Browsing by Author "Viswakula, S.D."

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Entomological surveillance with viral tracking demonstrates a migrated viral strain caused dengue epidemic in July, 2017 in Sri Lanka.
(Public Library of Science, 2020) Withanage, G.P.; Hapuarachchi, H.C.; Viswakula, S.D.; Gunawardene, Y.I.N.S.; Hapugoda, M.
BACKGROUND: Dengue is the most important mosquito-borne viral infection disease in Sri Lanka triggering extensive economic and social burden in the country. Even after numerous source reduction programmes, more than 30,000 incidences are reporting in the country every year. The last and greatest dengue epidemic in the country was reported in July, 2017 with more than 300 dengue related deaths and the highest number of dengue incidences were reported from the District of Gampaha. There is no Dengue Virus (DENV) detection system in field specimens in the district yet and therefore the aim of the study is development of entomological surveillance approach through vector survey programmes together with molecular and phylogenetic methods to identify detection of DENV serotypes circulation in order to minimize adverse effects of imminent dengue outbreaks. Entomological surveys were conducted in five study areas in the district for 36 months and altogether, 10,616 potential breeding places were investigated and 423 were positive for immature stages of dengue vector mosquitoes. During adult collections, 2,718 dengue vector mosquitoes were collected and 4.6% (n = 124) were Aedes aegypti. While entomological indices demonstrate various correlations with meteorological variables and reported dengue incidences, the mosquito pools collected during the epidemic in 2017 were positive for DENV. The results of the phylogenetic analysis illustrated that Envelope (E) gene sequences derived from the isolated DENV belongs to the Clade Ib of Cosmopolitan genotype of the DENV serotype 2 which has been the dominant stain in South-East Asian evidencing that a recent migration of DENV strain to Sri Lanka.
A forecasting model for dengue incidence in the District of Gampaha, Sri Lanka
(BioMed Central, 2018) Withanage, G.P.; Viswakula, S.D.; Nilmini Silva Gunawardena, Y.I.; Hapugoda, M.D.
BACKGROUND: Dengue is one of the major health problems in Sri Lanka causing an enormous social and economic burden to the country. An accurate early warning system can enhance the efficiency of preventive measures. The aim of the study was to develop and validate a simple accurate forecasting model for the District of Gampaha, Sri Lanka. Three time-series regression models were developed using monthly rainfall, rainy days, temperature, humidity, wind speed and retrospective dengue incidences over the period January 2012 to November 2015 for the District of Gampaha, Sri Lanka. Various lag times were analyzed to identify optimum forecasting periods including interactions of multiple lags. The models were validated using epidemiological data from December 2015 to November 2017. Prepared models were compared based on Akaike's information criterion, Bayesian information criterion and residual analysis. RESULTS: The selected model forecasted correctly with mean absolute errors of 0.07 and 0.22, and root mean squared errors of 0.09 and 0.28, for training and validation periods, respectively. There were no dengue epidemics observed in the district during the training period and nine outbreaks occurred during the forecasting period. The proposed model captured five outbreaks and correctly rejected 14 within the testing period of 24 months. The Pierce skill score of the model was 0.49, with a receiver operating characteristic of 86% and 92% sensitivity. CONCLUSIONS: The developed weather based forecasting model allows warnings of impending dengue outbreaks and epidemics in advance of one month with high accuracy. Depending upon climatic factors, the previous month's dengue cases had a significant effect on the dengue incidences of the current month. The simple, precise and understandable forecasting model developed could be used to manage limited public health resources effectively for patient management, vector surveillance and intervention programmes in the district.
Modelling Sri Lankan traffic accident casualties: time series count data model approach
(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Rajadasa, E.S.P.H.; Viswakula, S.D.
