Highlights

Above

A model focused on detecting a sharp rise in numbers instead of the absolute number of cases could help public health officials detect dengue outbreaks early.

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Getting ahead of dengue outbreaks

8 Jun 2021

This two-step outbreak detection framework could give early warnings of dengue outbreaks, improving Singapore’s ability to control the disease.

While COVID-19 is currently at the forefront of public consciousness, it is far from the only infectious disease we need to worry about. For highly urbanized, tropical Singapore, dengue has been a perennial concern. 2020 saw a record high of over 38,000 cases, far surpassing the previous high of 22,318 cases in 2013.

Just as weather reports can help people plan their daily activities, disease forecasting models are helping public health officials prepare for potential dengue outbreaks. However, most existing dengue prediction models focus on predicting what the absolute number of cases will be in the next couple of weeks, rather than trying to detect a sharp, consecutive increase in cases that would suggest an impending outbreak.

“There are two problems with this approach,” explained Xiuju Fu, a Senior Scientist at A*STAR’s Institute of High Performance Computing (IHPC). “The first is that accuracy drops dramatically the farther ahead the model tries to see. Second, there is a lack of clear definition of the threshold, so such identification of dengue outbreaks can be quite subjective.”

Instead, Fu and her team developed a model that can detect dengue outbreaks at an early stage, buying time for public health officials to launch interventions and prepare resources. The key to their success was that they focused on broader differences between outbreak and non-outbreak situations rather than simply the number of cases.

The researchers first sought to determine what ‘normal’ years and periods would look like, using weekly dengue data from 2006 to 2011, years where numbers were relatively low. To establish the best baseline possible, they deliberately left out periods when an outbreak was in full swing. The resulting model was able to generate accurate forecasts a week in advance.

Borrowing the ‘control chart’ concept from manufacturing quality control processes, the researchers then input the differences between the forecasted value and the actual number of cases observed. If the difference deviates from the normal pattern, the model signals that an outbreak might be imminent. “Moreover, by using the proposed framework adaptively, the dynamic threshold for detecting outbreaks can be automatically determined, which makes the model intelligent and adaptive in changing environments,” Fu said.

Fu and her team are currently working on adding further detail such as spatiotemporal patterns to their predictions. This additional layer of information could help identify which areas are more prone to large-scale dengue outbreaks, helping officials launch more targeted—and thus less costly—proactive control measures.

The A*STAR-affiliated researchers contributing to this research are from the Institute of High Performance Computing (IHPC).

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References

Chen, P., Fu, X., Ma, S., Xu, H.Y., Zhang, W., et al. Early dengue outbreak detection modeling based on dengue incidences in Singapore during 2012 to 2017. Statistics in Medicine 39, 2101-2114 (2020) | article

About the Researcher

Xiuju Fu

Senior Research Scientist

Institute of High Performance Computing
Xiuju Fu received her PhD in 2002 from Nanyang Technological University, Singapore, in Information Sciences. Thereafter, she conducted postdoctoral research the A*STAR’s Institute of High Performance Computing (IHPC) from 2002 to 2005. She currently works in the Institute as a Senior Research Scientist and Group Manager. Her research interests include big data analytics, complex system modeling and simulation, with specific applications in public health, maritime traffic/operations and supply chains.

This article was made for A*STAR Research by Wildtype Media Group