Traditional infectious disease surveillance is the gold standard, typically based on laboratory tests and other epidemiological data collected by public health institutions. However, it can include time lags, is expensive to produce, and typically lacks the local resolution needed for accurate monitoring. Further, it can be cost-prohibitive in low-income countries. In contrast, big data streams from internet queries, for example, are available in real-time and can track disease activity locally but have their own biases. Therefore, developing hybrid tools that combine traditional surveillance and real-time big data may provide a way forward, complementing rather than replacing existing traditional methods
The main research question of this project is: How can Google Trends data be used to improve our understanding of public health and disease trends?
Our specific objectives are to:
- Use Google Trends X-Data to identify public interest in some infectious diseases;
- Explore the predictability of internet search data by correlating real-life incidence data and online interests in infectious diseases;
- Develop efficient predictive models for monitoring disease outbreaks based on a hybrid of traditional data sources and internet search data.
- Conduct small area estimation and mapping by identifying areas at high risk of disease outbreaks.
Investigators:
- Sibukele Gumbo, University of Johannesburg
- Dr Lateef Babatunde Amusa, University of Johannesburg