Abstract
Risk maps are one of several sources used to inform risk-based disease surveillance and control systems, but their production can be hampered by lack of access to suitable disease data. In such situations, knowledge-driven spatial modeling methods are an alternative to data-driven approaches. This study used multicriteria decision analysis (MCDA) to identify areas in Asia suitable for the occurrence of highly pathogenic avian influenza virus (HPAIV) H5N1 in domestic poultry. Areas most suitable for H5N1 occurrence included Bangladesh, the southern tip and eastern coast of Vietnam, parts of north-central Thailand and large parts of eastern China. The predictive accuracy of the final model, as determined by the area under the receiver operating characteristic curve (ROC AUC), was 0.670 (95% CI 0.667-0.673) suggesting that, in data-scarce environments, MCDA provides a reasonable alternative to the data-driven approaches usually used to inform risk-based disease surveillance and control strategies. © 2012.
| Original language | English |
|---|---|
| Pages (from-to) | 1-14 |
| Journal | Spatial and Spatio-temporal Epidemiology |
| Volume | 4 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Mar 2013 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Research Keywords
- Emerging infectious diseases
- Influenza in birds
- Knowledge-driven spatial modeling
- Model validation
- Poultry diseases
- Risk factors
Policy Impact
- Cited in Policy Documents
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