Home > News Center > Medical Sciences > Using internet big data to predict outbreak of Dengue Fever and assess effectiveness of preventive and control measures

Using internet big data to predict outbreak of Dengue Fever and assess effectiveness of preventive and control measures

Last updated :2016-12-12

Source: School of Public Health
Written by: School of Public Health
Edited by: Wang Dongmei

With the accelerating progress of global warming, urbanization and ecological changes, Dengue Fever (DF) has become the most popular transmit arbovirus infectious disease in the world, particularly in the tropical and sub-tropical regions. Guangdong province in China is high-prevalent with Dengue Fever, outbreaks occurred frequently in recent years, especially in 2014, when Guangdong experienced a most severe outbreak, which significantly affected people’s life and work, caused heavy burden of disease, and also brought serious challenge to China’s prevention and control of Dengue Fever disease.

Recently, professor LU Jiahai and his team from the School of Public Health, Sun Yat-sen University is taking research in key technical measures of prevention and control of Dengue Fever, under the modern international theory of disease prevention and control——One Health, with colleagues from professor HU Wenbiao’s team in Queensland University of Technology, Australia, Guangzhou Center for Disease Control and Prevention, and Zhongshan Center for Disease Control and Prevention. The findings are published in Scientific Reports, entitled “Using Baidu Search Index to Predict Dengue Outbreak in China”. The research is based on internet big data of network monitoring system, using Baidu search index to evaluate effectiveness of control measures and predict outbreak of Dengue Fever.

Based on epidemics and outbreaks of DF in Guangzhou and Zhongshan, this research is using Baidu search engine to analyze the correlations between DF epidemics or outbreaks and the search index in Baidu about Dengue, and further to find the may index threshold of predicting DF outbreaks. This research combined internet big data and DF epidemics together by data mining method, has significant value in initiating timely public health interventions for DF prevention and control such as mosquito control, clinical treatment, vaccination or education of the targeted population, also provide scientific basis to construct a brand new diseases surveillance system.

Traditional surveillance systems for DF in China are built on the basis of passive or sentinel site surveillance, which are limited by underreporting, delayed diagnosis or missed diagnosis caused by under-resourced laboratory services and none quick detection methods. All these shows much less sensitivity in infectious disease surveillance system, so, a real-time tracking and surveillance early warning system is urgently needed to help us control infectious disease at the first beginning. This study found that the Baidu search engine combining with traditional diseases surveillance system may be considered for early detection of DF activities in China, which may provide for China health department officials a new scientific development direction to establish a more low-cost and effective digital early warning system.

Currently, about 70% of emerging infectious diseases are zoonosis. One Health is a modern theory promoted around the world to deal with emerging infectious disease, referring to collaborations and communications cross nations, regions, departments and multi-disciplines. Dengue Fever, as an important vector-borne infectious disease, especially need One Health approaches. This research can bring new thoughts to other infectious disease control and prevention.