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

• Published in: Journal of Tropical Medicine
• Main Authors: J. M. Manel K. Herath, Hemalika T. K. Abeyasundara, W. A. Priyanka P. De Silva, Thilini C. Weeraratne, S. H. P. Parakrama Karunaratne
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2022
• Subjects: Arctic medicine. Tropical medicine; RC955-962; Arctic
• Online Access: https://doi.org/10.1155/2022/4494660; https://doaj.org/article/571a9239f03a4d458055eefb488f5e0d

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.