- Open Access
Erratum to: Development and evaluation of mosquito-electrocuting traps as alternatives to the human landing catch technique for sampling host-seeking malaria vectors
© The Author(s) 2016
- Published: 15 November 2016
The original article was published in Malaria Journal 2015 14:502
The authors regret that the statistical analysis methods used in this article  to assess density-dependence between different types of trap (described in the third paragraph of the Statistical analysis section and in Supplementary Information S1) are invalid. However, re-analysis with a corrected method, described below, produced similar results to those obtained using the invalid method. Consequently, our conclusions regarding density-dependence are unchanged.
The methods in question aimed to test a null hypothesis of density-independence (linear correlation) against an alternative hypothesis of density-dependence (non-linear correlation), and to quantify the strength of linear correlation using the Pearson correlation coefficient. The flaw in the methods was in applying them to mosquito counts that had been log-transformed using ln(1 + count). This analysis assesses linearity on the log scale, but fails to take account of the fact that linearity on the log scale does not imply linearity on the untransformed count scale, which is the relevant scale when assessing density dependence. The simple solution of applying the same method to untransformed counts would not be valid due to violation of the standard assumptions of linear regression (normality and homoscedasticity of residuals), on which the method relies. We have therefore developed a new method for assessing density-dependence on the untransformed count scale, described fully in , which we have used to re-analyse the data in . Briefly, the new method models density-dependence between two trapping methods as a power law relationship, with E(x i ) = αE(y i ) β , where x i and y i are the ith of n paired mosquito catches from traps of types X and Y, E(x i ) and E(y i ) are the expected counts of x i and y i , and α is a scaling constant. The exponent β governs the degree of density-dependence [3, 4], so density-dependence can be quantified by the extent to which β deviates from 1. We calculated estimates and 95 % credible intervals (CI) for the density-dependence parameter, β, and a linear correlation coefficient for count data, r, defined in . A 95 % CI for β that did not include 1 was taken as evidence for density-dependence. Both statistics were estimated using Markov chain Monte Carlo (MCMC) as described in , with the exception that, instead of 104, a more informative prior variance for ln(β) of 1 was used (equivalent to specifying that, in the absence of data, the true value of β lies between 0.14 and 7.1 with 95 % probability). This change aided convergence when there was little information in the data about β due to low correlation between trapping methods.
Results of the re-analysis of the density-dependence data with the corrected statistical analysis method, for comparison with Tables 3 and S2 in Maliti et al. 
β estimate (95 % CI)
r estimate (95 % CI)
An. gambiae s.l.
1.17 (0.08, 2.83)
0.35 (0.00, 0.64)
1.32 (0.05, 3.67)
0.22 (0.00, 0.54)
0.77 (0.32, 1.27)
0.59 (0.27, 0.86)a
0.76 (0.22, 1.33)
0.51 (0.13, 0.83)a
An. funestus s.l.
1.35 (0.04, 4.02)
0.12 (0.00, 0.43)
1.45 (0.03, 4.30)
0.13 (0.00, 0.47)
1.05 (0.62, 1.51)
0.76 (0.53, 0.94)a
2.97 (0.06, 7.60)
0.15 (0.00, 0.54)
In summary, the conclusions regarding density-dependence that were reported in  were based on a flawed statistical analysis method, but are unchanged following re-analysis with a corrected method.
All authors read and approved the publication of this Erratum. We acknowledge our co-author P.C.D. Johnson for recognizing the flaw in our original analysis, performing the re-analysis with the corrected approach and drafting this Erratum.
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