Monte Carlo simulation of classical swine fever epidemics and control. I. General concepts and description of the model.
ABSTRACT A Monte Carlo simulation has been developed to describe the spread of classical swine fever virus between farms within a certain region. The data of the farms can be imported and considered individually. Transmission occurs via the infection routes direct animal and indirect person and vehicle contact, as well as by contaminated sperm and local spread. Parameters, such as incubation period and probability of detection, can be varied by the user and their impact on disease spread can be studied. The control measures stamping-out, movement control and pre-emptive slaughter in circular restriction areas as well as contact tracing can be applied and their effect on disease spread can thus be analysed. The numbers of culled and restricted farms and animals per epidemic and per day within an epidemic, the epidemic duration and the total length of restrictions per restricted farm are given. In an example, simulation runs were performed under the condition of application of all four-control measures. Because no real farm data were available, a test area was generated stochastically with a farm density of 1.3 farms/km(2). The distributions of the number of infected farms per epidemic and the epidemic length are shown.
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ABSTRACT: Classical swine fever (CSF) is a highly contagious viral disease of pigs and wild boars that causes severe commercial restrictions to the affected countries. The knowledge of spread patterns and risk factors that are involved in the transmission of CSF would help to implement specific measures and to reduce the disease spread in future outbreaks. In this article, we introduce a new spatial hybrid model developed for the spread of CSF. It is based on the combination of a stochastic individual Based model with a Susceptible-Infected model. The coefficients and parameters of the models are estimated using real data.Proceedings of the XXI CEDYA, 07/2009: pages 1-8; , ISBN: 978-84-692-6473-7
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ABSTRACT: Wild birds are the primary source of genetic diversity for influenza A viruses that eventually emerge in poultry and humans. Much progress has been made in the descriptive ecology of avian influenza viruses (AIVs), but contributions are less evident from quantitative studies (e.g. those including disease dynamic models). Transmission between host species, individuals and flocks has not been measured with sufficient accuracy to allow robust quantitative evaluation of alternate control protocols. We focused on the United States of America (USA) as a case study for determining the state of our quantitative knowledge of potential AIV emergence processes from wild hosts to poultry. We identified priorities for quantitative research that would build on existing tools for responding to AIV in poultry and concluded that the following knowledge gaps can be addressed with current empirical data: 1) quantification of the spatio-temporal relationships between AIV prevalence in wild hosts and poultry populations, 2) understanding how the structure of different poultry sectors impacts within-flock transmission, 3) determining mechanisms and rates of between-farm spread, and 4) validating current policy-decision tools with data. The modeling studies we recommend will improve our mechanistic understanding of potential AIV transmission patterns in USA poultry, leading to improved measures of accuracy and reduced uncertainty when evaluating alternative control strategies.Preventive Veterinary Medicine 03/2014; 113(4):376-397. · 2.39 Impact Factor
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ABSTRACT: Classical Swine Fever is a viral disease of pigs that causes severe restrictions on the movement of pigs and pig products in the affected areas. The knowledge of its spread patterns and risk factors would help to implement specific measures for controlling future outbreaks. In this article, we describe in detail a spatial hybrid model, called Be-FAST, based on the combination of a stochastic Individual-Based model (modeling the interactions between the farms, considered as individuals) for between-farm spread with a Susceptible-Infected model for within-farm spread, to simulate the spread of this disease and identify risk zones in a given region. First, we focus on the mathematical formulation of each component of the model. Then, in order to validate Be-FAST, we perform various numerical experiments considering the Spanish province of Segovia. Obtained results are compared with the ones given by two other Individual-Based models and real outbreaks data from Segovia and The Netherlands.Annals of Operations Research 08/2012; · 1.03 Impact Factor