Siriwan Wongkoon's scientific contributionswhile working at Walailak University (Nakhon Si Thammarat, Thailand) and other institutions

Publications (16)

Publications citing this author (107)

    • It has been extensively applied to model time series especially those which show evidence of seasonality. For instance, Wongkoon et al. [3] modelled monthly DHF incidence in Northern Thailand as a SARIMA(2,0,1)x(0,2,0) 12 model, Shitan et al. [4] modelled Bangladesh export values by a SARIMA(1,1,0)x(0,1,1) 12 , Cuhadar [5] forecasted tourism demand to Istanbul using a SARIMA(2,0,0)x(1,1,0) 12 , Asamoah-Boaheng [6] fitted a SARIMA(2,1,1)x(1,1,2) 12 model to monthly mean surface air temperature in the Ashanti Region of Ghana and Alvarez-Diaz and Gupta [7] proposed a SARIMA(3,1,1)x(2,1,2) 12 forecasting model for the United States Consumer Price Indices . Some others who have applied SARIMA modelling in recent times are Papmichail and Georgiou [8], Brida and Garrido [9], Khajavi et al. [10], Mombeni et al. [11] and Maarof et al. [12], to mention but a few.
    [Show abstract] [Hide abstract] ABSTRACT: Many economic and business time series exhibit seasonal tendencies. Analytical techniques for such series which take into account these tendencies have engaged the attention of researchers of recent. One such modelling technique is the Box-Jenkins seasonal autoregressive integrated moving average (SARIMA) technique. A novel algorithm is hereby proposed. This algorithm which is based on autoregressive-moving average duality arguments is applied to model daily exchange rates of the British pound sterling and the European Euro currencies. The data analyzed are 178 daily pound/euro exchange rates 13 th December 2015 to 7 th June 2016. Application of the algorithm using the SARIMA(1,1,1)x(1,1,1) 7 template as proposed yields a SARIMA(1,1,1)x(0,1,1) 7 model. Further 8 values from 8 th June to 15 th June 2016 are used for out-of-sample comparison of observations with forecasts. The adequacy of the chosen model is not in doubt since the residuals are uncorrelated and are normally distributed. Moreover out-of-sample forecasts closely agree with the observed values. This additive-multiplicative model may be used for forecasting and simulation purposes.
    Full-text · Article · Jan 2016
    • The MOAAS has integrated Google Earth™ and Google Maps™ to display geospatial information, showing mosquito larval distribution in 3D bar charts on Google Earth™ and Google Maps™, which has helped scientists, teachers, and students visualize the dengue risk areas. Previous findings showed topography, season, and type of water container impacted the number of Aedes larvae in Thailand (Thavara et al, 2001; Wongkoon et al, 2005 Wongkoon et al, , 2007). Together with the mosquito larvae data in the system, a 3D bar chart can be created in Google Earth™ and Google Maps™ as an interactive map that displays the number of mosquito larvae, and the monthly number of mosquito larvae, in different types of water containers.
    [Show abstract] [Hide abstract] ABSTRACT: The Mosquito Online Advanced Analytic Service (MOAAS) provides an essential tool for querying, analyzing, and visualizing patterns of mosquito larval distribution in Thailand. The MOAAS was developed using Structured Query Language (SQL) technology as a web-based tool for data entry and data access, webMathematica technology for data analysis and data visualization, and Google Earth and Google Maps for Geographic Information System (GIS) visualization. Fifteen selected schools in Thailand provided test data for MOAAS. Users performed data entry using the web-service, data analysis, and data visualization tools with webMathematica, data visualization with bar charts, mosquito larval indices, and three-dimensional (3D) bar charts overlaying on the Google Earth and Google Maps. The 3D bar charts of the number of mosquito larvae were displayed along with spatial information. The mosquito larvae information may be useful for dengue control efforts and health service communities for planning and operational activities.
