The Experts below are selected from a list of 108 Experts worldwide ranked by ideXlab platform

José Manuel Sánchez-vizcaíno - One of the best experts on this subject based on the ideXlab platform.

  • Identifying areas for Infectious Animal Disease surveillance in the absence of population data: highly pathogenic avian influenza in wild bird populations of Europe.
    Preventive veterinary medicine, 2010
    Co-Authors: I. Iglesias, Andres M Perez, A. De La Torre, M. J. Muñoz, M Martínez, José Manuel Sánchez-vizcaíno
    Abstract:

    A large number (n=591) of H5N1 highly pathogenic avian influenza virus (HPAIV) outbreaks have been reported in wild birds of Europe from October 2005 through January 2009. Consequently, prevention and control strategies have been implemented in response to the outbreaks and considerable discussion has taken place regarding the need for implementing surveillance programs in high-risk areas with the objective of early detecting and preventing HPAIV epidemics. However countries ability to define the temporal and spatial extension of the high risk areas has been impaired by the lack of information on the distribution of susceptible wild bird populations in the region. Here, a technique for the detection of time-space Disease clustering that does not require information on the distribution of susceptible populations and that has been referred to as the time-space permutation model of the scan statistic was used to identify areas and times of the year in which epidemics of H5N1 HPAIV were most likely to occur in wild bird populations of Europe from October, 2005, through December, 2008. The scan statistic was parameterized considering pre-existing knowledge on the epidemiological and ecological characteristics of the Disease in the region. Robustness of the results was assessed using a generalized linear regression model to compare the outcomes of 36 alternative parameterizations of the scan statistic. Ten significant time-space clusters of H5N1 HPAI outbreaks were detected in six European countries. Results were sensitive (P

  • Identifying areas for Infectious Animal Disease surveillance in the absence of population data: highly pathogenic avian influenza in wild bird populations of Europe.
    Preventive Veterinary Medicine, 2010
    Co-Authors: I. Iglesias, Andres M Perez, A. De La Torre, M. J. Muñoz, María Aránzazu Martínez, José Manuel Sánchez-vizcaíno
    Abstract:

    A large number (n = 591) of H5N1 highly pathogenic avian influenza virus (HPAIV) outbreaks have been reported in wild birds of Europe from October 2005 through January 2009. Consequently, prevention and control strategies have been implemented in response to the outbreaks and considerable discussion has taken place regarding the need for implementing surveillance programs in high-risk areas with the objective of early detecting and preventing HPAIV epidemics. However countries ability to define the temporal and spatial extension of the high risk areas has been impaired by the lack of information on the distribution of susceptible wild bird populations in the region. Here, a technique for the detection of time–space Disease clustering that does not require information on the distribution of susceptible populations and that has been referred to as the time–space permutation model of the scan statistic was used to identify areas and times of the year in which epidemics of H5N1 HPAIV were most likely to occur in wild bird populations of Europe from October, 2005, through December, 2008. The scan statistic was parameterized considering pre-existing knowledge on the epidemiological and ecological characteristics of the Disease in the region. Robustness of the results was assessed using a generalized linear regression model to compare the outcomes of 36 alternative parameterizations of the scan statistic. Ten significant time–space clusters of H5N1 HPAI outbreaks were detected in six European countries. Results were sensitive (P < 0.05) to the definition of the maximum spatial size defined for the clusters. Results presented here will help to identify high risk areas for HPAIV surveillance in the European Union. Assumptions, results, and implications of the analytical model are extensively presented and discussed in order to facilitate the use of this approach for the identification of high risk areas for Infectious Animal Disease surveillance in the absence of population data.

Ismaïla Seck - One of the best experts on this subject based on the ideXlab platform.

