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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

Summary of effects of three factors assessed in this study as potentially influencing model outcomes: quality control of input occurrence data (top panel), reduction of oversampling of occurrences in certain areas (middle), and weighting omission versus commission errors appropriately (bottom).In each case, the map represents a difference between our corrected and our mimicking of the original analysis such that a score of 100 (dark red) indicates a situation wherein the original analysis overemphasized the suitability of a site, whereas a score of -100 (dark blue) indicates underemphasis. All three maps are shown on the same color scale.
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pone-0100711-g003: Summary of effects of three factors assessed in this study as potentially influencing model outcomes: quality control of input occurrence data (top panel), reduction of oversampling of occurrences in certain areas (middle), and weighting omission versus commission errors appropriately (bottom).In each case, the map represents a difference between our corrected and our mimicking of the original analysis such that a score of 100 (dark red) indicates a situation wherein the original analysis overemphasized the suitability of a site, whereas a score of -100 (dark blue) indicates underemphasis. All three maps are shown on the same color scale.

Mentions: The original 107 case-occurrence points we considered reduced to only 20 when only high-confidence points (i.e., levels 1 and 2) were considered. Comparing models based on these two data sets (Figure 3), models without occurrence data quality control overemphasized humid areas of West and Central Africa, at the expense of areas in the Sahel that may also be suitable for LF occurrence. However, areas of contrast between the two maps were spatially autocorrelated—that is, they were highly contiguous, such that quality control of diagnoses of LF cases had clear and consistent implications for the ecological niche model that resulted and the potential distribution that was reconstructed.


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)

Summary of effects of three factors assessed in this study as potentially influencing model outcomes: quality control of input occurrence data (top panel), reduction of oversampling of occurrences in certain areas (middle), and weighting omission versus commission errors appropriately (bottom).In each case, the map represents a difference between our corrected and our mimicking of the original analysis such that a score of 100 (dark red) indicates a situation wherein the original analysis overemphasized the suitability of a site, whereas a score of -100 (dark blue) indicates underemphasis. All three maps are shown on the same color scale.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0100711-g003: Summary of effects of three factors assessed in this study as potentially influencing model outcomes: quality control of input occurrence data (top panel), reduction of oversampling of occurrences in certain areas (middle), and weighting omission versus commission errors appropriately (bottom).In each case, the map represents a difference between our corrected and our mimicking of the original analysis such that a score of 100 (dark red) indicates a situation wherein the original analysis overemphasized the suitability of a site, whereas a score of -100 (dark blue) indicates underemphasis. All three maps are shown on the same color scale.
Mentions: The original 107 case-occurrence points we considered reduced to only 20 when only high-confidence points (i.e., levels 1 and 2) were considered. Comparing models based on these two data sets (Figure 3), models without occurrence data quality control overemphasized humid areas of West and Central Africa, at the expense of areas in the Sahel that may also be suitable for LF occurrence. However, areas of contrast between the two maps were spatially autocorrelated—that is, they were highly contiguous, such that quality control of diagnoses of LF cases had clear and consistent implications for the ecological niche model that resulted and the potential distribution that was reconstructed.

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