Empirical optimization of risk thresholds for dengue: an approach towards entomological management of Aedes mosquitoes based on larval indices in the Kandy District of Sri Lanka

Abstract

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.

Spatial modelling of the infestation indices of Aedes aegypti: an innovative strategy for vector control actions in developing countries

“…The results demonstrate autocorrelation distribution patterns evolving throughout the year. This analysis is an innovative strategy and may be more effective in planning surveillance actions than infestation rates, as suggested by the literature [25]. Spatial autocorrelation more accurately points out localities with similar levels of vulnerability and risk of Ae.…”

Climate change induced vulnerability and adaption for dengue incidence in Colombo and Kandy Districts: The first detailed investigation in Sri Lanka

“…Despite the limitations and lapses in the entomological and epidemiological databases in Sri Lanka, the BI, PI and the number of reported dengue cases are the only re ective parameters of the direct impacts of climate variability on dengue [17]. Similar to many countries in the world, BI and PI are the most representative stegomyia indices that re ect the dynamics of dengue vector populations in Sri Lanka with an adequate accuracy [17,[51][52][53]. All vector controlling activities conducted by local VCE, are often guided by the BI, PI and the reported dengue cases, especially in timing the control efforts and in prioritizing the areas for resource allocation [17].…”

“…Similar to many countries in the world, BI and PI are the most representative stegomyia indices that re ect the dynamics of dengue vector populations in Sri Lanka with an adequate accuracy [17,[51][52][53]. All vector controlling activities conducted by local VCE, are often guided by the BI, PI and the reported dengue cases, especially in timing the control efforts and in prioritizing the areas for resource allocation [17]. The current vulnerability assessment has recognized all these parameters under exposure, due to their capability of representing the direct physical impacts of climate variability on dengue within the studied MOH areas.…”

“…Meanwhile, the relatively higher temperature, high levels of BI and PI could lead to relatively higher EI levels in Colombo [18,55,65]. On the other hand, the district of Kandy is characterized by relatively low level of BI and PI, degree of urbanization, population density and built environment [17,54]. Variations in the degree of urbanization and the environmental characteristics among these two districts could be recognized as the in uencing factors for spatial dissimilarities in VI [18,54].…”

“…Similar to many developing countries, Sri Lanka also focuses mainly on vector control and management in dengue control. However, numerous limitations in human, mechanical and nancial resources in uence negatively on the success of dengue epidemic management [17]. Therefore, recognition of the potential risk factors that govern the incidence and severity of dengue epidemics, forecasting dengue outbreaks, assessing vulnerability, implementing proactive programmes to reduce existing vulnerabilities and improving resilience capacity of the vulnerable communities are some of the key strategies to ensure the success of dengue epidemic management [16][17][18][19].…”