Browsing by Author "Perera, S. S. N."

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Factors associated with induction of labour and pregnancy outcomes in 14 healthcare facilities in Sri Lanka
(Journal of Science 2019, Department of Statistics & Computer Science, Faculty of Science, University of Kelaniya, Sri Lanka, 2019) Gunawardana, J. R. N. A.; Perera, S. S. N.
Induction of Labour (IOL) is an important practice that is carried out commonly in modern day obstetrics. In medium to large healthcare facilities in Sri Lanka, it is estimated that approximately 35.5% of all deliveries involve IOL. This research attempts to identify the factors that affect IOL and to assess the association between IOL and the pregnancy outcome. In this study, we considered 18309 women who were admitted to 14 healthcare facilities for delivery in 3 randomly selected provinces in Sri Lanka (Western, Southern and Eastern provinces), during July to October 2011. Multinomial Logistic Regression model (MLR) and Fuzzy Expert System (FES) were used to identify the factors that lead to IOL. The MLR model predicts IOL with a classification rate of 65.5% and the FES predicts IOL with an accuracy of 55.10%. 1Maternal age, number of previous caesarian sections,number of previous births, estimated gestational age, Pre-Eclampsia, number of previous pregnancies, Placenta Preavia, Abruption Placenta, total number of neonates delivered, birth weight and Maternal Severity Index (MSI) were identified as factors associated with IOL. Neonatal status after seven days of life can also be predicted using the developed FES. FES is predictive of IOL and birth outcome, where if the FES score is between 0.8570 and 0.8854, the patient will belong to the induced group and the baby would be alive after seven days of birth. This study concludes that, MLR and FES models can be used to predict IOL outcomes. These findings can be informative to healthcare providers when counselling women for labour induction and develop evidence-based protocols on IOL.
Factors that affect labour induction and its successfulness of pregnancies in Sri Lanka.
(International Research Symposium on Pure and Applied Sciences, 2017 Faculty of Science, University of Kelaniya, Sri Lanka., 2017) Gunawardana, J. R. N. A.; Perera, S. S. N.
Induction of Labour (IOL) is an important practice that is carried out commonly in modern day obstetrics. In medium to large health care facilities, it is estimated that approximately 35.5% of all deliveries involve IOL in Sri Lanka. The main objective of this study, was to identify the factors that affect IOL and to assess the association between IOL and each pregnancy outcome. In this study, we consider data of all women who were admitted to selected health care facilities for delivery in 3 randomly selected provinces in Sri Lanka, for the period from July to October 2011. Multinomial Logistic Regression model (MLR) and Fuzzy Expert System were used to identify the factors that affect IOL. MLR model predicts for spontaneous labour group and induced labour group, with reference to no labour (caesarean section/C-section) category. Obtained score under Fuzzy Expert System was appropriate to distinguish whether an individual should go through IOL or not. It also can be used to identify whether a new born would survive after seven days of life. The MLR model predicts for IOL with a classification rate of 65.5% and the Fuzzy Expert System predicts for IOL with an accuracy of 55.10%. Results indicated that IOL was related to maternal age, number of previous caesarian sections, number of previous births, estimate gestational age, number of previous pregnancies, PreEclampsia, Placenta Preavia, Abruption Placenta, total number of neonates delivered, birth weight and Maternal Severity Index (MSI). Fuzzy Expert System also states that, if the score is between 0.8570 and 0.8854, then the patient will belong to induced group and the baby would be alive after seven days of birth. This study concludes that, MLR and Fuzzy models can be used to deal with decision making procedures related to IOL.
Forecasting COVID-19 Cases Using Alpha-Sutte Indicator: A Comparison with Autoregressive Integrated Moving Average (ARIMA) Method
(Department of Statistics & Computer Science, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Attanayake, A. M. C. H.; Perera, S. S. N.
COVID-19 is a pandemic which has spread to more than 200 countries. Its high transmission rate makes it difficult to control. To date, no specific treatment has been found as a cure for the disease. Therefore, prediction of COVID-19 cases provides a useful insight to mitigate the disease. This study aims to model and predict COVID-19 cases. Eight countries: Italy, New Zealand, the USA, Brazil, India, Pakistan, Spain, and South Africa which are in different phases of COVID-19 distribution as well as in different socioeconomic and geographical characteristics were selected as test cases. The Alpha-Sutte Indicator approach was utilized as the modelling strategy. The capability of the approach in modelling COVID-19 cases over the ARIMA method was tested in the study. Data consist of accumulated COVID-19 cases present in the selected countries from the first day of the presence of cases to September 26, 2020. Ten percent of the data were used to validate the modelling approach. The analysis disclosed that the Alpha-Sutte modelling approach is appropriate in modelling cumulative COVID-19 cases over ARIMA by reporting 0.11%, 0.33%, 0.08%, 0.72%, 0.12%, 0.03%, 1.28%, and 0.08% of the mean absolute percentage error (MAPE) for the USA, Brazil, Italy, India, New Zealand, Pakistan, Spain, and South Africa, respectively. Differences between forecasted and real cases of COVID-19 in the validation set were tested using the paired t-test. The differences were not statistically significant, revealing the effectiveness of the modelling approach. Thus, predictions were generated using the Alpha-Sutte approach for each country. Therefore, the Alpha-Sutte method can be recommended for short-term forecasting of cumulative COVID-19 incidences. The authorities in the health care sector and other administrators may use the predictions to control and manage the COVID-19 cases
Identification of the patterns of dengue disease transmission: A Wavelet approach
(Research Symposium on Pure and Applied Sciences, 2018 Faculty of Science, University of Kelaniya, Sri Lanka, 2018) Nanayakkara, K. A. D. C. N.; Perera, S. S. N.