  • Using a participatory qualitative risk assessment to estimate the risk of introduction and spread of transboundary Animal Diseases in scarce‐data environments
    Transboundary and emerging diseases, 2020
    Co-Authors: Cécile Squarzoni‐diaw, Elena Arsevska, Sana Kalthoum, Pachka Hammami, Jamel Cherni, Assia Daoudi, Mohamed Karim Laoufi, Yassir Lezaar, Kechna Rachid, Ismaïla Seck
    Abstract:

    This article presents a participative and iterative qualitative risk assessment framework that can be used to evaluate the spatial variation of the risk of Infectious Animal Disease introduction and spread on a national scale. The framework was developed through regional training action workshops and field activities. The active involvement of national Animal health services enabled the identification, collection and hierarchization of risk factors. Quantitative data were collected in the field, and expert knowledge was integrated to adjust the available data at regional level. Experts categorized and combined the risk factors into ordinal levels of risk per epidemiological unit to ease implementation of risk-based surveillance in the field. The framework was used to perform a qualitative assessment of the risk of introduction and spread of foot-and-mouth Disease (FMD) in Tunisia as part of a series of workshops held between 2015 and 2018. The experts in attendance combined risk factors such as epidemiological status, transboundary movements, proximity to the borders and accessibility to assess the risk of FMD outbreaks in Tunisia. Out of the 2,075 Tunisian imadas, 23 were at a very high risk of FMD introduction, mainly at the borders; and 59 were at a very high risk of FMD spread. To validate the model, the results were compared to the FMD outbreaks notified by Tunisia during the 2014 FMD epizootic. Using a spatial Poisson model, a significant alignment between the very high and high-risk categories of spread and the occurrence of FMD outbreaks was shown. The relative risk of FMD occurrence was thus 3.2 higher for imadas in the very high and high spread risk categories than for imadas in the low and negligible spread risk categories. Our results show that the qualitative risk assessment framework can be a useful decision support tool for risk-based Disease surveillance and control, in particular in scarce-data environments.

I. Iglesias - One of the best experts on this subject based on the ideXlab platform.

  • Identifying areas for Infectious Animal Disease surveillance in the absence of population data: highly pathogenic avian influenza in wild bird populations of Europe.
    Preventive veterinary medicine, 2010
    Co-Authors: I. Iglesias, Andres M Perez, A. De La Torre, M. J. Muñoz, M Martínez, José Manuel Sánchez-vizcaíno
    Abstract:

    A large number (n=591) of H5N1 highly pathogenic avian influenza virus (HPAIV) outbreaks have been reported in wild birds of Europe from October 2005 through January 2009. Consequently, prevention and control strategies have been implemented in response to the outbreaks and considerable discussion has taken place regarding the need for implementing surveillance programs in high-risk areas with the objective of early detecting and preventing HPAIV epidemics. However countries ability to define the temporal and spatial extension of the high risk areas has been impaired by the lack of information on the distribution of susceptible wild bird populations in the region. Here, a technique for the detection of time-space Disease clustering that does not require information on the distribution of susceptible populations and that has been referred to as the time-space permutation model of the scan statistic was used to identify areas and times of the year in which epidemics of H5N1 HPAIV were most likely to occur in wild bird populations of Europe from October, 2005, through December, 2008. The scan statistic was parameterized considering pre-existing knowledge on the epidemiological and ecological characteristics of the Disease in the region. Robustness of the results was assessed using a generalized linear regression model to compare the outcomes of 36 alternative parameterizations of the scan statistic. Ten significant time-space clusters of H5N1 HPAI outbreaks were detected in six European countries. Results were sensitive (P

  • Identifying areas for Infectious Animal Disease surveillance in the absence of population data: highly pathogenic avian influenza in wild bird populations of Europe.
    Preventive Veterinary Medicine, 2010
    Co-Authors: I. Iglesias, Andres M Perez, A. De La Torre, M. J. Muñoz, María Aránzazu Martínez, José Manuel Sánchez-vizcaíno
    Abstract:

