Mapping the likelihood of introduction and spread of HPAI Virus H5N1 in Indonesia using multicriteria decision modelling
The spatial distribution of disease risk and its visual presentation through risk maps can assist in the design of targeted animal disease surveillance and control strategies. This approach is particularly useful in situations in which empirical data are not readily available (Clements et al 2006),...
| Autores principales: | , , , , |
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| Formato: | Artículo preliminar |
| Lenguaje: | Inglés |
| Publicado: |
International Food Policy Research Institute
2009
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/161918 |
| _version_ | 1855536520192065536 |
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| author | de Glanville, Will Stevens, Kim Costard, Solenne Métras, Raphaëlle Pfeiffer, Dirk |
| author_browse | Costard, Solenne Métras, Raphaëlle Pfeiffer, Dirk Stevens, Kim de Glanville, Will |
| author_facet | de Glanville, Will Stevens, Kim Costard, Solenne Métras, Raphaëlle Pfeiffer, Dirk |
| author_sort | de Glanville, Will |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The spatial distribution of disease risk and its visual presentation through risk maps can assist in the design of targeted animal disease surveillance and control strategies. This approach is particularly useful in situations in which empirical data are not readily available (Clements et al 2006), or when data are only available on some aspects of the epidemiology of a multi-factorial disease (Tachiiri et al 2006). In such circumstances data on known risk factors can be used to determine those areas in which a specific disease is most likely to occur using knowledge driven models, such as multicriteria decision modelling (MCDM) (Pfeiffer et al 2008). MCDM is an example of a static knowledge-driven modelling approach that can be used to produce qualitative or quantitative estimates of risk ‘based on existing or hypothesized understanding of the causal relationships leading to disease occurrence’ (Pfeiffer et al., 2008). Knowledge of the risk factors associated with the occurrence of a disease and their interrelationships are used to drive the model. The objective of this study was to use a multicriteria decision modelling (MCDM) approach to provide a qualitative estimate of the spatial distribution of the risk of spread of highly pathogenic avian influenza virus (HPAIV) subtype H5N1 in Indonesia. MCDM involves the following sequence of analytical steps (Pfeiffer et al 2008): 1. Defining the objective(s) 2. Defining the factors 3. Defining the relationship between each factor and the risk 4. Sourcing digital maps of the factors and constraints 5. Standardising the maps so that they can be compared 6. Defining the relative importance of each factor in relation to the objective 7. Combining all factors and constraints to produce a final weighted estimate of risk for each location in the study area 8. Sensitivity analysis It is important that the user of the outputs of these models is aware of the assumptions made in defining and quantifying the model inputs and any potential sources of information bias when interpreting the results of such analyses. This is especially important with knowledge-based models. This report details the methods used to produce risk maps illustrating the risk of spread of HPAIV in Indonesia. It is intended that the risk map be used to assist disease control and surveillance at the country level in Indonesia, whilst taking into account the limitations of the MCDM methodology. |
| format | Artículo preliminar |
| id | CGSpace161918 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2009 |
| publishDateRange | 2009 |
| publishDateSort | 2009 |
| publisher | International Food Policy Research Institute |
| publisherStr | International Food Policy Research Institute |
| record_format | dspace |
| spelling | CGSpace1619182025-11-06T07:13:46Z Mapping the likelihood of introduction and spread of HPAI Virus H5N1 in Indonesia using multicriteria decision modelling de Glanville, Will Stevens, Kim Costard, Solenne Métras, Raphaëlle Pfeiffer, Dirk avian influenza developing countries decision making risk assessment The spatial distribution of disease risk and its visual presentation through risk maps can assist in the design of targeted animal disease surveillance and control strategies. This approach is particularly useful in situations in which empirical data are not readily available (Clements et al 2006), or when data are only available on some aspects of the epidemiology of a multi-factorial disease (Tachiiri et al 2006). In such circumstances data on known risk factors can be used to determine those areas in which a specific disease is most likely to occur using knowledge driven models, such as multicriteria decision modelling (MCDM) (Pfeiffer et al 2008). MCDM is an example of a static knowledge-driven modelling approach that can be used to produce qualitative or quantitative estimates of risk ‘based on existing or hypothesized understanding of the causal relationships leading to disease occurrence’ (Pfeiffer et al., 2008). Knowledge of the risk factors associated with the occurrence of a disease and their interrelationships are used to drive the model. The objective of this study was to use a multicriteria decision modelling (MCDM) approach to provide a qualitative estimate of the spatial distribution of the risk of spread of highly pathogenic avian influenza virus (HPAIV) subtype H5N1 in Indonesia. MCDM involves the following sequence of analytical steps (Pfeiffer et al 2008): 1. Defining the objective(s) 2. Defining the factors 3. Defining the relationship between each factor and the risk 4. Sourcing digital maps of the factors and constraints 5. Standardising the maps so that they can be compared 6. Defining the relative importance of each factor in relation to the objective 7. Combining all factors and constraints to produce a final weighted estimate of risk for each location in the study area 8. Sensitivity analysis It is important that the user of the outputs of these models is aware of the assumptions made in defining and quantifying the model inputs and any potential sources of information bias when interpreting the results of such analyses. This is especially important with knowledge-based models. This report details the methods used to produce risk maps illustrating the risk of spread of HPAIV in Indonesia. It is intended that the risk map be used to assist disease control and surveillance at the country level in Indonesia, whilst taking into account the limitations of the MCDM methodology. 2009 2024-11-21T09:59:27Z 2024-11-21T09:59:27Z Working Paper https://hdl.handle.net/10568/161918 en Open Access application/pdf International Food Policy Research Institute International Livestock Research Institute Royal Veterinary College de Glanville, Will; Stevens, Kim; Costard, Solenne; Métras, Raphaëlle; Pfeiffer, Dirk. 2009. Mapping the likelihood of introduction and spread of HPAI Virus H5N1 in Indonesia using multicriteria decision modelling. https://hdl.handle.net/10568/161918 |
| spellingShingle | avian influenza developing countries decision making risk assessment de Glanville, Will Stevens, Kim Costard, Solenne Métras, Raphaëlle Pfeiffer, Dirk Mapping the likelihood of introduction and spread of HPAI Virus H5N1 in Indonesia using multicriteria decision modelling |
| title | Mapping the likelihood of introduction and spread of HPAI Virus H5N1 in Indonesia using multicriteria decision modelling |
| title_full | Mapping the likelihood of introduction and spread of HPAI Virus H5N1 in Indonesia using multicriteria decision modelling |
| title_fullStr | Mapping the likelihood of introduction and spread of HPAI Virus H5N1 in Indonesia using multicriteria decision modelling |
| title_full_unstemmed | Mapping the likelihood of introduction and spread of HPAI Virus H5N1 in Indonesia using multicriteria decision modelling |
| title_short | Mapping the likelihood of introduction and spread of HPAI Virus H5N1 in Indonesia using multicriteria decision modelling |
| title_sort | mapping the likelihood of introduction and spread of hpai virus h5n1 in indonesia using multicriteria decision modelling |
| topic | avian influenza developing countries decision making risk assessment |
| url | https://hdl.handle.net/10568/161918 |
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