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Even more granular examples, such as footfalls at identifiable business locations, risks de-identifying religious groups; patients visiting cancer hospitals, HIV clinics, or reproductive health clinics; or those seeking public assistance. The medical and public health communities long ago deemed the un-masking of such information without consent unacceptable, but companies have recently been releasing it on publicly available dashboards.

Until we know more about how these changing movement patterns impact epidemiological aspects of the disease, we should use these data with caution.

Simply presenting them, or interpreting them without a proper contextual understanding, could inadvertently lead to imposing or relaxing restrictions on lives and livelihoods, based on incomplete information.

In the absence of a tightly coupled testing and treatment plan, however, these apps risk either providing false reassurance to communities where infectious but asymptomatic individuals can continue to spread disease, or requiring an unreasonably large number of people to quarantine. The behavioral response of the population to these apps is therefore unknown and likely to vary significantly across societies.

Some contact-tracing apps follow black-box algorithms, which preclude the global community of scientists from refining them or adopting them elsewhere. These non-transparent, un-validated interventions — which are now being rolled out (or rolled back) in countries such as China, India, Israel and Vietnam — are in direct contravention to the open cross-border collaboration that scientists have adopted to address the Covid-19 pandemic.

pidemiological models that can help predict the burden and pattern of spread of Covid-19 rely on a number of parameters that are, as yet, wildly uncertain. We still lack many of the basic facts about this disease, including how many people have symptoms, whether people who have been infected are immune to reinfection, and — crucially — how many people have been infected so far. In the absence of reliable virological testing data, we cannot fit models accurately, or know confidently what the future of this epidemic will look like for all these reasons, and yet numbers are being presented to governments and the public with the appearance of certainty

Telenor, the Norwegian telco giant has led the way in responsible use of aggregated mobility data from cell phone tower records. Its data have been used, in close collaboration with scientists and local practitioners, to model, predict, and respond to outbreaks around the world. Telenor has openly published its methods and provided technological guidance on how telco data can be used in public health emergencies in a responsible, anonymized format that does not risk de-identification.

The Covid-19 Mobility Data Network, of which we are part, comprises a voluntary collaboration of epidemiologists from around the world analyzes aggregated data from technology companies to provide daily insights to city and state officials from California to Dhaka, Bangladesh.

Coronavirus has been a “significant accelerant” to linking data sets in order to overcome silos, he added.

Experian, meanwhile, has rolled out a demographic segmentation tool, dubbed “Experian Safeguard”, which it has offered for free to local councils, NHS trusts, fire and police services as well as charities. Such tools are primarily used by private companies to target consumers to market products.

Experian’s flagship Mosaic postcode demographics tool — which arranges the UK population into groups according to factors such as lifestyle and debt levels — has been deployed at a number of local authorities, including Leeds city and Stockton-on-Tees borough councils, according to data gathered by Tussell, a data provider which tracks UK government contracts and expenditure.