World-first computational model predicts dengue outbreaks
A world-first computational model that accurately predicts the spread of infectious diseases carried by air passengers, such as dengue fever, could help prevent future outbreaks.
Developed by scientists at CSIRO’s Data61 in collaboration with Queensland University and Queensland Health, the tool draws on travel data from the International Air Transportation Association and dengue incidence rates from the Global Health Data Exchange to derive insights about the spreading dynamics of the mosquito-borne disease.
Project researcher and postdoctoral fellow at Data61, Dr Jess Leibig, explained that the model combines monthly air passenger travel data, country-level dengue incidence rates, and seasonal statistics on the disease to detect the number of dengue importations on a global scale, rather than assessing the risk of importation like existing models.
Here we show the results for non-endemic countries/states with vector presence with the highest number of predicted imported dengue cases in 2015. The bars are stacked to distinguish between returning residents (green) and visitors (blue). The blue solid line corresponds to the total number of imported cases. The error bars correspond to the model’s coefficient of variation (see Material and methods). The six countries were selected because they are predicted to receive the highest number of dengue importations, are non-endemic and dengue vectors are established.
“By understanding the travel behaviour of infected individuals, we can estimate the number of infections that are imported into different countries each month,” said Dr Leibig.
“The tool also determines the infections’ country of origin and is able to uncover the routes along which dengue is most likely spread.”
The idea for the tool was triggered by the 2008/2009 dengue outbreak in Cairns, which saw 900 cases of the disease, including one death.
“In North Queensland the dengue vector Ae. Aegypti is established, however, the virus is not constantly circulating among the population. That is, local outbreaks only happen when the virus is introduced from outside. The outbreak in 2008/09 was triggered by a single infected traveller who returned from Indonesia and passed on the virus to local mosquitoes.”
“Our model has identified high-risk routes along which dengue is often imported as well as airports that are likely to see a high number of dengue infected arrivals.
The map shows the output of the tool’s model for August 2015.The area of a node increases with the number of dengue cases imported through the corresponding airport. Airports that are predicted to not receive any infections are not shown on the map. Endemic countries are coloured dark grey. Countries that are non-endemic and where dengue vectors Aedes aegypti and/or Aedes albopictus are present are coloured in light grey.
The tool has established the travel route from Puerto Rico to Florida as having the highest predicted volume of dengue-infected passengers travelling to a non-endemic region, followed by Guadeloupe and Martinique (islands located in the Caribbean) to France.
“This provides a useful tool to assist public health authorities with dengue preparedness,” explains project team member and researcher at Queensland Health, Dr Cassie Jansen.
“It can also help authorities to identify those locations where new dengue outbreaks may occur, following the arrival of infected passengers.”
The ten routes with the highest predicted number of dengue-infected passengers with final destinations in non-endemic countries with vector presence.
The tool can be applied to other vector-borne diseases of global concern such as malaria, Zika, and chikungunya virus. “For example, if we want to predict the monthly importations of malaria, we can simply replace the dengue incidence rates and seasonality with the data for malaria,” said Dr Liebig.
This project expands on the Disease Networks and Mobility (DiNeMo) project, which developed a real-time alert and surveillance system for human infectious diseases and how they could spread in Australia. An earlier model was developed to predict the spread of dengue within Australia.
The model was recently published in PLOS ONE and can be downloaded here.