Gary Kee Khoon Lee

Statistical modeling reveals the effect of absolute humidity on dengue in Singapore.

Weather factors are widely studied for their effects on indicating dengue incidence trends. However, these studies have been limited due to the complex epidemiology of dengue, which involves dynamic interplay of multiple factors such as herd immunity within a population, distinct serotypes of the virus, environmental factors and intervention programs. In this study, we investigate the impact of weather factors on dengue in Singapore, considering the disease epidemiology and profile of virus serotypes. A Poisson regression combined with Distributed Lag Non-linear Model (DLNM) was used to evaluate and compare the impact of weekly Absolute Humidity (AH) and other weather factors (mean temperature, minimum temperature, maximum temperature, rainfall, relative humidity and wind speed) on dengue incidence from 2001 to 2009. The same analysis was also performed on three sub-periods, defined by predominant circulating serotypes. The performance of DLNM regression models were then evaluated through the Akaikes Information Criterion. From the correlation and DLNM regression modeling analyses of the studied period, AH was found to be a better predictor for modeling dengue incidence than the other unique weather variables. Whilst mean temperature (MeanT) also showed significant correlation with dengue incidence, the relationship between AH or MeanT and dengue incidence, however, varied in the three sub-periods. Our results showed that AH had a more stable impact on dengue incidence than temperature when virological factors were taken into consideration. AH appeared to be the most consistent factor in modeling dengue incidence in Singapore. Considering the changes in dominant serotypes, the improvements in vector control programs and the inconsistent weather patterns observed in the sub-periods, the impact of weather on dengue is modulated by these other factors. Future studies on the impact of climate change on dengue need to take all the other contributing factors into consideration in order to make meaningful public policy recommendations.

Evaluating temporal factors in combined interventions of workforce shift and school closure for mitigating the spread of influenza.

Background It is believed that combined interventions may be more effective than individual interventions in mitigating epidemic. However there is a lack of quantitative studies on performance of the combination of individual interventions under different temporal settings. Methodology/Principal Findings To better understand the problem, we develop an individual-based simulation model running on top of contact networks based on real-life contact data in Singapore. We model and evaluate the spread of influenza epidemic with intervention strategies of workforce shift and its combination with school closure, and examine the impacts of temporal factors, namely the trigger threshold and the duration of an intervention. By comparing simulation results for intervention scenarios with different temporal factors, we find that combined interventions do not always outperform individual interventions and are more effective only when the duration is longer than 6 weeks or school closure is triggered at the 5% threshold; combined interventions may be more effective if school closure starts first when the duration is less than 4 weeks or workforce shift starts first when the duration is longer than 4 weeks. Conclusions/Significance We therefore conclude that identifying the appropriate timing configuration is crucial for achieving optimal or near optimal performance in mitigating the spread of influenza epidemic. The results of this study are useful to policy makers in deliberating and planning individual and combined interventions.

Time-series infectious disease data analysis using SVM and genetic algorithm

Dengue represents a serious health threat in the Tropics, owing to the year-round presence of Aedes mosquito vectors, and the lack of any anti-viral drugs or vaccines. Climatic factors are important in influencing the incidence of dengue. It is important to determine the relationships between climatic factors and disease incidence trends, which would be helpful for relevant environment and health agencies in planning appropriate pre-emptive control measures. Climatic factors and dengue case records vary over time. It is therefore difficult to justify the time-lag when a climatic factor affects the mosquito-to-human and human-to-mosquito loops. In this paper, we propose to use support vector machine (SVM) classifiers for analyzing the time- series dengue data and genetic algorithm (GA), to determine the time-lags and subset of climatic factors as effective factors influencing the spread of dengue. It is shown that the proposed model is able to detect important climatic factors and their time-lags which affect the disease, and the GA-based SVM classifiers could improve the classification accuracy significantly.

The Emergence of Urban Land Use Patterns Driven by Dispersion and Aggregation Mechanisms

We employ a cellular-automata to reconstruct the land use patterns of cities that we characterize by two measures of spatial heterogeneity: (a) a variant of spatial entropy, which measures the spread of residential, business, and industrial activity sectors, and (b) an index of dissimilarity, which quantifies the degree of spatial mixing of these land use activity parcels. A minimalist and bottom-up approach is adopted that utilizes a limited set of three parameters which represent the forces which determine the extent to which each of these sectors spatially aggregate into clusters. The dispersion degrees of the land uses are governed by a fixed pre-specified power-law distribution based on empirical observations in other cities. Our method is then used to reconstruct land use patterns for the city state of Singapore and a selection of North American cities. We demonstrate the emergence of land use patterns that exhibit comparable visual features to the actual city maps defining our case studies whilst sharing similar spatial characteristics. Our work provides a complementary approach to other measures of urban spatial structure that differentiate cities by their land use patterns resulting from bottom-up dispersion and aggregation processes.

Temporal factors in school closure policy for mitigating the spread of influenza

Is school closure effective in mitigating influenza outbreaks? For Singapore, we developed an individual-based simulation model using real-life contact data. We evaluated the impacts of temporal factors – trigger threshold and duration – on the effectiveness of school closure as a mitigation policy. We found an upper bound of the duration of school closure, where further extension beyond which will not bring additional benefits to suppressing the attack rate and peak incidence. For school closure with a relatively short duration (< 6 weeks), it is more effective to start closure after a relatively longer delay from the first day of infection; if the duration of school closure is long (>6 weeks), however, it is better to start it as early as reasonable. Our studies reveal the critical importance of timing in school closure, especially in cost-cautious situations. Our studies also demonstrate the great potential of a properly developed individual-based simulation model in evaluating various disease control policies.

