Internet data offers a potential complement to traditional data. If one knows where to look, digital traces of health are shed all over the internet. The use of internet data to monitor disease spread is built on the insight that when people get sick they search for information online and sometimes share it online.
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These digital traces of health get collected and can be accessed via websites like Google Trends, Twitter, and others. For some diseases — like flu and dengue —there is an association between the number of digital traces and the levels of disease in a population. The primary advantage of using internet data is that it is inexpensive and abundant, and it can be obtained in near real time.
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Scientists hope that by combining traditional data with internet data, we will be able to monitor and forecast diseases better than with traditional data alone. However, there are some shortcomings with the use of internet data. Excessive news media interest can drive searches about health that are not reflective of disease levels in the population, diseases with few cases are not easily able to be monitored, and symptoms of the disease need to be somewhat recognizable to the individual. Infectious Disease Advisor: What are some of the main takeaways from the new review?
While this is adequate for demonstrating feasibility, it does not provide the systematic rigor to understand how to transition these one-off research studies into a functional and actionable public health surveillance system. The review provides an important step toward this transition by surveying how internet data streams have been used for VBD surveillance and offering insight into when these data work, when they do not, and how they can be used.
Infectious Disease Advisor: What are remaining needs in terms of research and development in this area? Mr Generous: There are several. More attention needs to be given to practical demonstration of internet-based disease surveillance approaches in public health decision-making and response: How accurate do these approaches need to be to be actionable?
What level of uncertainty is acceptable? If a trial can be conducted demonstrating practical use of internet data streams, it would clear up a lot of questions.
Additionally, there needs to be more studies systematically evaluating the use of internet data streams for surveillance. This includes looking at multiple geographic scales local and state , diverse socioeconomic and demographic heterogeneity, and diseases and disease burden level. Overall, internet data streams seem to work best for monitoring large seasonal diseases in relatively wealthier areas of the world.
Biosurveillance, Methods and Case Studies by Taha Kass-Hout | | Booktopia
It is unclear at what point they stop working; for example, can you detect 10 people sick in a city of ,? What about people? It is also important to understand more about how news media bias can drive search traffic online and affect internet data. For example, if there is a panic of disease that is reported in the news media, many people who are not sick will search for information and possibly post about it on social media.
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Biosurveillance: Methods and Case Studies
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