Mapping the likelihood of introduction and spread of HPAI in Africa and Indonesia using Multicriteria Decision Modelling

Spatial analysis of the distribution of disease risk and its visual presentation through risk maps can be used to inform the design of animal disease surveillance resulting in more cost-effective strategies. It is suitable for application in the context of HPAI H5N1 in Africa and Indonesia where the...

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Autores principales: Stevens, Kim, de Glanville, Will, Costard, Solenne, Métras, Raphaëlle, Theuri, Wachira, Kruska, Russ, Randolph, Thomas F., Grace, Delia, Hendrickx, Saskia, Pfeiffer, Dirk
Formato: Brief
Lenguaje:Inglés
Publicado: International Food Policy Research Institute 2008
Materias:
Acceso en línea:https://hdl.handle.net/10568/161676
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author Stevens, Kim
de Glanville, Will
Costard, Solenne
Métras, Raphaëlle
Theuri, Wachira
Kruska, Russ
Randolph, Thomas F.
Grace, Delia
Hendrickx, Saskia
Pfeiffer, Dirk
author_browse Costard, Solenne
Grace, Delia
Hendrickx, Saskia
Kruska, Russ
Métras, Raphaëlle
Pfeiffer, Dirk
Randolph, Thomas F.
Stevens, Kim
Theuri, Wachira
de Glanville, Will
author_facet Stevens, Kim
de Glanville, Will
Costard, Solenne
Métras, Raphaëlle
Theuri, Wachira
Kruska, Russ
Randolph, Thomas F.
Grace, Delia
Hendrickx, Saskia
Pfeiffer, Dirk
author_sort Stevens, Kim
collection Repository of Agricultural Research Outputs (CGSpace)
description Spatial analysis of the distribution of disease risk and its visual presentation through risk maps can be used to inform the design of animal disease surveillance resulting in more cost-effective strategies. It is suitable for application in the context of HPAI H5N1 in Africa and Indonesia where the disease has already been introduced and is endemic in some areas.; Two main approaches can be used to produce risk maps:; A data-driven approach, which uses actual disease data to identify risk factors that allow the absolute risk of disease occurrence in an area to be determined;; A knowledge-driven approach, which uses knowledge about the epidemiology of the disease to identify areas at higher or lower risk of disease occurrence relative to the surrounding areas.; Both approaches are based on available evidence. However, when empirical data about the distribution of the disease are not readily available or when data are only available on some aspects of the epidemiology of a multi-factorial disease, knowledge-driven approaches can be used to determine those areas in which a specific disease is most likely to occur using models such as multicriteria decision modelling (MCDM) (Clements et al. 2006, Pfeiffer et al. 2008).; In contrast to data-driven modelling, MCDM does not generate estimates of absolute risk. Instead, MCDM generates maps that identify areas with a higher or lower likelihood of an event of interest occurring relative to surrounding areas on the same map. A study described in more detail in EDRS-AIA risk mapping documents (2009) was conducted using an MCDM approach to describe the spatial variation in the likelihood of: introduction and spread of highly pathogenic avian influenza virus HPAI H5N1 in Africa, and spread of HPAI H5N1 in Indonesia.; This brief summarizes the methodology used to produce the maps for continental Africa and Indonesia, and the findings. In addition to the three maps in this brief, maps for other African countries were produced and are presented in the report, Mapping the Likelihood of Introduction and Spread of Highly Pathogenic Avian Influenza Virus H5N1 in Africa, Ghana, Ethiopia, Kenya and Nigeria using Multicriteria Decision Modelling (Stevens et al. 2009); and maps for Indonesia can be found in Mapping the Risk of Spread of Highly Pathogenic Avian Influenza H5N1 in Indonesia using Multicriteria Decision Modelling (de Glanville et al. 2009). These reports also include a more detailed description of the methodology used to produce the maps.
