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13 results on '"Okeyo, Winnie A."'

3. A machine learning approach to integrating genetic and ecological data in tsetse flies (Glossina pallidipes) for spatially explicit vector control planning

Author

Bishop, Anusha P., primary, Amatulli, Giuseppe, additional, Hyseni, Chaz, additional, Pless, Evlyn, additional, Bateta, Rosemary, additional, Okeyo, Winnie A., additional, Mireji, Paul O., additional, Okoth, Sylvance, additional, Malele, Imna, additional, Murilla, Grace, additional, Aksoy, Serap, additional, Caccone, Adalgisa, additional, and Saarman, Norah P., additional

Published
2021
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4. A machine learning approach to integrating genetic and ecological data in tsetse flies (Glossina pallidipes) for spatially explicit vector control planning

Author

Bishop, Anusha P., Amatulli, Giuseppe, Hyseni, Chaz, Pless, Evlyn, Bateta, Rosemary, Okeyo, Winnie A., Mireji, Paul O., Okoth, Sylvance, Malele, Imna, Murilla, Grace, Aksoy, Serap, Caccone, Adalgisa, Saarman, Norah P., Bishop, Anusha P., Amatulli, Giuseppe, Hyseni, Chaz, Pless, Evlyn, Bateta, Rosemary, Okeyo, Winnie A., Mireji, Paul O., Okoth, Sylvance, Malele, Imna, Murilla, Grace, Aksoy, Serap, Caccone, Adalgisa, and Saarman, Norah P.

Abstract

Vector control is an effective strategy for reducing vector-borne disease transmission, but requires knowledge of vector habitat use and dispersal patterns. Our goal was to improve this knowledge for the tsetse species Glossina pallidipes, a vector of human and animal African trypanosomiasis, which are diseases that pose serious health and socioeconomic burdens across sub-Saharan Africa. We used random forest regression to (i) build and integrate models of G. pallidipes habitat suitability and genetic connectivity across Kenya and northern Tanzania and (ii) provide novel vector control recommendations. Inputs for the models included field survey records from 349 trap locations, genetic data from 11 microsatellite loci from 659 flies and 29 sampling sites, and remotely sensed environmental data. The suitability and connectivity models explained approximately 80% and 67% of the variance in the occurrence and genetic data and exhibited high accuracy based on cross-validation. The bivariate map showed that suitability and connectivity vary independently across the landscape and was used to inform our vector control recommendations. Post hoc analyses show spatial variation in the correlations between the most important environmental predictors from our models and each response variable (e.g., suitability and connectivity) as well as heterogeneity in expected future climatic change of these predictors. The bivariate map suggests that vector control is most likely to be successful in the Lake Victoria Basin and supports the previous recommendation that G. pallidipes from most of eastern Kenya should be managed as a single unit. We further recommend that future monitoring efforts should focus on tracking potential changes in vector presence and dispersal around the Serengeti and the Lake Victoria Basin based on projected local climatic shifts. The strong performance of the spatial models suggests potential for our integrative methodology to be used to understand future impa

Published
2021
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7. Additional file 1: Table S1. of Temporal genetic differentiation in Glossina pallidipes tsetse fly populations in Kenya

Author

Okeyo, Winnie, Saarman, Norah, Mengual, Michael, Dion, Kirstin, Bateta, Rosemary, Mireji, Paul, Sylvance Okoth, Ouma, Johnson, Collins Ouma, Ochieng, Joel, Murilla, Grace, Aksoy, Serap, and Caccone, Adalgisa

Abstract

Microsatellite locus name, dye used, primer sequences, size range of the amplified product, repeat motif length and the original publication sourced for primer design. Table S2. Microsatellite multiplex name, and the locus marked with each dye. Table S3. GeneMarker panel binning rules, including multiplex design, upper and lower size (bp) boundaries for each marker, allele names, and binning center, minimum, and maximum. Table S4. Pairwise geographical distance (km) and temporal distance (years) between samples from (a) Nguruman, and (b) Ruma. Table S5. Per locus FIS values obtained after testing for deviation from Hardy-Weinberg equilibrium (HWE) using FSTAT v2.9.3 [46] based on 10,000 randomizations for (a) Nguruman and (b) Ruma. Table S6. Pairwise locus P-values after testing for linkage disequilibrium estimated in Genepop v4.1 [43]. Table S7. Per-sample (a) estimates of effective population size (Ne) based on the Jorde/Ryman temporal method showing the interval used in generations (I), the Ne estimate, the 95% CI, and (b) bottleneck results showing the P-values (P) under the two-phase model (TPM), the infinite allele model (IAM) and using the mode-shift test. Figure S1. Allele frequency of each of the 10 microsatellites from (a) Nguruman and (b) Ruma. Figure S2. STRUCTURE [54] estimated Ln likelihood of the data for K values 1â 10 for (a) Nguruman and (b) Ruma, and plots of structure assignment for K values 2â 10 for (c) Nguruman and (d) Ruma. Figure S3. Principal Components Analysis (PCA) of microsatellite data from (a) Nguruman and (b) Ruma. Figure S4. Neighbor-joining tree [57] using Neiâ s genotype frequency based distances (a) Nguruman and (b) Ruma. (DOCX 1088 kb)

Published
2017
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8. Temporal genetic differentiation in Glossina pallidipes tsetse fly populations in Kenya

Author

Okeyo, Winnie A., primary, Saarman, Norah P., additional, Mengual, Michael, additional, Dion, Kirstin, additional, Bateta, Rosemary, additional, Mireji, Paul O., additional, Okoth, Sylvance, additional, Ouma, Johnson O., additional, Ouma, Collins, additional, Ochieng, Joel, additional, Murilla, Grace, additional, Aksoy, Serap, additional, and Caccone, Adalgisa, additional

Published
2017
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