Swine Fever infection dynamics in Sardinia using a spatially explicit transmission model in domestic pig farms

Mur

Sánchez-Vizcaı́no

Fernández-Carrión

et al. 2017

Transbound Emerg Dis

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SummaryAfrican swine fever virus (ASFV) has been endemic in Sardinia since 1978, resulting in severe losses for local pig producers and creating important problems for the island's veterinary authorities. This study used a spatially explicit stochastic transmission model followed by two regression models to investigate the dynamics of ASFV spread amongst domestic pig farms, to identify geographic areas at highest risk and determine the role of different susceptible pig populations (registered domestic pigs, non-registered domestic pigs [brado] and wild boar) in ASF occurrence. We simulated transmission within and between farms using an adapted version of the previously described model known as Be-FAST. Results from the model revealed a generally low diffusion of ASF in Sardinia, with only 24% of the simulations resulting in disease spread, and for each simulated outbreak on average only four farms and 66 pigs were affected. Overall, local spread (indirect transmission between farms within a 2 km radius through fomites) was the most common route of transmission, being responsible for 98.6% of secondary cases. The risk of ASF occurrence for each domestic pig farm was estimated from the spread model results and integrated in two regression models together with available data for brado and wild boar populations. There was a significant association between the density of all three populations (domestic pigs, brado, and wild boar) and ASF occurrence in Sardinia. The most significant risk factors were the high densities of brado (OR = 2.2) and wild boar (OR = 2.1). The results of both analyses demonstrated that ASF epidemiology and infection dynamics in Sardinia create a complex and multifactorial disease situation, where all susceptible populations play an important role. To stop ASF transmission in Sardinia, three main factors (improving biosecurity on domestic pig farms, eliminating brado practices and better management of wild boars) need to be addressed.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Risk Maps Of Asf Occurrencementioning

confidence: 99%

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Understanding African Swine Fever infection dynamics in Sardinia using a spatially explicit transmission model in domestic pig farms

Mur

Sánchez-Vizcaı́no

Fernández-Carrión

et al. 2017

Transbound Emerg Dis

Self Cite

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“…This is reasonable if, e.g., for contingency planning purposes the landscape scale is of interest in the modeling and individual-based representation may overcharge technical capacities. Moreover, some of the existing model environments (e.g., NAADSM; InterSpread Plus; DTU-DADS; Be-FAST) do not allow integration of individual-based within-herd simulations in their current version, although these model environments are applied in decision support ( 13 , 18 , 20 – 23 ). On the other hand, some herd-level model environments (e.g., InterSpread Plus and NAADSM) use pre-defined cumulative probability functions to represent disease progress within the animal unit.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Spread of African Swine Fever, Including the Effects of Residues from Dead Animals

et al. 2016

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To study the spread of African swine fever (ASF) within a pig unit and the impact of unit size on ASF spread, a simulation model was created. In the model, an animal can be in one of the following stages: susceptible, latent, subclinical, clinical, or recovered. Animals can be infectious during the subclinical stage and are fully infectious during the clinical stage. ASF virus (ASFV) infection through residues of dead animals in the slurries was also modeled in an exponentially fading-out pattern. Low and high transmission rates for ASFV were tested in the model. Robustness analysis was carried out in order to study the impact of uncertain parameters on model predictions. The results showed that the disease may fade out within the pig unit without a major outbreak. Furthermore, they showed that spread of ASFV is dependent on the infectiousness of subclinical animals and the residues of dead animals, the transmission rate of the virus, and importantly the unit size. Moreover, increasing the duration of the latent or the subclinical stages resulted in longer time to disease fade out. The proposed model is a simple and robust tool simulating the spread of ASFV within a pig house taking into account dynamics of ASFV spread and the unit size. The tool can be implemented in simulation models of ASFV spread between herds.

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“…Finally, if historical data are available, it can be used to assess whether the simulation results indicate that the model behaves as the real‐life system does. A companion procedure can consist in comparing the outputs to that from other models (Martínez‐López et al., ).…”

Section: Introductionmentioning

confidence: 99%

A Simple Geometric Validation Approach to Assess the Basic Behaviour of Space- and Time- Distributed Models of Epidemic Spread - An Example Using the Ontario Rabies Model

Ludwig

Berthiaume

Richer

et al. 2013

Transbound Emerg Dis

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Dynamic mathematical modelling and stochastic simulation of disease-host systems for the purpose of epidemiological analysis offer great opportunities for testing hypotheses, especially when field experiments are impractical or when there is a need to evaluate multiple experimental scenarios. This, combined with the ever increasing computer power available to researchers, has contributed to the development of many mathematical models for epidemic simulations, such as the individual-based model (IBM). Nevertheless, few of these models undergo extensive validation and proper assessment of intrinsic variability. The Ontario rabies model (ORM) will be used here to exemplify some advantages of appropriate model behaviour validation and to illustrate the use of a simple geometric procedure for testing directional bias in distributed stochastic dynamic model of spread of diseases. Results were obtained through the comparison of 10 000 epizootics resulting from 100 epidemic simulations started using 100 distinct base populations. The analysis results demonstrated a significant directional bias in epidemic dispersion, which prompted further verification of the model code and the identification of a coding error, which was then corrected. Subsequent testing of the corrected code showed that the directional bias could no longer be detected. These results illustrate the importance of proper validation and the importance of sufficient knowledge of the model behaviour to ensure the results will not confound the objectives of the end-users.

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A novel spatial and stochastic model to evaluate the within and between farm transmission of classical swine fever virus: II Validation of the model

Abstract: A new, recently published, stochastic and spatial model for the evaluation of classical swine fever virus (CSFV) spread into

Cited by 16 publications

References 20 publications

Understanding African Swine Fever infection dynamics in Sardinia using a spatially explicit transmission model in domestic pig farms

Understanding African Swine Fever infection dynamics in Sardinia using a spatially explicit transmission model in domestic pig farms

Simulation of Spread of African Swine Fever, Including the Effects of Residues from Dead Animals

A Simple Geometric Validation Approach to Assess the Basic Behaviour of Space- and Time- Distributed Models of Epidemic Spread - An Example Using the Ontario Rabies Model

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