↓ Skip to main content

Neighbourhood level real-time forecasting of dengue cases in tropical urban Singapore

Overview of attention for article published in BMC Medicine, August 2018
Altmetric Badge

Mentioned by

twitter
1 X user

Citations

dimensions_citation
56 Dimensions

Readers on

mendeley
109 Mendeley
Title
Neighbourhood level real-time forecasting of dengue cases in tropical urban Singapore
Published in
BMC Medicine, August 2018
DOI 10.1186/s12916-018-1108-5
Pubmed ID
Authors

Yirong Chen, Janet Hui Yi Ong, Jayanthi Rajarethinam, Grace Yap, Lee Ching Ng, Alex R. Cook

Abstract

Dengue, 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. The 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. Close 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. Spatially 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.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 109 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 109 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 15%
Student > Ph. D. Student 12 11%
Student > Bachelor 10 9%
Student > Master 9 8%
Student > Doctoral Student 7 6%
Other 16 15%
Unknown 39 36%
Readers by discipline Count As %
Medicine and Dentistry 11 10%
Agricultural and Biological Sciences 9 8%
Environmental Science 7 6%
Computer Science 6 6%
Biochemistry, Genetics and Molecular Biology 5 5%
Other 29 27%
Unknown 42 39%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 06 August 2018.
All research outputs
#20,529,173
of 23,099,576 outputs
Outputs from BMC Medicine
#3,365
of 3,466 outputs
Outputs of similar age
#288,623
of 330,720 outputs
Outputs of similar age from BMC Medicine
#65
of 66 outputs
Altmetric has tracked 23,099,576 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,466 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 43.7. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 330,720 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 66 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.