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8. Outbreak of Zika virus infection in Singapore: an epidemiological, entomological, virological, and clinical analysis

Author

Ho, Zheng Jie Marc, Hapuarachchi, Hapuarachchige Chanditha, Barkham, Timothy, Chow, Angela, Ng, Lee Ching, Lee, Jian Ming Vernon, Leo, Yee Sin, Prem, Kiesha, Lim, Yue Hui Georgina, de Sessions, Paola F, Rabaa, Maia A, Chong, Chee Seng, Tan, Cheong Huat, Rajarethinam, Jayanthi, Tan, Junhao, Anderson, Danielle E, Ong, Xinmei, Cook, Alex R, Chong, Chia Yin, Hsu, Li Yang, Yap, Grace, Lai, Yee Ling, Chawla, Tanu, Pan, Louise, Sim, Shuzhen, Chen, I-Cheng Mark, Thoon, Koh Cheng, Yung, Chee Fu, Li, Jia Hui, Ng, Hee Ling Deborah, Nandar, Khine, Ooi, Peng Lim, Lin, Raymond Tzer Pin, Aw, Pauline, Uehara, Anna, Pratim De, Partha, Soon, Wendy, Hibberd, Martin Lloyd, Ng, Huck Hui, Maurer-Stroh, Sebastian, and Sessions, October M

Published
2017
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11. Computed Tomography Imaging of the Larynx for Diagnosis of Vocal Cord Dysfunction

Author

Koh, Joo H., primary, Ruane, Laurence E., additional, Phyland, Debra, additional, Hamza, Kais, additional, Fukusho, Risa, additional, Leahy, Elizabeth, additional, Avram, Adriana, additional, Yap, Grace, additional, Lau, Kenneth K., additional, MacDonald, Martin I., additional, Leong, Paul, additional, Baxter, Malcolm, additional, and Bardin, Philip G., additional

Published
2023
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14. Association of rodents with man-made infrastructures and food waste in Urban Singapore

Author

Oh, Penny Hui Yun, Mahathir Humaidi, Chan, Qian Yi, Yap, Grace, Ang, Kai Yang, Tan, Jason, Ng, Lee Ching, Mailepessov, Diyar, School of Biological Sciences, and National Environment Agency

Subjects
Epidemiology, Biological sciences [Science], residential estates, rattus norvegicus, rat infestation, bin chambers, Infectious and parasitic diseases, RC109-216, Environmental Science (miscellaneous), Rat Infestation, rat tracking, Rattus Norvegicus, Research Article
Abstract

Background: Rodent population control is an important measure in reducing the risk of rodent-borne disease transmission. In this study, we examined rodent activity in the sanitary waste network around the household waste-collection bin chamber of an urban residential apartment block. Methods: We utilised infra-red camera traps to determine the pattern of rodent activity in a rodent-infested bin chamber and its associated sanitary waste network. Multivariable logistic regression was performed to assess the risk factors that were independently associated with rodent activity in the bin chambers. Result: The camera trap surveillance showed that the rodents were active in the bin chamber and sanitary network both in the day and at night. In the cross-sectional study, rodent activity in the bin chambers was independently associated with broken floor traps [Adjusted odds ratio (AOR): 36.7, CI: 21.3–66.3], calendar month [Log-likelihood ratio test (LRT) p = 0.002] and Town Council [LRT p = 0.004] variables. In restricted analysis, rodent activity in bin chambers was independently associated with defects in the wastewater pipe under the chamber [AOR: 12.3, CI: 4.3–51.7]. Conclusion: Our study suggests that urban municipal management councils should prioritize rodent control resources in areas according to the factors that increase the risk of rodent infestation. Published version

Published
2022
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17. Detection of Japanese Encephalitis Virus in Culex Mosquitoes in Singapore

Author

Yap, Grace, primary, Mailepessov, Diyar, additional, Lim, Xiao Fang, additional, Chan, Sharon, additional, How, Choon Beng, additional, Humaidi, Mahathir, additional, Yeo, Gladys, additional, Chong, Chee Seng, additional, Lam-Phua, Sai Gek, additional, Lee, Ruth, additional, Okumura, Chiharu, additional, Vythilingam, Indra, additional, and Ng, Lee Ching, additional

Published
2020
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18. Molecular Analysis of the Bloodmeals of Culex spp. Mosquitoes at Natural Habitats in Singapore to Investigate the Potential Risk of Japanese Encephalitis Virus and West Nile Virus Transmission