Dengue is transmitted over the human population by the mosquitoes Aedes aegypti and Aedes albopictus. This comes in three forms: Dengue Fever (DF), Dengue Hemorrhagic Fever (DHF) and Dengue Shock Syndrome (DSS). Sri Lanka has been suffered by dengue fever and DHF epidemics for over two decades and now the risk has been increased rapidly mainly in urban areas. In the context of Sri Lanka, during 2017, 186101 dengue cases have been reported from all over the island and approximately 35.2% of dengue cases were reported from the Western province. In the past few decades, many studies on vector control, the molecular biology of the virus, vaccine development and the pathogenesis of dengue hemorrhagic fever/dengue shock syndrome have been conducted. But comparatively, little effort has been directed towards identifying patterns of dengue transmission. That is because of dengue transmission mechanism is complex as it is based on several external factors such as climate, geography and human mobility. The main objective of this study is to focus on identification of Dengue disease transmission patterns in Colombo area using wavelet transformation. Particularly, the study focuses on identification of periodicity of outbreaks of dengue data and their frequency and intensity. Further to analyze the El Niño effect on dengue cases in Colombo area and to describe the impact of climatic factors such as rainfall and temperature. Wavelet analysis has been used to identify the patterns of dengue transmission. It is a powerful mathematical tool, which performs time-frequency decomposition of the signals, and estimates the spectral characteristics as a function of time. Cross-wavelet transform and wavelet coherence will be used to examine relationships in time-frequency space between two-time series. The wavelet power spectrum was obtained from weekly reported dengue cases in Colombo from 2006 to 2017, and significant regions were observed in the spectrum corresponding to approximately 26 week cycles during mid of 2014 to 2017. The cross wavelet power spectrum revealed that there is a similar strong link between how rainfall and temperatures resulting from the reoccurring El Niño phenomenon are associated with elevated risks of dengue epidemics from 2010 to 2012 and 2015 to 2017. Further using cross wavelet power spectrums, it was observed that to develop a prediction model for dengue transmission, rainfall and minimum temperature play a major role.
Predicting dengue incidences using rainfall and temperature
(Research Symposium on Pure and Applied Sciences, 2018 Faculty of Science, University of Kelaniya, Sri Lanka, 2018) Pushpakanthi, U. B. N.; Perera, S. S. N.
Dengue fever is a serious illness caused by a virus, which is transmitted through the infected female mosquito, namely Aedes aegypti (principal vector) and Aedes albopictus, through bites or blood meals on human hosts. Dengue is a major public health problem in most countries in tropical regions and it has spread rapidly in many parts of the world including Sri Lanka. The urban population in Sri Lanka is highly vulnerable while Colombo District is at high risk. This study focuses on the dengue cases reported around Colombo Municipal Council (CMC) in Sri Lanka. The aims of this study are to identify the lags for explanatory variables which are affected the dengue incidence most, and to identify a distribution of weekly dengue cases. The explanatory variables are average rainfall per week, average of the maximum temperature per week and average of the minimum temperature per week. Weekly dengue incidents from January 2009 to October 2017 in CMC were considered. By applying the cross correlation analysis, it showed that the average of the maximum temperature per week and the average rainfall per week have a significant influence to occurrence of dengue cases after 10 weeks their occurrences. Therefore, the best-lags were ten weeks for both weekly average maximum temperature and weekly average rainfall and, best-lag for weekly average minimum temperature was zero. The Negative Binomial regression model was used in this study. The number of dengue cases per week in CMC area followed negative binomial distribution given that the average rainfall and average maximum temperature before 10 weeks. This predictive distribution can be used as an early warning signal so that public health officials can be prepared in advance to minimize the disease burden.
Proactive Dengue Management System Synergize by an Exponential Smoothing Model
(Research & Development Centre for Mathematical Modelling,Department of Mathematics, University of Colombo,Sri Lanka, Department of Statistics & Computer Science,Faculty of Science, University of Kelaniya, Kelaniya, Sri Lanka., 2021) Wetthasinghe, W. A. U. K.; Attanayake, A. M. C. H.; Liyanage, U. P.; Perera, S. S. N.
In a critical area like health sector centralized computer system helps to improve the efficiency of the health system. In particular, controlling an epidemic is usually difficult in developing countries. In this study we introduce a multi-platform, centralized proactive management system to manage dengue controlling activities in Sri Lanka. The system make common platform (ProDMS) for all sectors who contribute their services for mitigating dengue [1]. We mainly focused to the special feature of the system which enhance the centralized property. Cross platform environment was developed under this feature as a bridge to connect researches and general public. ProDMS is a internet base web application and researches can plug their dengue forecasting models to the system and publish their outputs as graphs through the web system. The ProDMS web application, which consisting of plug and play system architecture concepts, fully support for any statistical or mathematical model to publish its results online. In this work we use one of the univariate time series modelling approaches; namely exponential smoothing to plug with the system. This research helps to enhance efficiency of Dengue controlling process and support to generalize centralization.