Traffic accident related deaths and fatalities have become the first level global health problem within the past 20 years. Accurate forecasting of the number of traffic accident casualties for a particular geographical area is very important in order to reduce the fatality rate associated with traffic accidents. Health care authorities, hospitals and emergency ambulance services can have a general idea about the number of emergency patients that can be received in given time period. Law enforcement and health care authorities can develop strategic plans to prevent traffic accidents by effectively managing road traffic. Therefore, the ultimate objective of this research was to identify the associated factors, and forecast traffic accident casualties in Sri Lanka with higher accuracy. Usually, count data are modelled with Poisson regression, and time series data are modelled with Gaussian time series modelling techniques. In order to get better forecasting accuracies, both time series and count aspects of traffic accident casualty data should be considered simultaneously. These hybrid models had been rarely used in literature due to the limited awareness and the complexity of the models. Therefore, this research was planned to introduce time series count data modelling approach for Sri Lanka traffic accident data. General time series modelling techniques such as Auto-regressive Integrated Moving Average Models and time series count data modelling techniques such as Time Series Generalized Linear Models have been compared to choose the best model. All island Sri Lankan traffic accident data for years between 2003 and 2016 that was collected by Sri Lanka Police Traffic Head Quarters, has been used to build our traffic accident forecasting model. The data set contained 29 categorical and 11 numerical variables after data cleaning. The time series count data model was able to decrease the mean absolute percentage error by 14.2% The Poisson time series count data model that was fitted using daily accumulated traffic accident casualty time series has become the overall best model. The exploratory analysis shows that there is a strong relationship between number of traffic accident casualties and the variables which indicate the geographical location of the accident such as Province or Police division. Therefore, the forecasting accuracy was further improved by fitting separate Poisson time series count data models for each Police division in Sri Lanka. For example, the root mean squared error was 3.1 for the daily road casualty forecasting model of Nugegoda police division after forecasting for 365 days. Fitting separate models for each police division holds more practical value, since the authorities can get a specific idea about small geographic area. The results of this study have further shown that the variables such as day of the week, time of the day and weather related variables do not have any significant relationship with the number of traffic accident casualties.
Multivariate spatio-temporal approach to identify vulnerable localities in dengue risk areas using Geographic Information System (GIS)
(Nature Publishing Group, 2021) Withanage, G.P.; Gunawardana, M.; Viswakula, S.D.; Samaraweera, K.; Gunawardene, N.S.; Hapugoda, M.D.
ABSTRACT: Dengue is one of the most important vector-borne infection in Sri Lanka currently leading to vast economic and social burden. Neither a vaccine nor drug is still not being practiced, vector controlling is the best approach to control disease transmission in the country. Therefore, early warning systems are imminent requirement. The aim of the study was to develop Geographic Information System (GIS)-based multivariate analysis model to detect risk hotspots of dengue in the Gampaha District, Sri Lanka to control diseases transmission. A risk model and spatial Poisson point process model were developed using separate layers for patient incidence locations, positive breeding containers, roads, total buildings, public places, land use maps and elevation in four high risk areas in the district. Spatial correlations of each study layer with patient incidences was identified using Kernel density and Euclidean distance functions with minimum allowed distance parameter. Output files of risk model indicate that high risk localities are in close proximity to roads and coincide with vegetation coverage while the Poisson model highlighted the proximity of high intensity localities to public places and possibility of artificial reservoirs of dengue. The latter model further indicate that clustering of dengue cases in a radius of approximately 150 m in high risk areas indicating areas need intensive attention in future vector surveillances.
Use of novaluron-based autocidal gravid ovitraps to control Aedes dengue vector mosquitoes in the district of Gampaha, Sri Lanka
(Hindawi Pub. Co., 2020) Withanage, G.P.; Viswakula, S.D.; Gunawardene, Y.I.N.S.; Hapugoda, M.D.
ABSTRACT:Dengue is the most important mosquito-borne viral infection in Sri Lanka causing an enormous social and economic burden in the country. In the absence of therapeutic drugs and the developed vaccines are under investigation, vector control is the best strategy to reduce the disease transmission. Therefore, the development of novel tools to control dengue vector mosquitoes has become the need of the hour. Novaluron is a recently developed Insect Growth Regulator (IGR) which inhibits chitin synthesis in immature stages of insects. The aim of the study was to identify the efficacy of a simple and cost-effective Autocidal Gravid Ovitrap (AGO) developed using Novaluron to control dengue outbreaks in the District of Gampaha, Sri Lanka. Laboratory and semifield experiments were performed to identify the activity range, optimum field dosage, and residual effects of Novaluron following the World Health Organization guidelines, and field experiments were performed in the Ragama Medical Officer of Health (MOH) area. Two study areas 800 m apart were selected and assigned as treated and control areas randomly. In each study area, 30 households were selected randomly. Each household was given two ovitraps, one placed indoors and the other placed outdoors. Mortality and survival counts were recorded separately for one-year time period and data were analyzed using a two-way repeated measures analysis of variance model. During the laboratory experiments, the adult emerging inhibition was 100% in all tested concentrations. The optimum field dosage was 2 ppm and the residual effect was 28 days. In the field experiments, significantly higher mortality counts were recorded in treated areas both indoor- and outdoor-placed AGOs. Two-factor repeated measures ANOVA followed by Tukey's test confirmed that the mean mortality count is high for the developed AGOs both indoor and outdoor settings. The developed AGO can be deployed to control both indoor and outdoor dengue vector mosquito populations, and in dengue-risk areas, the ovitrap will be supportive to local health authorities to enhance the efficiency of future vector control programs.