Author

Yeo, Gladys, primary, Chan, Sharon, additional, How, Choon Beng, additional, Humaidi, Mahathir, additional, Lim, Xiao Fang, additional, Mailepessov, Diyar, additional, Chong, Chee Seng, additional, Phua-Lam, Sai Gek, additional, Lee, Ruth, additional, Hapuarachchi, Hapuarachchige Chanditha, additional, Ng, Lee Ching, additional, and Yap, Grace, additional

Published
2020
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21. Pericoronary adipose tissue and quantitative global non-calcified plaque characteristics from CT angiography do not differ in matched South Asian, East Asian and European-origin Caucasian patients with stable chest pain

Author

Goeller, Markus, primary, Rahman Ihdayhid, Abdul, additional, Cadet, Sebastien, additional, Lin, Andrew, additional, Adams, Daniel, additional, Thakur, Udit, additional, Yap, Grace, additional, Marwan, Mohamed, additional, Achenbach, Stephan, additional, Dey, Damini, additional, and Ko, Brian, additional

Published
2020
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22. Three-month real-time dengue forecast models: an early warning system for outbreak alerts and policy decision support in Singapore

Author

Shi, Yuan, Liu, Xu, Kok, Suet-Yheng, Rajarethinam, Jayanthi, Liang, Shaohong, Yap, Grace, Chong, Chee-Seng, Lee, Kim-Sung, Tan, Sharon S.Y., Chin, Christopher Kuan Yew, Lo, Andrew, Kong, Waiming, Ng, Lee Ching, and Cook, Alex R.

Subjects
Real-time systems -- Usage -- Models, Epidemics -- Forecasts and trends -- Singapore, Dengue -- Forecasts and trends, Real-time control -- Usage -- Models, Real-time system, Market trend/market analysis, Environmental issues, Health
Abstract

BACKGROUND: With its tropical rainforest climate, rapid urbanization, and changing demography and ecology, Singapore experiences endemic dengue; the last large outbreak in 2013 culminated in 22,170 cases. In the absence of a vaccine on the market, vector control is the key approach for prevention. OBJECTIVES: We sought to forecast the evolution of dengue epidemics in Singapore to provide early warning of outbreaks and to facilitate the public health response to moderate an impending outbreak. METHODS: We developed a set of statistical models using least absolute shrinkage and selection operator (LASSO) methods to forecast the weekly incidence of dengue notifications over a 3-month time horizon. This forecasting tool used a variety of data streams and was updated weekly, including recent case data, meteorological data, vector surveillance data, and population-based national statistics. The forecasting methodology was compared with alternative approaches that have been proposed to model dengue case data (seasonal autoregressive integrated moving average and step-down linear regression) by fielding them on the 2013 dengue epidemic, the largest on record in Singapore. RESULTS: Operationally useful forecasts were obtained at a 3-month lag using the LASSO-derived models. Based on the mean average percentage error, the LASSO approach provided more accurate forecasts than the other methods we assessed. We demonstrate its utility in Singapore's dengue control program by providing a forecast of the 2013 outbreak for advance preparation of outbreak response. CONCLUSIONS: Statistical models built using machine learning methods such as LASSO have the potential to markedly improve forecasting techniques for recurrent infectious disease outbreaks such as dengue. http://dx.doi.org/10.1289/ehp.1509981., Introduction Dengue is an acute infectious disease common to tropical and subtropical regions. Dengue viruses are transmitted by Aedes mosquitoes, mainly Aedes aegypti and Aedes albopictus (Rosen et al. 1983). [...]

Published
2016
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27. Detection and characterization of a novel bat-borne coronavirus in Singapore using multiple molecular approaches

Author

Lim, Xiao Fang, primary, Lee, Chengfa Benjamin, additional, Pascoe, Sarah Marie, additional, How, Choon Beng, additional, Chan, Sharon, additional, Tan, Jun Hao, additional, Yang, Xinglou, additional, Zhou, Peng, additional, Shi, Zhengli, additional, Sessions, October M., additional, Wang, Lin-Fa, additional, Ng, Lee Ching, additional, Anderson, Danielle E., additional, and Yap, Grace, additional

Published
2019
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28. Characterization of Fowlpox virus in chickens and bird-biting mosquitoes: a molecular approach to investigating Avipoxvirus transmission

