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Item Investigating the effect of climate factors on dengue incidence in Kandy, Sri Lanka(Faculty of Science, University of Kelaniya Sri Lanka, 2023) Madhumali, H. A. N.; Varathan, N.Dengue, a mosquito-borne disease, poses significant public health challenges in tropical and subtropical regions globally. About two-thirds of the world’s population live in areas infected with dengue. It is one of the major emerging public health problems locally as well as globally. Dengue has been hyper-endemic in Sri Lanka in recent years. According to the epidemiology unit of Sri Lanka, dengue fever and the more severe dengue hemorrhagic fever became nationally notifiable diseases in Sri Lanka in 1996. The prevalence of dengue infections on a yearly basis has been increasing over time. Now it has become the leading killer mosquito infection in Sri Lanka. In urban areas, the dengue incidence is the highest, notably highest in Colombo and Gampaha districts. Currently, Kandy district is the third highest-risk area for dengue transmission in the country. The incidence of dengue was caused by several factors, one of which is the climatic conditions referring to temperature, rainfall, and humidity were reported to be important influential dengue transmitters. Since climate conditions influence the dengue transmission cycle, the relationship between dengue incidence and climatic conditions is investigated in this study. This study focuses on developing a suitable statistical model that describes the relationship between dengue incidence and meteorological factors such as temperature, humidity and rainfall in Kandy. Since the dengue incidence is a count data, the Poisson regression approach is considered to fit the model. For this study, monthly dengue incidences in the city of Kandy from 2007 to 2019 were obtained from the epidemiology unit of the Ministry of Health of Sri Lanka. The monthly climate data in the city of Kandy (monthly average temperature in o C, monthly average humidity, and monthly average rainfall in mm for the same period) were obtained from yearly statistical abstracts from the Department of Census and Statistics of Sri Lanka. Since the data was identified as over-dispersed, which has higher variance than the mean value, the negative binomial model was incorporated. Finally, models were compared with respect to the deviance values and the Akaike Information Criteria. Results reveal that the negative binomial model is the best-fitted model for the data. Further, rainfall was identified as the most significant variable for the dengue incidence in Kandy. The results of this research may help to improve the precaution strategies against dengue incidence in the near future. That is, the relevant authority may alert the public during the rainy season and do pre- cleaning activities of the environment.Item Time series modeling of red onion production in Jaffna, Sri Lanka(Research Symposium on Pure and Applied Sciences, 2018 Faculty of Science, University of Kelaniya, Sri Lanka, 2018) Mirojan, U.; Varathan, N.; Arumairajan, S.Onion is one of the most important commercial vegetable crops grown in Sri Lanka. Observing fluctuation of onion production is essential in the market economy. The level of the production and the fluctuation not only has a significant influence on farmers and consumers, but also a reasonable effect on the safe running of the onion in market. In this study, the annual production of red onion in Jaffna is modeled by using Box – Jenkins time series approach. The Onion production in Jaffna is cultivated in two seasons, Maha season: from September to March, Yala season: from April to August. The annual seasonal red onion production data was obtained from the office of the Deputy Provincial Director of Agriculture (Extension) during the period of 1987 to 2016. The main objective of this study is to find the suitable Auto Regressive Integrated Moving Average (ARIMA) model for the annual production of Red onion in Jaffna. Further, three statistical criteria such as Akaike’s information criteria, Bayesian information criteria, mean squared error were carried out in order to select the best ARIMA model. Through the modeling, it was identified that ARIMA (1,1,0) is the best fitting model to the given data. Moreover, the model validation has been done using the actual figures. Further, the identified best model can be used to predict the red onion production of Jaffna in near future.Item Statistical modelling of monthly electricity sales in Colombo: ARIMA approach(Research Symposium on Pure and Applied Sciences, 2018 Faculty of Science, University of Kelaniya, Sri Lanka, 2018) Herath, H. M. R. D. S.; Varathan, N.Electricity is the most essential form of energy, used all over the globe. It has influenced the economy, public health, technological growth and all other spheres of human activities. The electricity sales are growing day by day with the population growth and industrialization, etc. Even though Sri Lanka is a developing country, it has shown a huge progress, showing a national electrification ratio of 99.7% in 2017. Colombo; the capital of Sri Lanka, is the main commercial hub with the largest population and by far the most developed city in Sri Lanka. This study investigates to develop a suitable time series model for the monthly electricity sales of Colombo City. The monthly electricity sales data was obtained from Ceylon Electricity Board during the period of January 1982 to December 2016. The data analysis has been done using the Box-Jenkin’s Auto Regressive Integrated Moving Average (ARIMA) procedure. Results reveal that, the SARIMA (0,1,2)(0,1,1)12 is the most appropriate model for the monthly electricity sales data. Moreover, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Mean Square Error (MSE) were used to select the best model. Further, adequacy of the best model has been checked using Ljung-Box Chi-Squared test. Finally, the monthly electricity sales for the year 2017 were predicted using the selected best model.Item Factors associated with low birth weight babies in Jaffna, Sri Lanka.(International Research Symposium on Pure and Applied Sciences, 2017 Faculty of Science, University of Kelaniya, Sri Lanka., 2017) Nimantha, D. J.; Varathan, N.Low birth weight (LBW) is a major public health problem in many countries including Sri Lanka. It is a leading cause of prenatal and neonatal deaths resulting in severe short term and long term effect on babies. The World Health Organization specify LBW as a condition where the weight at birth of an infant is less than 2500 grams. Recent statistics from the Family Health Bureau website shows, in year 2016, about 11.2% of infants were born with low birth weight in Sri Lanka and nearly 9.25% infants were born with low birth weight in Jaffna district. This study aims to identify the significant factors associated with the low birth weight infants in Jaffna. The required data was obtained from the Jaffna Teaching Hospital during the period of January 1, 2016, to December 31, 2016, which consists the information of 420 low birth weight infants. The multiple linear regression technique was used to model the data and stepwise regression was applied to identify the best fitting model by means of Mean Square Error (MSE), Akaike Information Criteria (AIC) and Bayesian Information Criteria. Results from this study reveal that significant association among gestational age, maternal weight, fetal number, pregnancy experience, previous LBW history, and maternal body mass index (BMI) with low birth weight. Further, no significant association was found among baby’s gender and mode of delivery with LBW in this study. The findings of this study may be useful to reduce the LBW by improving the knowledge of expecting mothers and practice for a healthy pregnancy in future.