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spelling CGSpace1616762025-11-06T04:46:58Z Mapping the likelihood of introduction and spread of HPAI in Africa and Indonesia using Multicriteria Decision Modelling Stevens, Kim de Glanville, Will Costard, Solenne Métras, Raphaëlle Theuri, Wachira Kruska, Russ Randolph, Thomas F. Grace, Delia Hendrickx, Saskia Pfeiffer, Dirk avian influenza developing countries diseases risk vulnerability Spatial analysis of the distribution of disease risk and its visual presentation through risk maps can be used to inform the design of animal disease surveillance resulting in more cost-effective strategies. It is suitable for application in the context of HPAI H5N1 in Africa and Indonesia where the disease has already been introduced and is endemic in some areas.; Two main approaches can be used to produce risk maps:; A data-driven approach, which uses actual disease data to identify risk factors that allow the absolute risk of disease occurrence in an area to be determined;; A knowledge-driven approach, which uses knowledge about the epidemiology of the disease to identify areas at higher or lower risk of disease occurrence relative to the surrounding areas.; Both approaches are based on available evidence. However, when empirical data about the distribution of the disease are not readily available or when data are only available on some aspects of the epidemiology of a multi-factorial disease, knowledge-driven approaches can be used to determine those areas in which a specific disease is most likely to occur using models such as multicriteria decision modelling (MCDM) (Clements et al. 2006, Pfeiffer et al. 2008).; In contrast to data-driven modelling, MCDM does not generate estimates of absolute risk. Instead, MCDM generates maps that identify areas with a higher or lower likelihood of an event of interest occurring relative to surrounding areas on the same map. A study described in more detail in EDRS-AIA risk mapping documents (2009) was conducted using an MCDM approach to describe the spatial variation in the likelihood of: introduction and spread of highly pathogenic avian influenza virus HPAI H5N1 in Africa, and spread of HPAI H5N1 in Indonesia.; This brief summarizes the methodology used to produce the maps for continental Africa and Indonesia, and the findings. In addition to the three maps in this brief, maps for other African countries were produced and are presented in the report, Mapping the Likelihood of Introduction and Spread of Highly Pathogenic Avian Influenza Virus H5N1 in Africa, Ghana, Ethiopia, Kenya and Nigeria using Multicriteria Decision Modelling (Stevens et al. 2009); and maps for Indonesia can be found in Mapping the Risk of Spread of Highly Pathogenic Avian Influenza H5N1 in Indonesia using Multicriteria Decision Modelling (de Glanville et al. 2009). These reports also include a more detailed description of the methodology used to produce the maps. 2008 2024-11-21T09:57:15Z 2024-11-21T09:57:15Z Brief https://hdl.handle.net/10568/161676 en Open Access application/pdf International Food Policy Research Institute International Livestock Research Institute Royal Veterinary College Stevens, Kim; de Glanville, Will; Costard, Solenne; Métras, Raphaëlle; Theuri, Wachira; Kruska, Russ; Randolph, Thomas F.; Grace, Delia; Hendrickx, Saskia; Pfeiffer, Dirk. 2008. Mapping the likelihood of introduction and spread of HPAI in Africa and Indonesia using Multicriteria Decision Modelling. Controlling avian flu and protecting people’s livelihoods in Africa and Indonesia; HPAI Research Brief 7. https://hdl.handle.net/10568/161676
spellingShingle avian influenza
developing countries
diseases
risk
vulnerability
Stevens, Kim
de Glanville, Will
Costard, Solenne
Métras, Raphaëlle
Theuri, Wachira
Kruska, Russ
Randolph, Thomas F.
Grace, Delia
Hendrickx, Saskia
Pfeiffer, Dirk
Mapping the likelihood of introduction and spread of HPAI in Africa and Indonesia using Multicriteria Decision Modelling
title Mapping the likelihood of introduction and spread of HPAI in Africa and Indonesia using Multicriteria Decision Modelling
title_full Mapping the likelihood of introduction and spread of HPAI in Africa and Indonesia using Multicriteria Decision Modelling
title_fullStr Mapping the likelihood of introduction and spread of HPAI in Africa and Indonesia using Multicriteria Decision Modelling
title_full_unstemmed Mapping the likelihood of introduction and spread of HPAI in Africa and Indonesia using Multicriteria Decision Modelling
title_short Mapping the likelihood of introduction and spread of HPAI in Africa and Indonesia using Multicriteria Decision Modelling
title_sort mapping the likelihood of introduction and spread of hpai in africa and indonesia using multicriteria decision modelling
topic avian influenza
developing countries
diseases
risk
vulnerability
url https://hdl.handle.net/10568/161676
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