Author

Yeo, Gladys, primary, Wang, Yifan, additional, Chong, Shin Min, additional, Humaidi, Mahathir, additional, Lim, Xiao Fang, additional, Mailepessov, Diyar, additional, Chan, Sharon, additional, How, Choon Beng, additional, Lin, Yueh Nuo, additional, Huangfu, Taoqi, additional, Fernandez, Charlene Judith, additional, Hapuarachchi, Hapuarachchige Chanditha, additional, and Yap, Grace, additional

Published
2019
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30. Molecular characterization of two hantavirus strains from different rattus species in Singapore

Author

Kek Relus, Siew Chern-Chiang, Low Hwee-Teng, Yap Grace, Johansson Patrik, Ng Lee-Ching, and Bucht Göran

Subjects
Infectious and parasitic diseases, RC109-216
Abstract

Abstract Background Hantaviruses cause human disease in endemic regions around the world. Outbreaks of hantaviral diseases have been associated with changes in rodent population density and adaptation to human settlements leading to their proliferation in close proximity to human dwellings. In a parallel study initiated to determine the prevalence of pathogens in Singapore's wild rodent population, 1206 rodents were trapped and screened. The findings established a hantavirus seroprevalence of 34%. This paper describes the molecular characterization of hantaviruses from Rattus norvegicus and Rattus tanezumi, the predominant rodents caught in urban Singapore. Methodology Pan-hanta RT-PCR performed on samples of Rattus norvegicus and Rattus tanezumi indicated that 27 (2.24%) of the animals were positive. sequence analysis of the S and M segments established that two different hantavirus strains circulate in the rodent population of Singapore. Notably, the hantavirus strains found in Rattus norvegicus clusters with other Asian Seoul virus sequences, while the virus strains found in Rattus tanezumi had the highest sequence similarity to the Serang virus from Rattus tanezumi in Indonesia, followed by Cambodian hantavirus isolates and the Thailand virus isolated from Bandicota indica. Conclusions Sequence analysis of the S and M segments of hantavirus strains found in Rattus norvegicus (Seoul virus strain Singapore) and Rattus tanezumi (Serang virus strain Jurong TJK/06) revealed that two genetically different hantavirus strains were found in rodents of Singapore. Evidently, together with Serang, Cambodian and Thailand virus the Jurong virus forms a distinct phylogroup. Interestingly, these highly similar virus strains have been identified in different rodent hosts. Further studies are underway to analyze the public health significance of finding hantavirus strains in Singapore rodents.

Published
2010
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31. TCT-386 Ethnic differences in coronary luminal volume, left ventricular mass and CT-derived fractional flow reserve

Author

Ihdayhid, Abdul Rahman, primary, Adams, Daniel, additional, Goeller, Markus, additional, Dey, Damini, additional, Thakur, Udit, additional, Yap, Grace, additional, Lim, Ren Yik, additional, Isa, Mourushi, additional, Michail, Michael, additional, Cameron, James, additional, Seneviratne, Sujith, additional, and Ko, Brian, additional

Published
2018
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32. Dengue in Singapore from 2004 to 2016: Cyclical Epidemic Patterns Dominated by Serotypes 1 and 2

Author

Rajarethinam, Jayanthi, primary, Ang, Li-Wei, additional, Ong, Janet, additional, Ycasas, Joyce, additional, Hapuarachchi, Hapuarachchige Chanditha, additional, Yap, Grace, additional, Chong, Chee-Seng, additional, Lai, Yee-Ling, additional, Cutter, Jeffery, additional, Ho, Derek, additional, Lee, Vernon, additional, and Ng, Lee-Ching, additional

Published
2018
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33. Mapping dengue risk in Singapore using Random Forest

Author

Ong, Janet, primary, Liu, Xu, additional, Rajarethinam, Jayanthi, additional, Kok, Suet Yheng, additional, Liang, Shaohong, additional, Tang, Choon Siang, additional, Cook, Alex R., additional, Ng, Lee Ching, additional, and Yap, Grace, additional

Published
2018
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35. An Improvement to LMSV Parameter Estimation: Modelling and Forecasting Volatility and Value-at-risk

Author

Yap, Grace Lee Ching, Chin, Wen Cheong, Yap, Grace Lee Ching, and Chin, Wen Cheong

