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Mapping transmission risk of Lassa fever in West Africa: the importance of quality control, sampling bias, and error weighting.

Peterson AT, Moses LM, Bausch DG - PLoS ONE (2014)

Bottom Line: Each of the three factors assessed in this study had clear and consistent influences on model results, overestimating risk in southern, humid zones in West Africa, and underestimating risk in drier and more northern areas.The final, adjusted risk map indicates broad risk areas across much of West Africa.Although risk maps are increasingly easy to develop from disease occurrence data and raster data sets summarizing aspects of environments and landscapes, this process is highly sensitive to issues of data quality, sampling design, and design of analysis, with macrogeographic implications of each of these issues and the potential for misrepresenting real patterns of risk.

View Article: PubMed Central - PubMed

Affiliation: Biodiversity Institute, University of Kansas, Lawrence, Kansas, United States of America.

ABSTRACT
Lassa fever is a disease that has been reported from sites across West Africa; it is caused by an arenavirus that is hosted by the rodent M. natalensis. Although it is confined to West Africa, and has been documented in detail in some well-studied areas, the details of the distribution of risk of Lassa virus infection remain poorly known at the level of the broader region. In this paper, we explored the effects of certainty of diagnosis, oversampling in well-studied region, and error balance on results of mapping exercises. Each of the three factors assessed in this study had clear and consistent influences on model results, overestimating risk in southern, humid zones in West Africa, and underestimating risk in drier and more northern areas. The final, adjusted risk map indicates broad risk areas across much of West Africa. Although risk maps are increasingly easy to develop from disease occurrence data and raster data sets summarizing aspects of environments and landscapes, this process is highly sensitive to issues of data quality, sampling design, and design of analysis, with macrogeographic implications of each of these issues and the potential for misrepresenting real patterns of risk.

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Related in: MedlinePlus

Overall effect of the three corrections explored in this paper shown as the results of the ‘raw’ models designed to mimic the original models [7] (top panel), models based on all three of the corrections together (middle), and the difference between the two (bottom).In the bottom map, red areas are those overemphasized in the raw models, while blue areas indicate underemphasis of the raw models.
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pone-0100711-g004: Overall effect of the three corrections explored in this paper shown as the results of the ‘raw’ models designed to mimic the original models [7] (top panel), models based on all three of the corrections together (middle), and the difference between the two (bottom).In the bottom map, red areas are those overemphasized in the raw models, while blue areas indicate underemphasis of the raw models.

Mentions: Three corrections to risk mapping procedures where individually assessed in the preceding paragraphs. The comparison between the raw, unfixed, and corrected model outputs is quite instructive. Raw model outputs resemble closely the maps presented by Fichet-Calvet and Rogers [7] (see our Figures 2 and 4), emphasizing humid forest habitats across West Africa and south and east into Central Africa. In contrast, the corrected models extend considerably farther north into the more arid Sahel region, but areas of Ghana and Côte d'Ivoire show more reduced areas of suitability.


Mapping transmission risk of Lassa fever in West Africa: the importance of quality control, sampling bias, and error weighting.

Peterson AT, Moses LM, Bausch DG - PLoS ONE (2014)

Overall effect of the three corrections explored in this paper shown as the results of the ‘raw’ models designed to mimic the original models [7] (top panel), models based on all three of the corrections together (middle), and the difference between the two (bottom).In the bottom map, red areas are those overemphasized in the raw models, while blue areas indicate underemphasis of the raw models.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4126660&req=5

pone-0100711-g004: Overall effect of the three corrections explored in this paper shown as the results of the ‘raw’ models designed to mimic the original models [7] (top panel), models based on all three of the corrections together (middle), and the difference between the two (bottom).In the bottom map, red areas are those overemphasized in the raw models, while blue areas indicate underemphasis of the raw models.
Mentions: Three corrections to risk mapping procedures where individually assessed in the preceding paragraphs. The comparison between the raw, unfixed, and corrected model outputs is quite instructive. Raw model outputs resemble closely the maps presented by Fichet-Calvet and Rogers [7] (see our Figures 2 and 4), emphasizing humid forest habitats across West Africa and south and east into Central Africa. In contrast, the corrected models extend considerably farther north into the more arid Sahel region, but areas of Ghana and Côte d'Ivoire show more reduced areas of suitability.

Bottom Line: Each of the three factors assessed in this study had clear and consistent influences on model results, overestimating risk in southern, humid zones in West Africa, and underestimating risk in drier and more northern areas.The final, adjusted risk map indicates broad risk areas across much of West Africa.Although risk maps are increasingly easy to develop from disease occurrence data and raster data sets summarizing aspects of environments and landscapes, this process is highly sensitive to issues of data quality, sampling design, and design of analysis, with macrogeographic implications of each of these issues and the potential for misrepresenting real patterns of risk.

View Article: PubMed Central - PubMed

Affiliation: Biodiversity Institute, University of Kansas, Lawrence, Kansas, United States of America.

ABSTRACT
Lassa fever is a disease that has been reported from sites across West Africa; it is caused by an arenavirus that is hosted by the rodent M. natalensis. Although it is confined to West Africa, and has been documented in detail in some well-studied areas, the details of the distribution of risk of Lassa virus infection remain poorly known at the level of the broader region. In this paper, we explored the effects of certainty of diagnosis, oversampling in well-studied region, and error balance on results of mapping exercises. Each of the three factors assessed in this study had clear and consistent influences on model results, overestimating risk in southern, humid zones in West Africa, and underestimating risk in drier and more northern areas. The final, adjusted risk map indicates broad risk areas across much of West Africa. Although risk maps are increasingly easy to develop from disease occurrence data and raster data sets summarizing aspects of environments and landscapes, this process is highly sensitive to issues of data quality, sampling design, and design of analysis, with macrogeographic implications of each of these issues and the potential for misrepresenting real patterns of risk.

Show MeSH
Related in: MedlinePlus