Ethics and governance for digital disease surveillance

Ethics and governance for digital disease surveillance

SCIENCE policy forum

Michelle M. Mello, C. Jason Wang





A key question is how to ensure that companies and governments conducting and using epidemiologic analyses of new data sources are accountable for what they do. Democratic processes ordinarily help ensure that policy-making is reasonably transparent, the public has opportunities for in­ put, and irresponsible officials can be removed. But many initiatives during COVID-19 have been undertaken by countries without strong democratic traditions and free-speech protections. Even in the United States, technological solutions are being pursued by small groups of officials and tech company leaders working outside ordinary channels and public view. The need to make decisions quickly may justify such processes but increases concerns about responsible practices.

The potential for misappropriation of data collected and methods developed for disease surveillance looms large. After all, the same approaches that can be used for case identification and contact tracing can be used to identify and track a government’s political opponents (5). Such fears undercut trust in what public health officials are trying to do, and with­ out public trust and participation, many key strategies for fighting infectious disease cannot succeed.




There are only few publications available that reflect on one of the (rather neglected) management principles: Accountability (also very much required from highest level in UN DRR ).

The problem space of Accountability is much broader than mentioned in the paragraph quoted from the above cited paper but the authors certainly raise a lot of further discussions in that direction.

(misappropriation of data  vs. accountability for not having certain information and communication principles implemented)


Public Trust enhancement is very much dependent of carefully negotiated documentation principles, transparently described “open” analytics (together with the corresponding cautions of use because of uncertainties / error propagation), and differentiated writeup of decisions made, not just from decision text collection but by documenting the full situation description of available information.


It also becomes very clear that for decision support not only the “data” but the full semantics and processing/decision/use model (pragmatics) needs to be available

(I would call this “from Big Data to Adequate Information and Decision Support”)


Difficult to have this in times of crisis (no new principles, most of it has been required before, so its currently mainly a matter of unpreparedness)  but there is hope that those massive social and economic consequences (here mentioned in sequence of importance to information society at large) bring along much better awareness to implement information management principles at the general state-of-art that is already available in methods and techniques.


Horst Kremers