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BackgroundLarval indices such as Premise Index (PI), Breteau Index (BI) and Container Index (CI) are widely used to interpret the density of dengue vectors in surveillance programmes. These indices may be useful for forecasting disease outbreaks in an area. However, use of the values of these indices as alarm signals is rarely considered in control programmes. Therefore, the current study aims to propose threshold values for vector indices based on an empirical modeling approach for the Kandy District of Sri Lanka.MethodsMonthly vector indices, viz PI, BI and CI, for Aedes aegypti and Aedes albopictus, of four selected dengue high risk Medical Officer of Health (MOH) areas in the Kandy District from January 2010 to August 2017, were used in the study. Gumbel frequency analysis was used to calculate the exceedance probability of quantitative values for each individual larval index within the relevant MOH area, individually and to set up the threshold values for the entomological management of dengue vectors.ResultsAmong the study MOH areas, Akurana indicated a relatively high density of both Ae. aegypti and Ae. albopictus, while Gangawata Korale MOH area had the lowest. Based on Ae. aegypti, threshold values were defined for Kandy as low risk (BIagp > 1.77), risk (BIagp > 3.23), moderate risk (BIagp > 4.47) and high risk (BIagp > 6.23). In addition, PI > 6.75 was defined as low risk, while PI > 9.43 and PI>12.82 were defined as moderate and high risk, respectively as an average.ConclusionsThreshold values recommended for Ae. aegypti (primary vector for dengue) along with cut-off values for PI (for Ae. aegypti and Ae. albopictus), could be suggested as indicators for decision making in vector control efforts. This may also facilitate the rational use of financial allocations, technical and human resources for vector control approaches in Sri Lanka in a fruitful manner.Electronic supplementary materialThe online version of this article (10.1186/s13071-018-2961-y) contains supplementary material, which is available to authorized users.

In this letter, a new approach for the learning process of multilayer feedforward neural network is introduced. This approach minimizes a modified form of the criterion used in the standard backpropagation algorithm. This criterion is based on the sum of the linear and the nonlinear quadratic errors of the output neuron. The quadratic linear error signal is appropriately weighted. The choice of the weighted design parameter is evaluated via rank convergence series analysis and asymptotic constant error values. The new proposed modified standard backpropagation algorithm (MBP) is first derived on a single neuron-based net and then extended to a general feedforward neural network. Simulation results of the 4-b parity checker and the circle in the square problem confirm that the performance of the MBP algorithm exceed the standard backpropagation (SBP) in the reduction of the total number of iterations and in the learning time.

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