    A large number (n = 591) of H5N1 highly pathogenic avian influenza virus (HPAIV) outbreaks have been reported in wild birds of Europe from October 2005 through January 2009. Consequently, prevention and control strategies have been implemented in response to the outbreaks and considerable discussion has taken place regarding the need for implementing surveillance programs in high-risk areas with the objective of early detecting and preventing HPAIV epidemics. However countries ability to define the temporal and spatial extension of the high risk areas has been impaired by the lack of information on the distribution of susceptible wild bird populations in the region. Here, a technique for the detection of time–space Disease clustering that does not require information on the distribution of susceptible populations and that has been referred to as the time–space permutation model of the scan statistic was used to identify areas and times of the year in which epidemics of H5N1 HPAIV were most likely to occur in wild bird populations of Europe from October, 2005, through December, 2008. The scan statistic was parameterized considering pre-existing knowledge on the epidemiological and ecological characteristics of the Disease in the region. Robustness of the results was assessed using a generalized linear regression model to compare the outcomes of 36 alternative parameterizations of the scan statistic. Ten significant time–space clusters of H5N1 HPAI outbreaks were detected in six European countries. Results were sensitive (P < 0.05) to the definition of the maximum spatial size defined for the clusters. Results presented here will help to identify high risk areas for HPAIV surveillance in the European Union. Assumptions, results, and implications of the analytical model are extensively presented and discussed in order to facilitate the use of this approach for the identification of high risk areas for Infectious Animal Disease surveillance in the absence of population data.

Andres M Perez - One of the best experts on this subject based on the ideXlab platform.

  • A Review of Quantitative Tools Used to Assess the Epidemiology of Porcine Reproductive and Respiratory Syndrome in U.S. Swine Farms Using Dr. Morrison's Swine Health Monitoring Program Data.
    Frontiers in veterinary science, 2017
    Co-Authors: Carles Vilalta, Andreia G. Arruda, Steven J. P. Tousignant, Pablo Valdes-donoso, Petra Muellner, Ulrich Muellner, Moh A. Alkhamis, Robert B. Morrison, Andres M Perez
    Abstract:

    Porcine Reproductive and Respiratory Syndrome (PRRS) causes far-reaching financial losses to infected countries and regions, including the U.S. The Swine Health Monitoring Project (SHMP) is a voluntary program in which producers and veterinarians share sow farm PRRS status weekly to contribute to the understanding, in quantitative terms, of PRRS epidemiological dynamics and, ultimately, to support its control in the U.S. Here, we offer a review of a variety of analytic tools that were applied to SHMP-data to assess Disease dynamics in quantitative terms to support the decision-making process for veterinarians and producers. Use of those methods has helped the U.S. swine industry to quantify the cyclical patterns of PRRS, to describe the impact that emerging pathogens has had on that pattern, to identify the nature and extent at which environmental factors (e.g. precipitation or land cover) influence PRRS risk, to identify PRRSv emerging strains, and to assess the influence that voluntary reporting has on Disease control. Results from the numerous studies reviewed here provide important insights into PRRS epidemiology that help to create the foundations for a near real-time prediction of Disease risk, and, ultimately, will contribute to support the prevention and control of, arguably, one of the most devastating Diseases affecting the North American swine industry. The review also demonstrates how different approaches to analyze and visualize the data may help to add value to the routine collection of surveillance data and support Infectious Animal Disease control.

  • Identifying areas for Infectious Animal Disease surveillance in the absence of population data: highly pathogenic avian influenza in wild bird populations of Europe.
    Preventive veterinary medicine, 2010
    Co-Authors: I. Iglesias, Andres M Perez, A. De La Torre, M. J. Muñoz, M Martínez, José Manuel Sánchez-vizcaíno
    Abstract:

    A large number (n=591) of H5N1 highly pathogenic avian influenza virus (HPAIV) outbreaks have been reported in wild birds of Europe from October 2005 through January 2009. Consequently, prevention and control strategies have been implemented in response to the outbreaks and considerable discussion has taken place regarding the need for implementing surveillance programs in high-risk areas with the objective of early detecting and preventing HPAIV epidemics. However countries ability to define the temporal and spatial extension of the high risk areas has been impaired by the lack of information on the distribution of susceptible wild bird populations in the region. Here, a technique for the detection of time-space Disease clustering that does not require information on the distribution of susceptible populations and that has been referred to as the time-space permutation model of the scan statistic was used to identify areas and times of the year in which epidemics of H5N1 HPAIV were most likely to occur in wild bird populations of Europe from October, 2005, through December, 2008. The scan statistic was parameterized considering pre-existing knowledge on the epidemiological and ecological characteristics of the Disease in the region. Robustness of the results was assessed using a generalized linear regression model to compare the outcomes of 36 alternative parameterizations of the scan statistic. Ten significant time-space clusters of H5N1 HPAI outbreaks were detected in six European countries. Results were sensitive (P

