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Spatio-temporal analysis of abundances of three malaria vector species in southern Benin using zero-truncated models.

Moiroux N, Djènontin A, Bio-Bangana AS, Chandre F, Corbel V, Guis H - Parasit Vectors (2014)

Bottom Line: During the dry season, predictive maps showed that abundance greater than 1 bite per person per night were observed only for An. funestus and An. coluzzii.During the rainy season, we observed both increase and decrease in abundance of An. funestus, which are dependent on the ecological setting.Abundances of both An. coluzzii and An. gambiae s.s. increased during the rainy season but not in the same areas.

View Article: PubMed Central - HTML - PubMed

Affiliation: MIVEGEC (IRD 224-CNRS 5290-UM1-UM2), Institut de Recherche pour le Développement (IRD), BP64501, 34394 Montpellier, France. nicolas.moiroux@ird.fr.

ABSTRACT

Background: A better understanding of the ecology and spatial-temporal distribution of malaria vectors is essential to design more effective and sustainable strategies for malaria control and elimination. In a previous study, we analyzed presence-absence data of An. funestus, An. coluzzii, and An. gambiae s.s. in an area of southern Benin with high coverage of vector control measures. Here, we further extend the work by analysing the positive values of the dataset to assess the determinants of the abundance of these three vectors and to produce predictive maps of vector abundance.

Methods: Positive counts of the three vectors were assessed using negative-binomial zero-truncated (NBZT) mixed-effect models according to vector control measures and environmental covariates derived from field and remote sensing data. After 8-fold cross-validation of the models, predictive maps of abundance of the sympatric An. funestus, An. coluzzii, and An. gambiae s.s. were produced.

Results: Cross-validation of the NBZT models showed a satisfactory predictive accuracy. Almost all changes in abundance between two surveys in the same village were well predicted by the models but abundances for An. gambiae s.s. were slightly underestimated. During the dry season, predictive maps showed that abundance greater than 1 bite per person per night were observed only for An. funestus and An. coluzzii. During the rainy season, we observed both increase and decrease in abundance of An. funestus, which are dependent on the ecological setting. Abundances of both An. coluzzii and An. gambiae s.s. increased during the rainy season but not in the same areas.

Conclusions: Our models helped characterize the ecological preferences of three major African malaria vectors. This works highlighted the importance to study independently the binomial and the zero-truncated count processes when evaluating vector control strategies. The study of the bio-ecology of malaria vector species in time and space is critical for the implementation of timely and efficient vector control strategies.

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Related in: MedlinePlus

Distribution of the Relative Error of Predictions (REP) of positive abundances of (A) An. funestus, (B) An. coluzzii, and (C) An. gambiae s.s. The relative error of the predictions (REP) in each village during each survey was calculated as REP = /Obs - Pred//Obs with Pred the predicted value and Obs the observed number of Anopheles collected in the field. Boxes indicate median, 1st and the 3rd quartiles. Whiskers indicate the most extreme data that is no more than 1.5 times the interquartile range. Outliers are not showed.
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Figure 1: Distribution of the Relative Error of Predictions (REP) of positive abundances of (A) An. funestus, (B) An. coluzzii, and (C) An. gambiae s.s. The relative error of the predictions (REP) in each village during each survey was calculated as REP = /Obs - Pred//Obs with Pred the predicted value and Obs the observed number of Anopheles collected in the field. Boxes indicate median, 1st and the 3rd quartiles. Whiskers indicate the most extreme data that is no more than 1.5 times the interquartile range. Outliers are not showed.

Mentions: Figure 1 shows the distribution of the relative error of prediction against the observed counts of Anopheles. We observed that the prediction error tended to be lower for lower number of vectors collected. The error distributions and hence the predictive powers of the three models were highly comparable. Figure 2 compares the predicted counts to the observed counts for the three species in each village during each survey. The An. funestus (Figure 2A) and An. coluzzii (Figure 2B) models were very efficient in predicting counts. Although the An. gambiae s.s. model (Figure 2C) reflected the trends well, it often underestimated counts.


