IPRC - 2019
Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/20881
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Item A Fuzzy Linear Model Using Possibilistic Linear Regression with Least Squares Method: An Application to Dengue and Rainfall Data(International Postgraduate Research Conference 2019, Faculty of Graduate Studies, University of Kelaniya, Sri Lanka, 2019) Attanayake, A.M.C.H.; Perera, S.S.N.; Liyanage, U.P.Fuzzy linear models deal with vague and imprecise phenomenon in order to represent better models. These type of models are especially suitable in modelling and predicting dengue disease as the disease associated with various unknown and uncontrollable factors. Further, modelling and predicting the dengue disease is important as it is one of the leading diseases in the world which reports higher number of deaths. This study focuses on modelling reported dengue cases in the Colombo district, Sri Lanka. Particularly, Possibilistic Linear Regression with Least Squares (PLRLS) Method was applied as the modelling procedure. This method was proposed by H. Lee and H. Tanaka in 1999 to deal with crisp inputs and fuzzy output. The rainfall as one of the leading climatic factors that associated with dengue disease included in the model as an independent variable. Data consists of weekly reported dengue cases and weekly average rainfall in the Colombo district from 46th week of 2009 to 12th week of 2015. 2009 to 2014 data were used for model development and rest of the data for model validation. Cross correlation analysis revealed that the rainfall with 10 lags was associated with the reported dengue cases. By considering dengue and rainfall data as crisp inputs, the upper approximation model and lower approximation model were obtained to reflect the fuzziness of the dengue count in the district. The developed coefficients of the fuzzy linear regression were in the form of non-symmetric triangular fuzzy numbers. The left and the right spreads of the central value determined the lower and upper boundary of the interval, respectively, where the corresponding degree of membership equals to 0. The predicted values from the fuzzy regression model and the actual values of the validation set were within the upper and lower approximation models which indicated the possibility of the dengue prediction through PLRLS method. The authors are in the process of testing additional fuzzy linear models by changing fuzzy input/output combinations with incorporating more independent variables.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 Assessment of Possible Risk Factors Affecting Transmission of Dengue in the District of Gampaha Based on Reported Dengue Cases(International Postgraduate Research Conference 2019, Faculty of Graduate Studies, University of Kelaniya, Sri Lanka, 2019) Perera, E.H.L.; Viswakula, S.; Gunawardene, Y.I.N.S.; Subasinghe, U.; Hapugoda, M.D.Dengue is a fast spreading arboviral infection transmitted by the bite of infected females of Aedes aegypti (Linnaeus) and Ae. albopictus (Skuse). According to the Epidemiology Unit, the second highest number of dengue cases is reported in the District of Gampaha, Sri Lanka over past ten years. Objective of this study was to investigate the entomological and socio-economic risk factors affecting transmission of dengue in laboratory-confirmed dengue case reported stations in the District of Gampaha. Laboratory confirmed positive dengue patients (n=100) by dengue NS1 antigen test during the period of June, 2018-August, 2019 were selected. Entomological surveillance was conducted by visiting to each patient within one week of notification of a positive case. For the collection of socio-economic data, an interviewer-administrated questionnaire was used. Adult Aedes mosquito samples collected using a back-pack aspirator showed, 98.64% (73/74) of Ae. albopictus and 1.35% (1/74) of Ae. aegypti mosquitoes. Larval collection using standard larval surveillance techniques showed 92.96% (185/199) and 7.04% (14/199) of Ae. albopictus and Ae.aegypti larvae respectively. The highest House Index (55.17%-16/29), Container Index (28.89%-13/45) and Breteau Index (44.83%-13/29) were reported in the month of June, 2019. The major Aedes breeding place was identified as plastic buckets/barrels (48.6%-84/173) that being used to discard waste. Piped borne water (88%-88/100) was the major water source of the house-holds. Water source of tube well (9%-9/100) was the next popular water source and 66.67%(6/9) of tube wells were positive breeding places for Aedes larvae. Average homestead of the premises of dengue patients was 16.14 perches. From the 100 dengue cases, 67 cases were from middle of town areas, while 2 were from rural areas. Vegetation coverage of the 78% (78/100) house-holds were grass, bushes and small trees and 3% (3/100) house-holds didn’t have any vegetation coverage. The major mosquito prevention method was usage of mosquito nets (54%-54/100) and among dengue patients 7% (7/100) of dengue patients weren’t using any mosquito prevention method. High density of Ae. albopictus mosquitoes, was reported although Ae. aegypti is the major vector of dengue. Therefore, it is required to draw more attention about the Ae. albopictus breeding sites in dengue control programmes. Participants from the study sites were well aware about the disease but still there is a lack of knowledge on breeding sites and vector control methods. Drawbacks in the waste disposal methods, lack of cleanliness in gardens, unplanned water sources and neglecting preventive actions could be considered as the possible risk factors.