    Full-text · Article · Jul 2013
    • Wet season rains create more breeding habitats, and elevated humidity levels extend the lifespan of adults, thus prolonging disease transmission rates[3]. For example, dengue outbreaks regularly coincide with wet seasons in Brazil, Thailand and Australia[19][20][21]. Mosquito-borne diseases such as malaria, yellow fever and chikungunya are thriving worldwide, especially in the tropics.
    [Show abstract] [Hide abstract] ABSTRACT: Background: Anthropogenic land use changes have contributed considerably to the rise of emerging and re-emerging mosquito-borne diseases. These diseases appear to be increasing as a result of the novel juxtapositions of habitats and species that can result in new interchanges of vectors, diseases and hosts. We studied whether the mosquito community structure varied between habitats and seasons and whether known disease vectors displayed habitat preferences in tropical Australia. Methods: Using CDC model 512 traps, adult mosquitoes were sampled across an anthropogenic disturbance gradient of grassland, rainforest edge and rainforest interior habitats, in both the wet and dry seasons. Nonmetric multidimensional scaling (NMS) ordinations were applied to examine major gradients in the composition of mosquito and vector communities. Results: We captured ~13,000 mosquitoes from 288 trap nights across four study sites. A community analysis identified 29 species from 7 genera. Even though mosquito abundance and richness were similar between the three habitats, the community composition varied significantly in response to habitat type. The mosquito community in rainforest interiors was distinctly different to the community in grasslands, whereas forest edges acted as an ecotone with shared communities from both forest interiors and grasslands. We found two community patterns that will influence disease risk at out study sites, first, that disease vectoring mosquito species occurred all year round. Secondly, that anthropogenic grasslands adjacent to rainforests may increase the probability of novel disease transmission through changes to the vector community on rainforest edges, as most disease transmitting species predominantly occurred in grasslands. Conclusion: Our results indicate that the strong influence of anthropogenic land use change on mosquito communities could have potential implications for pathogen transmission to humans and wildlife.
    Full-text · Article · Dec 2016
    • The prediction error was close to that found in another previous study that used ARIMA (3,1,4) model to forecast dengue incidence in North-Eastern Thailand with a margin of error of 7 % (i.e. MAPE = 7.0) [36]. The difference could be attribute to the fact that our predictions covered a slightly longer period of 60 months compared to 40 months in the previous study.
    [Show abstract] [Hide abstract] ABSTRACT: Background Predicting future prevalence of any opportunistic infection (OI) among persons infected with the human immunodeficiency virus (HIV) on highly active antiretroviral therapy (HAART) in resource poor settings is important for proper planning, advocacy and resource allocation. We conducted a study to forecast 5-years prevalence of any OI among HIV-infected individuals on HAART in Uganda. Methods Monthly observational data collected over a 10-years period (2004–2013) by the AIDS support organization (TASO) in Uganda were used to forecast 5-years annual prevalence of any OI covering the period 2014–2018. The OIs considered include 14 AIDS-defining OIs, two non-AIDS defining OIs (malaria & geohelminths) and HIV-associated Kaposi’s sarcoma. Box-Jenkins autoregressive integrated moving average (ARIMA) forecasting methodology was used. ResultsBetween 2004 and 2013, a total of 36,133 HIV patients were enrolled on HAART of which two thirds (66 %) were female. Mean annual prevalence for any OI in 2004 was 57.6 % and in 2013 was 27.5 % (X2trend = 122, b = −0.0283, p <0.0001). ARIMA (1, 1, 1) model was the most parsimonious and best fit for the data. The forecasted mean annual prevalence of any OI was 26.1 % (95 % CI 21.1–31.0 %) in 2014 and 15.3 % (95 % CI 10.4–20.3 %) in 2018. Conclusions While the prevalence of any OI among HIV positive individuals on HAART in Uganda is expected to decrease overall, it’s unlikely that OIs will be completely eliminated in the foreseeable future. There is therefore need for continued efforts in prevention and control of opportunistic infections in all HIV/AIDS care programmes in these settings.
