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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

Detail of Sierra Leone from the “corrected” model shown in Figure 4.The top panel shows the modeled suitability (black = low, white = high) and LASV-infected rodent prevalences at 13 sites across the country (shading within squares, black = low, white = high). The bottom panel shows the relationship between LASV prevalences in rodents and modeled LF suitability.
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pone-0100711-g005: Detail of Sierra Leone from the “corrected” model shown in Figure 4.The top panel shows the modeled suitability (black = low, white = high) and LASV-infected rodent prevalences at 13 sites across the country (shading within squares, black = low, white = high). The bottom panel shows the relationship between LASV prevalences in rodents and modeled LF suitability.

Mentions: Given that two of the three authors of this paper (LMM and DGB) have considerable experience with LF in Sierra Leone, we paid considerable attention to patterns of suitability that were reconstructed in that region (Figure 5). The uneven pattern of suitability in the region was intriguing, with areas of high and low suitability reconstructed across the country. Hence, we took two additional steps in exploring and understanding our models.


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)

Detail of Sierra Leone from the “corrected” model shown in Figure 4.The top panel shows the modeled suitability (black = low, white = high) and LASV-infected rodent prevalences at 13 sites across the country (shading within squares, black = low, white = high). The bottom panel shows the relationship between LASV prevalences in rodents and modeled LF suitability.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0100711-g005: Detail of Sierra Leone from the “corrected” model shown in Figure 4.The top panel shows the modeled suitability (black = low, white = high) and LASV-infected rodent prevalences at 13 sites across the country (shading within squares, black = low, white = high). The bottom panel shows the relationship between LASV prevalences in rodents and modeled LF suitability.
Mentions: Given that two of the three authors of this paper (LMM and DGB) have considerable experience with LF in Sierra Leone, we paid considerable attention to patterns of suitability that were reconstructed in that region (Figure 5). The uneven pattern of suitability in the region was intriguing, with areas of high and low suitability reconstructed across the country. Hence, we took two additional steps in exploring and understanding our models.

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