Dataset Information

Patterns of Urban Housing Shape Dengue Distribution in Singapore at Neighborhood and Country Scales.

ABSTRACT: Dengue is the most important human arboviral disease in Singapore. We classified residential areas into low-rise and high-rise housing and investigated the influence of urban drainage on the distribution of dengue incidence and outdoor breeding at neighborhood and country scales. In Geylang area (August 2014 to August 2015), dengue incidence was higher in a subarea of low-rise housing compared to high-rise one, averaging 26.7 (standard error, SE = 4.83) versus 2.43 (SE = 0.67) per 1,000 people. Outdoor breeding drains of Aedes aegypti have clustered in the low-rise housing subarea. The pupal density per population was higher in the low-rise blocks versus high-rise ones, 246 (SE = 69.08) and 35.4 (SE = 25.49) per 1,000 people, respectively. The density of urban drainage network in the low-rise blocks is double that in the high-rise ones, averaging 0.05 (SE = 0.0032) versus 0.025 (SE = 0.00245) per meter. Further, a holistic analysis at a country-scale has confirmed the role of urban hydrology in shaping dengue distribution in Singapore. Dengue incidence (2013-2015) is proportional to the fractions of the area (or population) of low-rise housing. The drainage density in low-rise housing is 4 times that corresponding estimate in high-rise areas, 2.59 and 0.68 per meter, respectively. Public housing in agglomerations of high-rise buildings could have a positive impact on dengue if this urban planning comes at the expense of low-rise housing. City planners in endemic regions should consider the density of drainage networks for both the prevention of flooding and the breeding of mosquitoes.

SUBMITTER: Seidahmed OME

PROVIDER: S-EPMC7007139 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Patterns of Urban Housing Shape Dengue Distribution in Singapore at Neighborhood and Country Scales.

Seidahmed Osama M E OME Lu Deng D Chong Chee Seng CS Ng Lee Ching LC Eltahir Elfatih A B EAB

GeoHealth 20180126 1

Dengue is the most important human arboviral disease in Singapore. We classified residential areas into low-rise and high-rise housing and investigated the influence of urban drainage on the distribution of dengue incidence and outdoor breeding at neighborhood and country scales. In Geylang area (August 2014 to August 2015), dengue incidence was higher in a subarea of low-rise housing compared to high-rise one, averaging 26.7 (standard error, SE = 4.83) versus 2.43 (SE = 0.67) per 1,000 people. ...[more]

PMID: 32159000

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Project description:BackgroundDengue, a vector-borne infectious disease caused by the dengue virus, has spread through tropical and subtropical regions of the world. All four serotypes of dengue viruses are endemic in the equatorial city state of Singapore, and frequent localised outbreaks occur, sometimes leading to national epidemics. Vector control remains the primary and most effective measure for dengue control and prevention. The objective of this study is to develop a novel framework for producing a spatio-temporal dengue forecast at a neighbourhood level spatial resolution that can be routinely used by Singapore's government agencies for planning of vector control for best efficiency.MethodsThe forecasting algorithm uses a mixture of purely spatial, purely temporal and spatio-temporal data to derive dynamic risk maps for dengue transmission. LASSO-based regression was used for the prediction models and separate sub-models were constructed for each forecast window. Data were divided into training and testing sets for out-of-sample validation. Neighbourhoods were categorised as high or low risk based on the forecast number of cases within the cell. The predictive accuracy of the categorisation was measured.ResultsClose concordance between the projections and the eventual incidence of dengue were observed. The average Matthew's correlation coefficient for a classification of the upper risk decile (operational capacity) is similar to the predictive performance at the optimal 30% cut-off. The quality of the spatial predictive algorithm as a classifier shows areas under the curve at all forecast windows being above 0.75 and above 0.80 within the next month.ConclusionsSpatially resolved forecasts of geographically structured diseases like dengue can be obtained at a neighbourhood level in highly urban environments at a precision that is suitable for guiding control efforts. The same method can be adapted to other urban and even rural areas, with appropriate adjustment to the grid size and shape.

Project description:BackgroundDengue is a mosquito-borne viral disease caused by one of four serotypes (DENV1-4). Infection provides long-term homologous immunity against reinfection with the same serotype. Plaque reduction neutralization test (PRNT) is the gold standard to assess serotype-specific antibody levels. We analysed serotype-specific antibody levels obtained by PRNT in two serological surveys conducted in Singapore in 2009 and 2013 using cluster analysis, a machine learning technique that was used to identify the most common histories of DENV exposure.MethodsWe explored the use of five distinct clustering methods (i.e. agglomerative hierarchical, divisive hierarchical, K-means, K-medoids and model-based clustering) with varying number (from 4 to 10) of clusters for each method. Weighted rank aggregation, an evaluating technique for a set of internal validity metrics, was adopted to determine the optimal algorithm, comprising the optimal clustering method and the optimal number of clusters.ResultsThe K-means algorithm with six clusters was selected as the algorithm with the highest weighted rank aggregation. The six clusters were characterised by (i) dominant DENV2 PRNT titres; (ii) co-dominant DENV1 and DENV2 titres with average DENV2 titre > average DENV1 titre; (iii) co-dominant DENV1 and DENV2 titres with average DENV1 titre > average DENV2 titre; (iv) low PRNT titres against DENV1-4; (v) intermediate PRNT titres against DENV1-4; and (vi) dominant DENV1-3 titres. Analyses of the relative size and age-stratification of the clusters by year of sample collection and the application of cluster analysis to the 2009 and 2013 datasets considered separately revealed the epidemic circulation of DENV2 and DENV3 between 2009 and 2013.ConclusionCluster analysis is an unsupervised machine learning technique that can be applied to analyse PRNT antibody titres (without pre-established cut-off thresholds to indicate protection) to explore common patterns of DENV infection and infer the likely history of dengue exposure in a population.

Project description:The principal objective of this study was to assess a modeling approach to Lu. longipalpis distribution in an urban scenario, discriminating micro-scale landscape variables at microhabitat and macrohabitat scales and the presence from the abundance of the vector. For this objective, we studied vectors and domestic reservoirs and evaluated different environmental variables simultaneously, so we constructed a set of 13 models to account for micro-habitats, macro-habitats and mixed-habitats. We captured a total of 853 sandflies, of which 98.35% were Lu. longipalpis. We sampled a total of 197 dogs; 177 of which were associated with households where insects were sampled. Positive rK39 dogs represented 16.75% of the total, of which 47% were asymptomatic. Distance to the border of the city and high to medium density vegetation cover ended to be the explanatory variables, all positive, for the presence of sandflies in the city. All variables in the abundance model ended to be explanatory, trees around the trap, distance to the stream and its quadratic, being the last one the only one with negative coefficient indicating that the maximum abundance was associated with medium values of distance to the stream. The spatial distribution of dogs infected with L. infantum showed a heterogeneous pattern throughout the city; however, we could not confirm an association of the distribution with the variables assessed. In relation to Lu. longipalpis distribution, the strategy to discriminate the micro-spatial scales at which the environmental variables were recorded allowed us to associate presence with macrohabitat variables and abundance with microhabitat and macrohabitat variables. Based on the variables associated with Lu. longipalpis, the model will be validated in other cities and environmental surveillance, and control interventions will be proposed and evaluated in the microscale level and integrated with socio-cultural approaches and programmatic and village (mesoscale) strategies.

Dataset Information

Patterns of Urban Housing Shape Dengue Distribution in Singapore at Neighborhood and Country Scales.

Publications

Patterns of Urban Housing Shape Dengue Distribution in Singapore at Neighborhood and Country Scales.

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