The Effect of Breeding Habitat Characteristics on the Larval Abundance of Aedes Vector Mosquitoes (Diptera: Culicidae) in Three Localities, Galle District, Sri Lanka

Dissanayake, Devani Sakunthala; Dammika, Wijekoon, Chandana; Wegiriya, H.C.E.

doi:10.1155/2021/9911571

Cited by 6 publications

(6 citation statements)

References 14 publications

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“…Aedes aegypti is the predominant Aedes species in northern [9] and western [10] provinces, whereas Ae. albopictus is dominant in western, north-western, central, and southern provinces [11][12][13]. Since an efective vaccine against the virus is not available, dengue prevention and control mainly depend on efective vector control measures against immature aquatic stages and adults.…”

Section: Introductionmentioning

confidence: 99%

Weather-Based Prediction Models for the Prevalence of Dengue Vectors Aedes aegypti and Ae. albopictus

Herath

Abeyasundara

Silva

et al. 2022

Journal of Tropical Medicine

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Dengue is an important vector-borne disease transmitted by the mosquitoes Aedes aegypti and Ae. albopictus. In the absence of an effective vaccine, vector control has become the key intervention tool in controlling the disease. Vector densities are significantly affected by the changing weather patterns of a region. The present study was conducted in three selected localities, i.e., urban Bandaranayakapura, semiurban Galgamuwa, and rural Buluwala in the Kurunegala district of Sri Lanka to assess spatial and temporal distribution of dengue vector mosquitoes and to predict vector prevalence with respect to changing weather parameters. Monthly ovitrap surveys and larval surveys were conducted from January to December 2019 and continued further in the urban area up to December 2021. Aedes aegypti was found moderately in the urban area and to a lesser extent in semiurban but not in the rural area. Aedes albopictus had the preference for rural over urban areas. Aedes aegypti preferred indoor breeding, while Ae. albopictus preferred both indoor and outdoor. For Ae. albopictus, ovitrap index (OVI), premise index (PI), container index (CI), and Breteau index (BI) correlated with both the rainfall (RF) and relative humidity (RH) of the urban site. Correlations were stronger between OVI and RH and also between BI and RF. Linear regression analysis was fitted, and a prediction model was developed using BI and RF with no lag period (R2 (sq) = 86.3%; F = 53.12; R2 (pred) = 63.12%; model: Log10 (BI) = 0.153 + 0.286 ∗ Log10 (RF); RMSE = 1.49). Another prediction model was developed using OVI and RH with one month lag period (R2 (sq) = 70.21%; F = 57.23; model: OVI predicted = 15.1 + 0.528 ∗ Lag 1 month RH; RMSE = 2.01). These two models can be used to monitor the population dynamics of Ae. albopictus in urban settings to predict possible dengue outbreaks.

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Section: Introductionmentioning

confidence: 99%

Weather-Based Prediction Models for the Prevalence of Dengue Vectors Aedes aegypti and Ae. albopictus

Herath

Abeyasundara

Silva

et al. 2022

Journal of Tropical Medicine

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“…The high-density vegetation is usually an unaddressed area, and dengue can breed even in smaller water collection zones, like the case of water accumulated fallen leaves, water filled holes in the tree trunks, etc. Aedes aegypti and Aedes albopictus had the largest relative abundance in containers of 50–100 mL volume, according to Dissanayake et al, ( 2021 ). Hence, the areas with higher NDVI values will have higher dengue spread, as these areas will have a higher number of sylvan Aedes mosquitoes.…”

