COVID-19 (Part 3): Using data-driven decision making to navigate a path back to normality

Blog published by GeoSpock, under Big Data

The global COVID-19 pandemic has thrown a spotlight on the crucial need for a rapid, joined-up approach to tackling such threats to our health – and virtually every aspect of our daily lives. Technology holds the key to ensuring we can live with the virus threat rather than dying from it. In the third of a series of three blogs, we look at how data fusion can unlock vital understanding of our invisible enemy.

Understanding the virus progression

Understanding when and where the virus is progressing is crucial for continuous management and control of the pandemic situation – including track-and-trace activity with granular containment, isolation and travel policies.

Being aware of potentially high-risk areas allows individuals to make sensible behavioural choices to minimise the spread of the virus. And awareness of any early-stage outbreaks, along with the rate of progression, enables local authorities to plan and stockpile resources.

An understanding of the progression of the virus also enables low-risk or low-exposure areas and industries to reopen – to minimise both short-term and long-term economic disruption. And it allows continuity planning – with an early-warning system to alert businesses to regional supply-chain disruptions, quarantines or unavailability of public transportation services, for example, so that they can react more effectively to an evolving situation without facing a binary stop-or-go decision.

Data is the key that holds the potential to unlock vital understanding of our invisible enemy's activity.

This can be in the form of close-contact data – from Bluetooth signals as part of the government-backed NHS app, for example, or travel records from ticket purchases or Oyster cards. It also includes fine-grained location data – such as GPS signals from mobile devices or social media check-ins.

Another category is coarse-grained location data – which includes government place-of-residency records and network-based location trilateration from telecoms providers, using mobile phone towers. Meanwhile, supplementary contextual data can be in the form of COVID-19 testing outcomes, for example, Highways England road use, crowd monitoring via CCTV or even weather conditions.

Each of these datasets can be used in a wide variety of potential combinations and some of them will be machine generated – meaning large amounts of data will need to be stored and processed for insights and analytics. And, of course, insights are only useful if they arrive in a timely manner – particularly when they're being used to intervene in a live pandemic situation to drive better outcomes.

Dealing with complexities

Dealing with this complexity, scale of data and required speed of response requires a highly sophisticated data fusion platform – exactly the kind of task our state-of-the-art spatial database was created to solve.

Following a data fusion approach allows the problems of traditional data silos to be eradicated and drastically reduces the complexity of building any one application – it brings together many different types of data to fulfil a wide variety of purposes from a single data platform.

An individual who has encountered someone who has tested positive for COVID-19 would be alerted via the NHS app, for example – but they could also be informed if hyper-localised cases have occurred in their neighbourhood or at their place of work. This would enable them to stay at home or avoid visiting public places to minimise exposure risk without a nationwide lockdown.

Healthcare managers could be alerted to any regional growth in infection rates – enabling them to shift medical staff and resources to where they are needed most. And nationwide analytics could enable the government to identify virus hotspots and react proactively and appropriately – by placing individual towns in lockdown, for example, rather than the whole country.

This kind of data-driven decision making, along with real-time monitoring and management of physical world systems, is critical when it comes to improving real-world outcomes for an ever-evolving pandemic situation.

Unpicking the current lockdown without triggering a second wave of the virus is a complex challenge. And that's just the start – globalisation is increasing the likelihood that we will face future pandemics. Technology holds the key to navigating a path back to normality for all of us.

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