Traditional epidemiology is being disrupted by big data, according to Vayena et al. (2015).
Digital Disease Detection (DDD) uses data sources, including those gathered from mobile devices, wearables, social media and health forums. DDD has been used to monitor the outbreak of Ebola in Africa in advance of official reports. However whilst DDD, its analysis and dissemination will benefit society, potential risks and unintended consequences should be both considered and anticipated.
DDD combines personal information, public health and technology, using big data – large, complex data sets which are constantly being added to at an ever-increasing speed.
Health information can be extracted from many different places and cross-referenced to provide not only early outbreak detection but how epidemics develop and recur. However, although this has the potential for societal good, data governance and ethics are becoming more complex. Public health ethics were brought into focus following the horrors of Nazi experimentation during WW2 and although there are strict national and international guidelines they have not moved with the times to take account of the changes in technology nor the issues raised if DDD were to be embedded in public health systems.
Vayena et al. consider there to be three sets of ethical challenges for DDD:
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How can big data be used for societal good whilst respecting a person's right to privacy?
They argue that DDD has a benefit to health and should be considered for the common good, unlike corporate big data which is used to generate profit for the few, therefore privacy laws should be adapted for DDD. DDD projects can be run on a global level therefore privacy need to be balanced in different cultures.
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How can DDD methodology be made robust enough when the data is messy, biases need managed and the field is still being developed? At the moment most data used is already in the public domain e.g. social networking sites but do people realise that what they post is being used for reasons beyond their original intent? DDD must deal with this type of uncertainty, be clear about data sources and be transparent about how data are being used.
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Is it legitimate and ethically justified to expect DDD to adhere to outdated compliance rules and how is present compliance perceived? Today disease can spread quickly but early warning can reduce the impact. To this end International Health Regulations have been revised to include information gathering from various sources, creating a legitimate space for DDD practice. This is a starting point but as DDD becomes integrated it will build its own legitimacy by developing best practice standards and setting up monitoring boards.