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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

Mean predicted LF risk map from the Model 2 series developed by Fichet-Calvet and Rogers [7], with posterior probability color scale from 0.0 (no risk) to 1.0 (highest risk) shown at inset.Gray areas are areas either lacking suitable imagery (because of cloud contamination—coastal Nigeria and Cameroon) or that are so distant in environmental space that predictions were not possible. Used with permission.
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pone-0100711-g002: Mean predicted LF risk map from the Model 2 series developed by Fichet-Calvet and Rogers [7], with posterior probability color scale from 0.0 (no risk) to 1.0 (highest risk) shown at inset.Gray areas are areas either lacking suitable imagery (because of cloud contamination—coastal Nigeria and Cameroon) or that are so distant in environmental space that predictions were not possible. Used with permission.

Mentions: In a recent publication, Fichet-Calvet and Rogers [7] provided “risk maps of Lassa fever in West Africa,” assembling a data set of 111 occurrences of LASV infection and LF from published seroprevalence studies and clinical case reports (Figure 1). Rodent occurrence data were also compiled in the study, but were excluded from analyses. The resultant maps have been cited and republished frequently as definitive distribution risk maps for LF [8]. The maps, however, developed using ecological niche modeling approaches, show several characteristics of concern (Figure 2) [9]: high-risk areas are broadly disjunct at the western and eastern extremes of West Africa, without apparent coincidence with known biogeographic or environmental breaks, and (most worrisome) high-risk areas coincide closely with areas of most intense sampling (i.e., near research centers). This result suggests either that LF occurrence has been sampled thoroughly in the only areas where it is most prevalent (i.e., its distribution is well-characterized by existing sampling), or that models were overfit to input data, producing risk maps with little generality or predictive power [10], offering a falsely clear geographic picture of risk. Overfit models are those that replicate well the input data, identifying areas that have already been sampled, but that have little generality that might permit genuine prediction and anticipation of risk in areas where sampling has not occurred. Such models often perform poorly when challenged to predict independent sets of data, as might be produced in the present case via intensive on-ground studies in, for example, Benin or Togo.


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)

Mean predicted LF risk map from the Model 2 series developed by Fichet-Calvet and Rogers [7], with posterior probability color scale from 0.0 (no risk) to 1.0 (highest risk) shown at inset.Gray areas are areas either lacking suitable imagery (because of cloud contamination—coastal Nigeria and Cameroon) or that are so distant in environmental space that predictions were not possible. Used with permission.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0100711-g002: Mean predicted LF risk map from the Model 2 series developed by Fichet-Calvet and Rogers [7], with posterior probability color scale from 0.0 (no risk) to 1.0 (highest risk) shown at inset.Gray areas are areas either lacking suitable imagery (because of cloud contamination—coastal Nigeria and Cameroon) or that are so distant in environmental space that predictions were not possible. Used with permission.
Mentions: In a recent publication, Fichet-Calvet and Rogers [7] provided “risk maps of Lassa fever in West Africa,” assembling a data set of 111 occurrences of LASV infection and LF from published seroprevalence studies and clinical case reports (Figure 1). Rodent occurrence data were also compiled in the study, but were excluded from analyses. The resultant maps have been cited and republished frequently as definitive distribution risk maps for LF [8]. The maps, however, developed using ecological niche modeling approaches, show several characteristics of concern (Figure 2) [9]: high-risk areas are broadly disjunct at the western and eastern extremes of West Africa, without apparent coincidence with known biogeographic or environmental breaks, and (most worrisome) high-risk areas coincide closely with areas of most intense sampling (i.e., near research centers). This result suggests either that LF occurrence has been sampled thoroughly in the only areas where it is most prevalent (i.e., its distribution is well-characterized by existing sampling), or that models were overfit to input data, producing risk maps with little generality or predictive power [10], offering a falsely clear geographic picture of risk. Overfit models are those that replicate well the input data, identifying areas that have already been sampled, but that have little generality that might permit genuine prediction and anticipation of risk in areas where sampling has not occurred. Such models often perform poorly when challenged to predict independent sets of data, as might be produced in the present case via intensive on-ground studies in, for example, Benin or Togo.

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