    Full-text · Article · Aug 2016
    • Our study shows that temperature with three-month lead-time and rainfall with two and three-month lead-time is the best predictors for dengue transmission patterns in Yogyakarta Municipality. The time delay between temperature and rainfall with corresponding influences on the dengue cases have shown in others province in Indonesia [36] and neighbor countries [27,37] as well. This time delays are likely to represent biological processes in the vector life cycle [19,20].
    [Show abstract] [Hide abstract] ABSTRACT: Research is needed to create early warnings of dengue outbreaks to inform stakeholders and control the disease. This analysis composes of a comparative set of prediction models including only meteorological variables; only lag variables of disease surveillance; as well as combinations of meteorological and lag disease surveillance variables. Generalized linear regression models were used to fit relationships between the predictor variables and the dengue surveillance data as outcome variable on the basis of data from 2001 to 2010. Data from 2011 to 2013 were used for external validation purposed of prediction accuracy of the model. Model fit were evaluated based on prediction performance in terms of detecting epidemics, and for number of predicted cases according to RMSE and SRMSE, as well as AIC. An optimal combination of meteorology and autoregressive lag terms of dengue counts in the past were identified best in predicting dengue incidence and the occurrence of dengue epidemics. Past data on disease surveillance, as predictor alone, visually gave reasonably accurate results for outbreak periods, but not for non-outbreaks periods. A combination of surveillance and meteorological data including lag patterns up to a few years in the past showed most predictive of dengue incidence and occurrence in Yogyakarta, Indonesia. The external validation showed poorer results than the internal validation, but still showed skill in detecting outbreaks up to two months ahead. Prior studies support the fact that past meteorology and surveillance data can be predictive of dengue. However, to a less extent has prior research shown how the longer-term past disease incidence data, up to years, can play a role in predicting outbreaks in the coming years, possibly indicating cross-immunity status of the population.
    Full-text · Article · Mar 2016
    • This figure indicating the mosquito's dynamic live circle was complete. Environment factors such as temperature, humidity in Wonosobo is adequate to support life circle process [10]. The original habitat of Ae. albopictus is forest and have adapted to live in the land use for agriculture surrounding human settlement [11, 12].
    [Show abstract] [Hide abstract] ABSTRACT: The increased cases of dengue fever have occurred in the highland of Wonosobo District, and the epidemic taken place in 2009 had 59.3 cases per 100,000 populations. This study aimed to describe of vector competence of the mosquitoes as a dengue vector in the highland of Wonosobo District, Central Java Province. The serial laboratory work was done to measure of vector competence complementary with vector bionomic study. The samples were 20 villages, which were located at Wonosobo sub district. Every village was observed about 15-20 houses. The observed variables were vector competition, bionomic and transovarial infection level, and titer of virus on the mosquitoes after injection. Immunohistochemistry or IHC methods were used to identify transovarial infection status. The number of Ae. aegypti and Ae. albopictus were almost similar and both were found indoors or outdoors. Based on HI and OI index, the larvae density in the highland was enough high than standard of the program. Transovarial infection was found on Ae. aegypti and Ae. albopictus. Environment parameters such as temperature and relative humidity fulfilled the optimum requirement to support the vectors' life cycle. Transovarial infection has been proven, thus, it indicates that the local transmission has been occurred in this area. Titer of virus was also increasing after day per day. This indicate that the mosquitoes has the ability being vector. As used to do in other area, it is important to conduct breeding places elimination (PSN) indoors as well as outdoors, through active participation of the community in highland area.