Abstract

This paper proposes an improvement to the long memory stochastic volatility (LMSV) model to forecast volatility using high frequency data. To allow frequency domain quasi-maximum likelihood (FDQML) estimation, we suggest a parsimonious normalization procedure that avoids repetitive parameter estimation. This resultantly produces more efficient parameter estimation as less estimation error is involved. Besides, a de-trending procedure is proposed prior to the de-seasonalization procedure to improve the identification of seasonal patterns. We compare the performance of volatility forecasts by the proposed refined FDQML-LMSV model with the existing LMSV and the linear long memory model that fits to logarithms of realised volatility. The empirical results show that the proposed method outperforms the existing models statistically, and the output of the proposed method improves the accuracy and efficiency in value-at-risk forecasting.

Published
2017

37. Salmonella in Retail Food and Wild Birds in Singapore—Prevalence, Antimicrobial Resistance, and Sequence Types.

Author

Kyaw Thu Aung, Hong Jun Chen, Man Ling Chau, Yap, Grace, Xiao Fang Lim, Humaidi, Mahathir, Chua, Cliff, Yeo, Gladys, Hooi Ming Yap, Jia Quan Oh, Manogaran, Vijitha, Hapuarachchi, Hapuarachchige Chanditha, Maiwald, Matthias, Nancy Wen Sim Tee, Barkham, Timothy, Tse Hsien Koh, Gutiérrez, Ramona Alikiiteaga, Schlundt, Jorgen, and Lee Ching Ng

Published
2019
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44. Feeding Host Range ofAedes albopictus(Diptera: Culicidae) Demonstrates Its Opportunistic Host-Seeking Behavior in Rural Singapore

Author

Kek, Relus, primary, Hapuarachchi, H. C., additional, Chung, Chiew-Yuan, additional, Humaidi, Mahathir Bin, additional, Razak, Muhammad Aliff B. A., additional, Chiang, Suzanna, additional, Lee, Caleb, additional, Tan, Cheong-Huat, additional, Yap, Grace, additional, Chong, Chee-Seng, additional, Lee, Kim-Sung, additional, and Ng, Lee-Ching, additional

Published
2014
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49. Three-Month Real-Time Dengue Forecast Models: An Early Warning System for Outbreak Alerts and Policy Decision Support in Singapore.

Author

Yuan Shi, Xu Liu, Suet-Yheng Kok, Jayanthi Rajarethinam, Shaohong Liang, Yap, Grace, Chee-Seng Chong, Kim-Sung Lee, Tan, Sharon S. Y., Yew Chin, Christopher Kuan, Lo, Andrew, Waiming Kong, Lee Ching Ng, and Cook, Alex R.

Subjects
DENGUE hemorrhagic fever, FORECASTING, PREVENTION of epidemics, MOSQUITOES, PEST control, REGRESSION analysis, RESEARCH funding, PREVENTION
Abstract

BACKGROUND: With its tropical rainforest climate, rapid urbanization, and changing demography and ecology, Singapore experiences endemic dengue; the last large outbreak in 2013 culminated in 22,170 cases. In the absence of a vaccine on the market, vector control is the key approach for prevention. OBJECTIVES: We sought to forecast the evolution of dengue epidemics in Singapore to provide early warning of outbreaks and to facilitate the public health response to moderate an impending outbreak. METHODS: We developed a set of statistical models using least absolute shrinkage and selection operator (LASSO) methods to forecast the weekly incidence of dengue notifications over a 3-month time horizon. This forecasting tool used a variety of data streams and was updated weekly, including recent case data, meteorological data, vector surveillance data, and population-based national statistics. The forecasting methodology was compared with alternative approaches that have been proposed to model dengue case data (seasonal autoregressive integrated moving average and step-down linear regression) by fielding them on the 2013 dengue epidemic, the largest on record in Singapore. RESULTS: Operationally useful forecasts were obtained at a 3-month lag using the LASSO-derived models. Based on the mean average percentage error, the LASSO approach provided more accurate forecasts than the other methods we assessed. We demonstrate its utility in Singapore's dengue control program by providing a forecast of the 2013 outbreak for advance preparation of outbreak response. CONCLUSIONS: Statistical models built using machine learning methods such as LASSO have the potential to markedly improve forecasting techniques for recurrent infectious disease outbreaks such as dengue. [ABSTRACT FROM AUTHOR]

Published
2016
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