IPRC - 2019
Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/20881
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Item Prediction of Dengue Incidence Based on Time Series Modelling in the District of Colombo, Sri Lanka(International Postgraduate Research Conference 2019, Faculty of Graduate Studies, University of Kelaniya, Sri Lanka, 2019) Udayanga, L.; Herath, K.; Gunanthilaka, N.; Iqbal, M.C.M.; Abeyewickreme, W.Timely implementation of intervention activities, is essential in controlling dengue epidemics. This requires the prediction of dengue epidemics, while respecting the spatial and temporal trends in dengue incidence. However, such aspects are limitedly focused in dengue epidemic management of Sri Lanka. Therefore, the current study was conducted to develop a temporal prediction model for dengue incidence in the district of Colombo in Sri Lanka. Dengue cases reported from 2000 to 2018 in the district of Colombo were collected from the Epidemiology Unit, Sri Lanka. Selected meteorological parameters such as number of rainy days, monthly cumulative rainfall, minimum and maximum relative humidity and temperature corresponding to the same study period were collected from the Department of Meteorology, along with the Oceanic Niño Index (ONI) from the National Oceanic and Administration (NOAA) Centre. All the data were arranged at monthly level. After evaluation of the normality, seasonality, stationarity and seasonal stationarity of the epidemic data, a Seasonal Autoregressive Integrated Moving Average (SARIMA) model was fitted for the prediction of dengue by using the R statistical package. Subsequently, the meteorological factors and the dengue incidence was subjected to a cross correlation analysis to identify the most representative meteorological factors associated with dengue epidemic incidence and an Autoregressive Integrated Moving Average with Exogeneous Input (ARIMAX) model was fitted. The best fitted SARIMA (0, 1, 0) (3, 0, 0)12 model was characterized by an Akaike Information Criteria value (AIC) of -19.04, Bayesian information criterion (BIC) of -5.42, Mean error (ME) of 0.002 and Root Mean Square Error (RMSE) of 0.518. According to the cross correlation analysis, number of rainy days (RD) and Oceanic Niño Index (ONI) denoted a significant negative association with the reported dengue cases in Colombo, while monthly cumulative rainfall (RF), maximum relative humidity (Max_RH), maximum temperature (Max_T) and minimum temperature (Min_T) shared a positive correlation (P < 0.05 at 95% level of confidence). The best fitting ARIMAX model (as indicated below) was characterized by an AIC of -15.74, BIC of -11. 2, ME of 0.006 and RMSE of 0.171. ARIMA (0, 1, 1) + [-0.0006 RDt-3 + 0.0008 RFt-3 + 0.0260 Max_RHt-3 + 0.0766 Min_Tt—4 - 0.0661 ONIt-5] Based on the performance, the ARIMAX model is recommended to be used for the prediction of dengue incidence in the Colombo district to ensure rational allocation of resources for vector control and dengue epidemic managementItem Evaluation of the Pyrethroid Resistance based on Voltage-Gated Sodium Channel (VGSC) Mutations in Aedes aegypti populations of Colombo, Gampaha and Kandy Districts in Sri Lanka(International Postgraduate Research Conference 2019, Faculty of Graduate Studies, University of Kelaniya, Sri Lanka, 2019) Ranathunge, T.; Udayanga, L.; Sarasija, S.; Karunathilaka, S.; Nawarathne, S.; Rathnarajah, H.; Dulficar, F.F.; Shafi, F.N.; Dassanayake, R.S.; Gunawardene, Y.I.N.S.Many countries focus on chemical based vector control strategies to restrict the disease transmissions, where pyrethroid insecticides are widely used as the first line of defense against Ae. aegypti. However, the constant use of insecticides have proven to induce insecticide resistance in mosquitoes. The knockdown resistance (kdr) occurs due to mutations in the Voltage Sensitive Sodium Channel (VSSC) or mutations in the Voltage-Gated Sodium Channel (VGSC), coded by the VSSC gene. Only three kdr mutations namely, the V1016G, S989P, and F1534C have been confirmed as commonly occurring amino acid substitutions among mosquito populations in Southeast Asia. Therefore, to extend this observation, current study was conducted to evaluate the prevalence of V1016G and F1534C mutations among Ae. aegypti mosquito populations in three different geographical regions of Sri Lanka. Immature (both pupae and larvae) stages of Ae. aegypti mosquitoes were collected from Colombo, Gampaha and Kandy districts from March to December 2018 and samples were transported to the Molecular Medicine Unit, Faculty of Medicine, University of Kelaniya. A total of 855 Ae. aegypti larvae were collected from all districts and polymerase chain reaction (PCR) assay for molecular genotyping of mutations was performed for collected all Ae. aegypti larvae (III instar), to identify the prevalence of kdr mutations in the three Ae. aegypti populations. The frequencies of the resistant and susceptible kdr alleles were determined by using the Hardy–Weinberg Equilibrium for each of the point mutation. The Ae. aegypti populations from Colombo, Gampha and Kandy districts showed 40.07% (123/307), 39.58% (114/288) and 19.58% (47/240) of V1016G and F1534C mutations, respectively. The wild type (RR) genotype remained predominant within all the three districts, whereas the homogenous (SS) mutation genotype occurred only in minority. Further, the F1534C was predominant in Ae. aegypti populations of all districts. Among the kdr mutation population, heterogeneous genotyping (RS) for both V1016G and F1534C was prominent, while SS genotyping for V1016G mutation was not observed in the Kandy district. The findings clearly denote that long-term insecticide applications and multiple use of pyrethroids has led to the progression of insecticide resistance among local Ae. aegypti populations. Therefore, evaluation of the prevalence levels of these kdr mutations highlights the necessity for shifting towards novel vector control strategies