A Dynamic Hybrid Resource Provisioning Approach for Running Large-Scale Computational Applications on Cloud Spot and On-Demand Instances

Testing and executing large-scale computational applications in public clouds is becoming prevalent due to cost saving, elasticity, and scalability. However, how to increase the reliability and reduce the cost to run large-scale applications in public clouds is still a big challenge. In this paper, we analyzed the pricing schemes of Amazon Elastic Compute Cloud (EC2) and found the disturbance effect that the price of the spot instances can be heavily affected due to the large number of spot instances required. We proposed a dynamic approach which schedules and runs large-scale computational applications on a dynamic pool of cloud computational instances. We use hybrid instances, including both on-demand instances for high priority tasks and backup, and spot instances for normal computational tasks so as to further reduce the cost without significantly increasing the completion time. Our proposed method takes the dynamic pricing of cloud instances into consideration, and it reduces the cost and tolerates the failures for running large-scale applications in public clouds. We conducted experimental tests and an agent based Scalable complex System modeling for Sustainable city (S3) application is used to evaluate the scalability, reliability and cost saving. The results show that our proposed method is robust and highly flexible for researchers and users to further reduce cost in real practice.

A QUANTITATIVE PROCEDURE FOR THE SPATIAL CHARACTERIZATION OF URBAN LAND USE

We have developed a procedure that characterizes the land use pattern of an urban system using: (a) Spatial entropy that measures the extent of spread of residential, business and industrial sectors; and (b) Index of dissimilarity that quantifies the degree of mixing in space of different sectors. The approach is illustrated by using the land use zoning maps of the city state of Singapore and a selection of North American cities. We show that a common feature of most cities is for the industrial areas to be highly clustered while at the same time segregated from the residential or business districts. We also demonstrate that the combination of entropy of residential and dissimilarity index between residential and business areas provides a quantitative and potentially useful means of differentiating the land use pattern of different cities.

Comparability of Results from Pair and Classical Model Formulations for Different Sexually Transmitted Infections

The “classical model” for sexually transmitted infections treats partnerships as instantaneous events summarized by partner change rates, while individual-based and pair models explicitly account for time within partnerships and gaps between partnerships. We compared predictions from the classical and pair models over a range of partnership and gap combinations. While the former predicted similar or marginally higher prevalence at the shortest partnership lengths, the latter predicted self-sustaining transmission for gonorrhoea (GC) and Chlamydia (CT) over much broader partnership and gap combinations. Predictions on the critical level of condom use (Cc) required to prevent transmission also differed substantially when using the same parameters. When calibrated to give the same disease prevalence as the pair model by adjusting the infectious duration for GC and CT, and by adjusting transmission probabilities for HIV, the classical model then predicted much higher Cc values for GC and CT, while Cc predictions for HIV were fairly close. In conclusion, the two approaches give different predictions over potentially important combinations of partnership and gap lengths. Assuming that it is more correct to explicitly model partnerships and gaps, then pair or individual-based models may be needed for GC and CT since model calibration does not resolve the differences.

Mining Weather Information in Dengue Outbreak: Predicting Future Cases Based on Wavelet, SVM and GA

Dengue Fever has existed throughout the contemporary history of mankind and poses an endemic threat to most tropical regions. Dengue virus is transmitted to humans mainly by the Aedes aegypti mosquito. It has been observed that there are significantly more Aedes aegypti mosquitoes present in tropical areas than in other climate regions. As such, it is commonly believed that the tropical climate suits the life-cycle of the mosquito. Thus, studying the correlation between the climatic factors and trend of dengue cases is helpful in conceptualising a more effective pre-emptive control measure towards dengue outbreaks. In this chapter, a novel methodology for forecasting the number of dengue cases based on climactic factors is presented. We proposed to use Wavelet transformation for data pre-processing before employing a Support Vector Machines (SVM)-based Genetic Algorithm to select the most important features. After which, regression based on SVM was used to perform forecasting of the model. The results drawn from this model based on dengue data in Singapore showed improvement in prediction performance of dengue cases ahead. It has also been demonstrated that in this model, prior climatic knowledge of 5 years is sufficient to produce satisfactory prediction results for up to 2 years. This model can help the health control agency to improve its strategic planning for disease control to combat dengue outbreak. The experimental result arising from this model also suggests strong correlation between the monsoon seasonality and dengue virus transmission. It also confirms previous work that showed mean temperature and monthly seasonality contribute minimally to outbreaks.

A GA-SVM feature selection model based on high performance computing techniques

Supervised learning is well-known and widely applied in many domains including bioinformatics, cheminformatics and financial forecasting. However, the interference from irrelevant features may lead to the poor accuracy of classifiers. As a popular feature selection model, GA-SVM is desirable in many of those cases to filter out irrelevant features and improve the learning performance subsequently. However, the high computational cost strongly discourages the application of GA-SVM in large-scale datasets. In this paper, an HPC-enabled GA-SVM (HGA-SVM) is proposed by integrating data parallelization, multithreading and heuristic techniques with the ultimate goal of robustness and low computational cost. Our proposed model is comprised of four improvement strategies: 1) GA Parallelization, 2) SVM Parallelization, 3) Neighbor Search and 4) Evaluation Caching. All the four strategies improve various aspects of the feature selection model and contribute collectively towards higher computational throughput.