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. 2014 Aug 8;9(8):e100711.
doi: 10.1371/journal.pone.0100711. eCollection 2014.

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

Affiliations

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

A Townsend Peterson et al. PLoS One. .

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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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Summary of occurrence data input in the ecological niche models.
The dashed outline shows the limits of the area of analysis, with circles indicating the data used by Fichet-Calvet and Rogers . Gray circles indicate data that met quality control criteria levels 1 and 2 (see Table 1). Black crosses indicate data after random subsampling (see Methods for rationale and details).
Figure 2
Figure 2. Mean predicted LF risk map from the Model 2 series developed by Fichet-Calvet and Rogers , 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.
Figure 3
Figure 3. 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.
Figure 4
Figure 4. Overall effect of the three corrections explored in this paper shown as the results of the ‘raw’ models designed to mimic the original models (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.
Figure 5
Figure 5. 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.

References

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