The research builds on a methodology previously developed by the team to track influenza in the US.
The mathematical modelling tool, known as “AutoRegression with GOogle search queries” (ARGO), revived hopes in 2015 that internet search data could help health officials track diseases after earlier systems like Google Flu Trends and Google Dengue Trends returned poor results.
Dengue, a mosquito-borne virus that infects about 390 million people each year, is often difficult to monitor with traditional hospital-based reporting due to inefficient communication, but dengue-related Google searches could provide faster alerts.
The researchers used Google’s “Trends” tool to track the top ten dengue-related search queries made by users in each country during the study period.
The scientists then compared ARGO’s estimates with those from five other methods. They found that ARGO returned more accurate estimates than did any other method for Mexico, Brazil, Thailand, and Singapore.
Estimates for Taiwan were less accurate, possibly because the country experienced less-consistent seasonal disease patterns from year to year, researchers said.
The findings highlight the potential for Google searches to enable accurate, timely tracking of mosquito-borne diseases in countries lacking effective traditional surveillance systems.
Future work could investigate whether this method could be improved to track disease on finer spatial and temporal scales, and whether environmental data, such as temperature, could improve estimates, researchers said.
“The wide availability of internet throughout the globe provides the potential for an alternative way to reliably track infectious diseases, such as dengue, faster than traditional clinical-based systems,” said Mauricio Santillana of Boston Children’s Hospital and Harvard Medical School.
“This alternative way of tracking disease could be used to alert governments and hospitals when elevated dengue incidence is anticipated, and provide safety information for travellers,” said Santillana, senior author of the study published in the journal PLOS Computational Biology.