Section: Resultsmentioning

confidence: 93%

Dengue risk zone mapping of Thiruvananthapuram district, India: a comparison of the AHP and F-AHP methods

et al. 2022

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Dengue fever, which is spread by Aedes mosquitoes, has claimed many lives in Kerala, with the Thiruvananthapuram district bearing the brunt of the toll. This study aims to demarcate the dengue risk zones in Thiruvananthapuram district using the analytical hierarchy process (AHP) and the fuzzy-AHP (F-AHP) methods. For the risk modelling, geo-environmental factors (normalized difference vegetation index, land surface temperature, topographic wetness index, land use/land cover types, elevation, normalized difference built-up index) and demographic factors (household density, population density) have been utilized. The ArcGIS 10.8 and ERDAS Imagine 8.4 software tools have been used to derive the risk zone maps. The area of the risk maps is classified into five zones. The dengue risk zone maps were validated using dengue case data collected from the Integrated Disease Surveillance Programme portal. From the receiver operating characteristic (ROC) curve analysis and the area under the ROC curve (AUC) values, it is proved that the F-AHP method (AUC value of 0.971) has comparatively more prediction capability than the AHP method (AUC value of 0.954) in demarcating the dengue risk zones. Also, based on the comparison of the risk zone map with actual case data, it was confirmed that around 82.87% of the dengue cases occurred in the very high and high-risk zones, thus proving the efficacy of the model. According to the dengue risk map prepared using the F-AHP model, 9.09% of the area of Thiruvananthapuram district is categorized as very high risk. The prepared dengue risk maps will be helpful for decision-makers, staff with the health, and disaster management departments in adopting effective measures to prevent the risks of dengue spread and thereby minimize loss of life.

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“…et al, 2021 and Herath, J.M. et al, 2024) [2,9] . Study done in Nigeria also strongly agree with the result obtained in the study done in gash barka more larvae of Aedes mosquito were collected from urban comparing to the rural (Egwu, O. et al, 2018) [2] .…”

Section: Discussion and Recommendationsmentioning

confidence: 99%

“…et al, 2024) [2,9] . Study done in Nigeria also strongly agree with the result obtained in the study done in gash barka more larvae of Aedes mosquito were collected from urban comparing to the rural (Egwu, O. et al, 2018) [2] . The source reduction is an effective way for the community to manage the populations of many kinds of mosquitoes (Rajesh, K; et al 2013) [12] .…”

Section: Discussion and Recommendationsmentioning

confidence: 99%

Assessment of container breeding preference, distribution and composition of human biting mosquitos (Diptera: Culicidae) larvae within the urban and semi-urban sites of the Gash Barka zone, Eritrea

Weldu,

Mebrahtu,

Yakob

2024

Int. J. Mosq. Res.

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Mosquito-borne diseases, particularly malaria, pose a significant public health threat globally. Affecting nearly half the world's population, malaria is most prevalent in Sub-Saharan Africa, including Eritrea's Gash Barka Zone. This region accounts for 80% of the country's annual malaria cases, with over 67% of its population living in high-risk areas. To understand mosquito breeding patterns and inform control strategies, a cross-sectional study was conducted in Gash Barka Zone. This study surveyed mosquito larvae in various artificial containers across six districts (three urban and three semi-urban) from September to December 2020. A total of 2,570 containers were inspected, with 358 found positive for mosquito larvae. The study identified two dominant mosquito species: Aedes (8.7%) and Anopheles (5.4%). Notably, the survey revealed a higher prevalence of artificial containers harboring mosquito larvae in urban areas compared to semi-urban settings. Additionally, Aedes larvae exhibited a preference for breeding in urban environments. These findings highlight the importance of identifying mosquito breeding habitats for effective control of mosquito-borne diseases like malaria, dengue, and chikungunya. By understanding mosquito breeding preferences and distribution patterns, targeted interventions can be implemented to reduce mosquito populations and ultimately, disease transmission.

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The Effect of Breeding Habitat Characteristics on the Larval Abundance of Aedes Vector Mosquitoes (Diptera: Culicidae) in Three Localities, Galle District, Sri Lanka

Cited by 6 publications

References 14 publications

Weather-Based Prediction Models for the Prevalence of Dengue Vectors Aedes aegypti and Ae. albopictus

Weather-Based Prediction Models for the Prevalence of Dengue Vectors Aedes aegypti and Ae. albopictus

Dengue risk zone mapping of Thiruvananthapuram district, India: a comparison of the AHP and F-AHP methods

Assessment of container breeding preference, distribution and composition of human biting mosquitos (Diptera: Culicidae) larvae within the urban and semi-urban sites of the Gash Barka zone, Eritrea

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