Introgression between Anopheles gambiae and Anopheles coluzzii in Burkina Faso and its associations with kdr resistance and Plasmodium infection

Background: Insecticide resistance in Anopheles coluzzii mosquitoes has become widespread throughout West Africa including in Burkina Faso. The insecticide resistance allele (kdr or L1014F) is a prime indicator that is highly correlated with phenotypic resistance in West Africa. Studies from Benin, Ghana and Mali have suggested that the source of the L1014F is introgression of the 2L divergence island via interspecific hybridization with Anopheles gambiae. The goal of this study was to characterize local mosquito populations in the Nouna Department, Burkina Faso with respect to: (i) the extent of introgression between An. coluzzii and An. gambiae, (ii) the frequency of the L1014F mutation and (iii) Plasmodium infection rates. Methods: A total of 95 mosquitoes were collected from ten sites surrounding Nouna town in Kossi Province, Burkina Faso in 2012. The species composition, the extent of introgression in An. coluzzii mosquitoes and their Plasmodium infection rates were identified with a modified version of the “Divergence Island SNP” (DIS) genotyping assay. Results: The mosquito collection contained 70.5% An. coluzzii, 89.3% of which carried a 3 Mb genomic region on the 2L chromosome with L1014F insecticide resistance mutation that was introgressed from An. gambiae. In addition, 22.4% in the introgressed An. coluzzii specimens were infected with Plasmodium falciparum, whereas none of the non-introgressed (“pure”) An. coluzzii were infected. Conclusion: This paper is the first report providing divergence island SNP genotypes for natural population of Burkina Faso and corresponding Plasmodium infection rates. These observations warrant further study and could have a major impact on future malaria control strategies in Burkina Faso

Estimation of Change Point in Generalized Variance Control Chart

In this study, using maximum likelihood estimation, a considerably effective change point model is proposed for the generalized variance control chart in which the required statistics are calculated with its distributional properties. The procedure, when used with generalized variance control charts, would be helpful for practitioners both controlling the multivariate process dispersion and detecting the time of the change in variance-covariance matrix of a process. The procedure starts after the chart issues a signal. Several structural changes for the variance-covariance matrix are considered and the precision and the accuracy of the proposed method is discussed

A Multivariate Change Point Detection Procedure for Monitoring Mean and Covariance Simultaneously

The signal issued by a control chart triggers the process professionals to investigate the special cause. Change point methods simplify the efforts to search for and identify the special cause. In this study, using maximum likelihood estimation, a multivariate joint change point estimation procedure for monitoring both location and dispersion simultaneously is proposed. After a signal is generated by the simultaneously used Hotelling's T-2 and/or generalized variance control charts, the procedure starts detecting the time of the change. The performance of the proposed method for several structural changes for the mean vector and covariance matrix is discussed

A Multivariate Change Point Detection Procedure for Monitoring Mean and Covariance Simultaneously

The signal issued by a control chart triggers the process professionals to investigate the special cause. Change point methods simplify the efforts to search for and identify the special cause. In this study, using maximum likelihood estimation, a multivariate joint change point estimation procedure for monitoring both location and dispersion simultaneously is proposed. After a signal is generated by the simultaneously used Hotelling's T-2 and/or generalized variance control charts, the procedure starts detecting the time of the change. The performance of the proposed method for several structural changes for the mean vector and covariance matrix is discussed