    Full-text · Article · Feb 2017
    • The breeding of mosquitoes is determined by the availability of suitable and sufficient habitat for the larval stages, and this is dependent on rainfall (Russell, 1998). Rainfall and the number of rainy days have been found to correlate with dengue in many provinces in Thailand, such as Prachaup Khiri Khan, Phetchabun, Sing Buri, Suphan Buri, Trat, Pattani, Phuket (Thammapalo et al., 2005), Sisaket (Wongkoon et al., 2011), and Nakhon Si Thammarat (Wongkoon et al., 2013). This study found that the number of rainy days and the amount of rainfall were positively associated with the dengue incidence in Phatthalung and Ranong, respectively.
    [Show abstract] [Hide abstract] ABSTRACT: This study explored the spatio-temporal patterns of dengue infection in southern Thailand. Data on monthly-notified cases of dengue fever, over the period of January 1981-May 2014 were collected from the Bureau of Epidemiology, Department of Disease Control, Ministry of Public Health. Weather data over the period of January 2007-May 2014 were obtained from the Thai Meteorological Department. Box and whisker plots were used to study the spatial temporal patterns of dengue incidence. Spearman correlation analysis and time-series adjusted Poisson regression analysis were performed to quantify the relationship between weather and the number of dengue cases. The results show that the highest dengue cases occurred in July in the Gulf of Thailand. Conversely, for the Andaman Sea, the highest dengue cases occurred in June in Phang-Nga, Phuket and Ranong provinces. Only Krabi province had the most dengue cases occurring in July. When we compared dengue transmission duration between the Andaman Sea and the Gulf of Thailand, we found that for the Andaman Sea, Trang province had the longest dengue transmission duration (i.e. June-September) and for the Gulf of Thailand, Nakhon Si Thammarat had the longest dengue transmission duration (i.e. June-September). The number of rainy days and relative humidity were the main predictors of dengue incidence by the Gulf of Thailand, while the amount of rainfall and the temperature were the main predictors of dengue incidence by the Andaman Sea. The time series Poisson regression models provided such goodness-of-fit that the correlation between observed and predicted numbers of dengue incidence exceeded 80%. These models could be used to optimise dengue prevention by predicting trends in dengue incidence. Accurate predictions, for even a few months, provide an invaluable opportunity to mount a vector control intervention or to prepare for hospital demand in the community.
    Full-text · Article · Mar 2016
    • This study demonstrated that the number of Aedes larvae was higher in the rainy season than in the winter and summer seasons. Many studies have reported similar findings in many other parts of Thailand10111516 and Côte d’Ivoire17. The seasonality of Aedes larvae in Sisaket showed a similar pattern to that observed by Mogi et al15 in Chiang Mai, Thailand, that Aedes larvae remained low in summer and winter seasons, but increased in the rainy season.
    [Show abstract] [Hide abstract] ABSTRACT: Background & objectives: Environmental factors including weather variables may play a significant role in the transmission of dengue. This study investigated the effect of seasonal variation on the abundance of Aedes aegypti and Ae. albopictus larvae and explored the impact of weather variability on dengue transmission in Sisaket, Thailand. Methods: The monthly mosquito larval surveys were carried out in urban and rural areas in Sisaket, Thailand from January to December 2010. Data on monthly-reported cases of dengue fever over the period 2004-2010 were obtained from the Ministry of Public Health. Weather data over the same period were obtained from the Thai Meteorological Department. Chi-square test was used to find the differences relating to seasonal variability, areas of study, and mosquito species factors using entomological survey data. Time series Poisson regression analysis was performed using data on monthly weather variables and dengue cases. Results: There were more Ae. aegypti larvae per household than Ae. albopictus larvae in the winter and rainy seasons. More Aedes larvae per household were found in the rainy season than in the winter and summer seasons. Relative humidity at a lag of one month and rainy days in the current month were significant predictors of dengue incidence in Sisaket. Interpretation & conclusions: Increased rain during the current month and less humidity during the previous month might trigger a higher incidence of dengue epidemic in Sisaket. The present findings suggest that the dengue incidence corresponds with the number of Aedes larvae. The seasonal patterns of dengue outbreaks coincide with the rainy season.
    Full-text · Article · Sep 2013