Digital epidemiology occupies a particularly interesting role in the digital transformation of health and care, which is also impacting key public health functions. It describes a new field which has undergone rapid growth in the past few years, fuelled by the increased availability of Big Data and technologies powered by Artificial Intelligence (AI) and relying on new data analysis methods.
Methods of digital epidemiology include making use of data not primarily designed for the purpose of epidemiology. This includes digital trace data, for example from Internet-based sites such as Google Trends, Twitter and Wikipedia, as well as crowd-sourced information (e.g. from web-based surveys) which can expand established disease surveillance systems by capturing and recording real-time data (e.g. in combination with geo-location) and trends regarding health outcomes. The reliance of digital epidemiology on non-traditional data sources is not without controversy, but it is also contributing to a broader understanding of the different factors that can influence contagious disease outbreaks and other epidemiological events.
Considering this, the paper discusses the importance that the rapidly growing field of digital epidemiology plays in public health, as well as its limitations and concerns that need to be taken into consideration with the expansion of digital epidemiology.