  • Identifying areas for Infectious Animal Disease surveillance in the absence of population data: highly pathogenic avian influenza in wild bird populations of Europe.
    Preventive Veterinary Medicine, 2010
    Co-Authors: I. Iglesias, Andres M Perez, A. De La Torre, M. J. Muñoz, María Aránzazu Martínez, José Manuel Sánchez-vizcaíno
    Abstract:

    A large number (n = 591) of H5N1 highly pathogenic avian influenza virus (HPAIV) outbreaks have been reported in wild birds of Europe from October 2005 through January 2009. Consequently, prevention and control strategies have been implemented in response to the outbreaks and considerable discussion has taken place regarding the need for implementing surveillance programs in high-risk areas with the objective of early detecting and preventing HPAIV epidemics. However countries ability to define the temporal and spatial extension of the high risk areas has been impaired by the lack of information on the distribution of susceptible wild bird populations in the region. Here, a technique for the detection of time–space Disease clustering that does not require information on the distribution of susceptible populations and that has been referred to as the time–space permutation model of the scan statistic was used to identify areas and times of the year in which epidemics of H5N1 HPAIV were most likely to occur in wild bird populations of Europe from October, 2005, through December, 2008. The scan statistic was parameterized considering pre-existing knowledge on the epidemiological and ecological characteristics of the Disease in the region. Robustness of the results was assessed using a generalized linear regression model to compare the outcomes of 36 alternative parameterizations of the scan statistic. Ten significant time–space clusters of H5N1 HPAI outbreaks were detected in six European countries. Results were sensitive (P < 0.05) to the definition of the maximum spatial size defined for the clusters. Results presented here will help to identify high risk areas for HPAIV surveillance in the European Union. Assumptions, results, and implications of the analytical model are extensively presented and discussed in order to facilitate the use of this approach for the identification of high risk areas for Infectious Animal Disease surveillance in the absence of population data.

Cécile Squarzoni‐diaw - One of the best experts on this subject based on the ideXlab platform.

  • Using a participatory qualitative risk assessment to estimate the risk of introduction and spread of transboundary Animal Diseases in scarce‐data environments
    Transboundary and emerging diseases, 2020
    Co-Authors: Cécile Squarzoni‐diaw, Elena Arsevska, Sana Kalthoum, Pachka Hammami, Jamel Cherni, Assia Daoudi, Mohamed Karim Laoufi, Yassir Lezaar, Kechna Rachid, Ismaïla Seck
    Abstract:

    This article presents a participative and iterative qualitative risk assessment framework that can be used to evaluate the spatial variation of the risk of Infectious Animal Disease introduction and spread on a national scale. The framework was developed through regional training action workshops and field activities. The active involvement of national Animal health services enabled the identification, collection and hierarchization of risk factors. Quantitative data were collected in the field, and expert knowledge was integrated to adjust the available data at regional level. Experts categorized and combined the risk factors into ordinal levels of risk per epidemiological unit to ease implementation of risk-based surveillance in the field. The framework was used to perform a qualitative assessment of the risk of introduction and spread of foot-and-mouth Disease (FMD) in Tunisia as part of a series of workshops held between 2015 and 2018. The experts in attendance combined risk factors such as epidemiological status, transboundary movements, proximity to the borders and accessibility to assess the risk of FMD outbreaks in Tunisia. Out of the 2,075 Tunisian imadas, 23 were at a very high risk of FMD introduction, mainly at the borders; and 59 were at a very high risk of FMD spread. To validate the model, the results were compared to the FMD outbreaks notified by Tunisia during the 2014 FMD epizootic. Using a spatial Poisson model, a significant alignment between the very high and high-risk categories of spread and the occurrence of FMD outbreaks was shown. The relative risk of FMD occurrence was thus 3.2 higher for imadas in the very high and high spread risk categories than for imadas in the low and negligible spread risk categories. Our results show that the qualitative risk assessment framework can be a useful decision support tool for risk-based Disease surveillance and control, in particular in scarce-data environments.