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How infodemic intoxicates public health surveillance: from a big to a slow data culture
  1. Arnaud Chiolero
  1. Population Health Laboratory, University of Fribourg, Fribourg, Switzerland
  1. Correspondence to Professor Arnaud Chiolero, Population Health Laboratory (#PopHealthLab), University of Fribourg, Fribourg 1700, Switzerland; achiolero{at}

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Too much data? Too much information? The COVID-19 pandemic has made the case. The WHO coined the term ‘infodemic’ to describe the issue of overabundance of information, including misinformation, disseminated in real time via multiple channels.1 2 A related concept is ‘datademic’ to describe the overabundance of data. I argue in this essay that infodemic intoxicates public health surveillance and decision-making, and that we need to revisit how we conduct surveillance in the age of big data by fostering a slow data culture.

Why too much data intoxicate public health surveillance

Surveillance is the ongoing systematic collection, analysis and interpretation of data, closely integrated with the timely dissemination of the resulting information to those responsible for preventing and controlling disease and injury.3 Traditionally, it requires high-quality data which are collected for this purpose along well-defined methods. In the era of infodemic and big data, the access to different types of data has increased tremendously, offering new opportunities for surveillance. These new data, however, are not collected primarily for surveillance, often of relatively low (or not well-documented) quality, and, what is highly problematic for surveillance, of weak consistency across settings and time.3 The consequences are the questionable quality and reliability of information derived from these data.

An additional problem is what is called the selectivity bias.4 With some types of big data, it is difficult to define the source population from which they emerged; there is no well-defined source population. For instance, routinely collected data from healthcare providers are usually event based rather than population based, and the population using the services of these providers is changing, unpredictably, across time. These data are further exposed to surveillance bias and streetlight effect.3 Defining the source population is also very difficult with data from social media networks or internet queries, and it …

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  • Contributors AC wrote the manuscript and is responsible for the overall content as guarantor.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Provenance and peer review Commissioned; externally peer reviewed.