Statistics & Computer Science
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Item Vaccination Coverage for COVID-19 in Sri Lanka: With and Without Age Stratification on Susceptible-Infectious-Recovered Simulation(Journal of Occupational Health and Epidemiology,, 2022) Attanayake, A.M.C.H.Background: Vaccination against COVID-19 is as a key solution to interrupt its spread. This study aimed to describe the vaccination coverage required to stop the spread of COVID-19 in Sri Lanka using a mathematical modeling strategy. Materials & Methods: This longitudinal study used age-stratified and unstratified Susceptible-Infectious-Recovered (SIR) models. Data on the population's age distribution were acquired from the census report of the Census and Statistics Center of Sri Lanka, consisting of groups: below 30, between 30-59, and over 60. Models with differential equations forecasted the spread of COVID-19 with vaccination based on parameter estimates and numerical simulation, assuming fixed population, infection, and recovery rates. Results: Simulations investigated how the susceptible, infected, and recovered populations varied according to the different vaccination coverages. According to the results, 75% vaccination coverage was required in the entire population of Sri Lanka to interrupt the transmission of COVID-19 completely. The age-stratified SIR model showed that over 90% of vaccination coverage in each age group (below 30, between 30-59, and over 60) was required to interrupt the transmission of COVID-19 in the country altogether. Conclusions: The number of COVID-19 infections in each age group of Sri Lanka reduces with the increase in vaccination coverage. As 75% vaccination coverage is required in Sri Lanka to interrupt the transmission of the disease, precise vaccination coverage measurement is essential to assess the successfulness of a vaccine campaign and control COVID-19.Item MODELING COVID-19 CASES IN SRI LANKA USING ARIMA MODELS(The Open University Of Srilanka., 2020) Attanayake, A.M.C.H.; Perera, S.S.N.COVID-19 (Novel Coronavirus) is a pandemic which spread around the world at an alarming rate. As of 10th June 2020, 1,880 infections and 11 deaths were reported in Sri Lanka due to COVID-19. The number of infections increase day by day requiring research on modelling the pandemic. Modelling of COVID 19 cases will be useful to understand the behavioural patterns of the disease and hence to identify control mechanisms. The aim of this study is to model and predict the daily cumulative COVID-19 cases in Sri Lanka. Autoregressive Integrated Moving Average (ARIMA) technique was applied to model the reported COVID-19 cases in Sri Lanka. Data from 11th March - 1st of June 2020 were used for the model development and data from 2nd - 10th June 2020 (10% of data) were used for model validation. In the analysis, second order differencing removed the non-stationarity of the original series. Different candidate ARIMA models were tested based on ACF and PACF plots and the best ARIMA model was selected based on minimum AIC and BIC measures. The most appropriate ARIMA model for the COVID-19 cases in Sri Lanka is ARIMA (2,2,2). After verifying the assumptions of the model, MAPE of the validation set revealed 1.86%. Therefore, the selected most appropriate model was used to forecast the future COVID-19 cases in Sri Lanka. According to the forecasted values of the model, it can be concluded that COVID19 cases in Sri Lanka will increase slowly in the upcoming days. ARIMA technique is appropriate in only short-term forecasting. Availability of an effective prediction model will be helpful in anticipating the cases and to take timely action to control the COVID-19 incidence. Unexpected recordings cannot be modelled and predicted by the fitted models. Uncertainties limit the effectiveness of a model, specially, in an epidemic like novel coronavirus.Item COMPARISON OF PERFORMANCES OFSELECTED FORECASTING MODELS:AN APPLICATION TO DENGUE DATA IN COLOMBO, SRI LANKA(Department of Statistics & Computer Science, Faculty of Science,& Research & Development Centre for Mathematical Modelling, Faculty of Science, University of Colombo, Sri Lanka., 2021) Attanayake, A.M.C.H.; Perera, S.S.N.; Liyanage, U.P.Dengue is a one of the diseases in the world which has no exact treatment to recover from the disease. It is rapidly spreading throughout the world by causing large number of deaths [1]. In Sri Lanka, there is an increase of reported dengue cases over recent years. The majority of dengue cases reported in the Colombo district within the Sri Lanka. Effective dengue management and controlling strategies should be implemented to reduce the deaths from the disease. Modelling and predicting the distribution of the dengue will be useful in detecting outbreaks of the dengue and to execute controlling actions beforehand. The objective of this study is to develop an appropriate modelling technique to predict dengue cases. To accomplish this objective, we have chosen our study area as Colombo, Sri Lanka. Seven modelling techniques, namely, Na¨ıve, Seasonal Na¨ıve, Random Walk with Drift, Mean Forecasting, Autoregressive Integrated Moving Average, Exponential Smoothing and TBATS (Trigonometric, Box-Cox Transformation, ARMA errors, Trend and Seasonal components) [2] were chosen in this study to model dengue data. For model development process, monthly reported dengue cases in Colombo from January 2010 to December 2018 were used and validated using the data from January to December in 2019. Mean error, root mean squared error and mean absolute percentage error measurements were used to select the most parsimonious model to predict dengue cases in Colombo, Sri Lanka. Both Exponential and TBATS models were competed in predicting dengue cases by reporting minimum error measures. Therefore, results disclosed that among the selected methods either Exponential Smoothing model or TBATS model can be used to predict dengue cases in Colombo, Sri Lanka.Item Identifying Factors Associated with Price Categories of Motorcycles in Sri Lanka using Discriminant Analysis(Department of Statistics & Computer Science, University of Kelaniya, Sri Lanka, 2021) Chanika, P.A.L.; Attanayake, A.M.C.H.; Gunaratne, M.D.N.The motorcycle is one of the most popular modes of transportation in Asia because it is a fast, economic and convenient mode of transportation. According to the motor vehicle registration statistics of the department of motor traffic, motorcycle is the best-selling vehicle in Sri Lanka. Pricing of the motorcycles is one of the major concerns for motorcycle byers and importers. The purposes of this study are to identify the factors which are associated with the prices of motorcycles and to construct a model to determine the price of a motorcycle based on significant factors. Specifications and features of selected motorcycles were collected from secondary data sources. ‘Motorcycle Category’, ‘Fuel Efficiency’, ‘Engine Power’, ‘Brake System’, ‘Torque’, ‘Length’ were the significant factors which had an association between the price categories of motorcycles among 15 properties of motorcycles considered under study. The canonical discriminant analysis was used to develop the model and to predict the price categories of motorcycles. The significant categorical variables were used as the dummy variables. Torque was omitted from the model because of the presence of multicollinearity. The two significant discriminant functions were classified the data into four categories of price. The 75% responds were correctly classified into four price categories. Motorcycle category, Fuel Efficiency, Engine Power, Brake System and Length were found as significantly associated factors to the prices of motorcycles. Decision makers in the field can make good use of those factors in developing their pricing strategies in the motorcycle industry