Spatio-temporal analysis of abundances of three malaria vector species in southern Benin using zero-truncated models.

Moiroux N, Djènontin A, Bio-Bangana AS, Chandre F, Corbel V, Guis H - Parasit Vectors (2014)

Distribution of the Relative Error of Predictions (REP) of positive abundances of (A) An. funestus, (B) An. coluzzii, and (C) An. gambiae s.s. The relative error of the predictions (REP) in each village during each survey was calculated as REP = /Obs - Pred//Obs with Pred the predicted value and Obs the observed number of Anopheles collected in the field. Boxes indicate median, 1st and the 3rd quartiles. Whiskers indicate the most extreme data that is no more than 1.5 times the interquartile range. Outliers are not showed.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4008307&req=5

Figure 1: Distribution of the Relative Error of Predictions (REP) of positive abundances of (A) An. funestus, (B) An. coluzzii, and (C) An. gambiae s.s. The relative error of the predictions (REP) in each village during each survey was calculated as REP = /Obs - Pred//Obs with Pred the predicted value and Obs the observed number of Anopheles collected in the field. Boxes indicate median, 1st and the 3rd quartiles. Whiskers indicate the most extreme data that is no more than 1.5 times the interquartile range. Outliers are not showed.
Mentions: Figure 1 shows the distribution of the relative error of prediction against the observed counts of Anopheles. We observed that the prediction error tended to be lower for lower number of vectors collected. The error distributions and hence the predictive powers of the three models were highly comparable. Figure 2 compares the predicted counts to the observed counts for the three species in each village during each survey. The An. funestus (Figure 2A) and An. coluzzii (Figure 2B) models were very efficient in predicting counts. Although the An. gambiae s.s. model (Figure 2C) reflected the trends well, it often underestimated counts.

Bottom Line: During the dry season, predictive maps showed that abundance greater than 1 bite per person per night were observed only for An. funestus and An. coluzzii.During the rainy season, we observed both increase and decrease in abundance of An. funestus, which are dependent on the ecological setting.Abundances of both An. coluzzii and An. gambiae s.s. increased during the rainy season but not in the same areas.

View Article: PubMed Central - HTML - PubMed

Affiliation: MIVEGEC (IRD 224-CNRS 5290-UM1-UM2), Institut de Recherche pour le Développement (IRD), BP64501, 34394 Montpellier, France. nicolas.moiroux@ird.fr.

ABSTRACT

Background: A better understanding of the ecology and spatial-temporal distribution of malaria vectors is essential to design more effective and sustainable strategies for malaria control and elimination. In a previous study, we analyzed presence-absence data of An. funestus, An. coluzzii, and An. gambiae s.s. in an area of southern Benin with high coverage of vector control measures. Here, we further extend the work by analysing the positive values of the dataset to assess the determinants of the abundance of these three vectors and to produce predictive maps of vector abundance.

Methods: Positive counts of the three vectors were assessed using negative-binomial zero-truncated (NBZT) mixed-effect models according to vector control measures and environmental covariates derived from field and remote sensing data. After 8-fold cross-validation of the models, predictive maps of abundance of the sympatric An. funestus, An. coluzzii, and An. gambiae s.s. were produced.

Results: Cross-validation of the NBZT models showed a satisfactory predictive accuracy. Almost all changes in abundance between two surveys in the same village were well predicted by the models but abundances for An. gambiae s.s. were slightly underestimated. During the dry season, predictive maps showed that abundance greater than 1 bite per person per night were observed only for An. funestus and An. coluzzii. During the rainy season, we observed both increase and decrease in abundance of An. funestus, which are dependent on the ecological setting. Abundances of both An. coluzzii and An. gambiae s.s. increased during the rainy season but not in the same areas.

Conclusions: Our models helped characterize the ecological preferences of three major African malaria vectors. This works highlighted the importance to study independently the binomial and the zero-truncated count processes when evaluating vector control strategies. The study of the bio-ecology of malaria vector species in time and space is critical for the implementation of timely and efficient vector control strategies.

Show MeSH
Related in: MedlinePlus