We’ve been thinking for some time about how best to present the dynamic of the pandemic in a way that actually shows what’s happening – the R number doesn’t give any idea of magnitude and is – in our opinion – best kept behind the scenes as a contributor to analytic models, raw or compensated case numbers are just that – daily records – shocking enough in themselves but they still don’t show the energy in the thing.
Which is the point: this is a dynamic system with both momentum from existing infections and where rising (or falling) infection rates cause case rate to accelerate or decelerate. So we brushed up on our Newtonian physics and used his first and second Laws of Motion to calculate the momentum and its force – the acceleration or deceleration of the pandemic – at any time.
So the graph below shows the energy (blue) and acceleration (pink) of the pandemic across the UK from 1st April 2020. The equivalent maps show that, while the pandemic is decelerating in the South-East (and accelerating slightly in the NW and Scotland), the level of energy in the SE is still huge (and effectively off the scale relative to the first wave in March-May, showing that it would take very little for the pandemic to start to turn up again)
When we started experimenting with this approach, we expected something. We didn’t quite expect such a dramatic visualisation, especially one that subjectively feels to have a strong correlation with real world impact, hopefully bringing that home for non-experts, and at a visceral level. Remember here that the UK currently has about the world’s highest case rate – well ahead per capita of the USA.
We’ve been over our methodology and believe that we’re calculating both reasonably and accurately.
Over the last few months, we have been using advanced data intelligence to improve the sourcing, timeliness and validation of Covid-19 statistics. We then use our emergent and adaptive platform to provide high quality predictive modelling of its likely progress.
Human nature being what it is, people have become somewhat desensitised to raw numbers and to the differences between the first surge of the virus in March-May and where we are now, despite that difference being genuinely scary, as any front line medic will confirm, if they still have the energy. So anything that helps communicate the current situation more effectively can only help – this is one of our alpha stage experiments, animating the rolling case rate per 100,000 population for the UK, from March 12 2020 to January 5 2021.
Throughout the pandemic, we’ve watched UK government Covid-19 policy-making as it appears to follow a drunkard’s walk between, on the one hand, an inherent laziness of response and a politically-influenced disinclination to act and, on the other, an attempt to claim some sort of causal relationship with the scientific and real world advice that they’re being given. The core mantra apparently is to do nothing until it’s too late, then blame any combination of scientists, the wider population and random acts of nature for the outcome.
Some parts of the UK are seeing increasing daily case numbers (first image) and a continuing increase in the rolling live case rate (the second image shows weekly case rate calculated per 100,000 population). 1
We – as a society – had the opportunity to prevent SARS-CoV-2 becoming endemic. We largely wasted it, initially by not locking down early enough or for long enough to remove it from the population. Nor did we use the lockdown period to set up effective data collection, testing, tracking and analytic tools to enable rapid and fine-grained response to predicted changes in incidence (it’s a truism that, by the time you’re working with actual data, you’re already behind in your response).
Public policy decisions are therefore based on incomplete and lagging data, partial models and on individual and committee opinion (however well qualified the participants) rather than being informed by data-driven modelling of potential outcomes. We are also behaving as though we’re dealing with a static target rather than a continuously evolving situation, one where an unintended consequence of partial and incomplete restrictions is that it effectively selects for different strains of the virus, as it evolves to cope with changes in population behaviour. This virus, like any other, has been mutating since before it collided head-on with our species, and it continues to evolve as it seeks selective advantage in exploiting its human host population, at any given time. Continue reading Time for a different approach?→
In developing our daily-predictive AI for Covid-19 infections , we’ve come across some, ah, interesting quirks in the official UK data: previously, we’d been using the government’s daily download data set for England, hoovering it into udu and thence driving the internal and R-based analytic and learning models. We’ve done the same for Scotland, Wales and Northern Ireland, from their respective data gateways, and merged the outcome to create a consistent baseline for analysis. Overall then, a bit clumsy, but perfectly workable. Continue reading Damned (Official) Statistics…→
Two Worlds is one of the successful applicants to a £40M fund created to support “Business-led innovation in response to global disruption”, a competition that attracted 8,600 applicants. Working with a team including epidemiologists, mathematical modelling specialists and the Department of Computer Science at Imperial College, Two Worlds is using udu’s intelligent analytic software to tackle this problem.Continue reading Two Worlds wins research funding for Covid-19 Intelligent Analytics→
Many of us will be going through the same thing right now: my partner and I live a way away from elderly parents – in our case our respective mothers – who both most definitely fall into the ‘most vulnerable’ category for Covid-19. Both are given support by regular visits from professional carers. So, quite naturally, we asked the care provider to send us over their procedures for minimising the chances of transmission of the virus to and between their staff and charges. Continue reading Covid-19: Box Ticking versus Delivery→
Two Worlds has successfully completed the first phase of an R&D project with Scottish Natural Heritage (SNH) , funded by the UK’s Small Business Research Initiative (SBRI) programme.
The project’s goal was to demonstrate the feasibility of a service to provide a single point of advice to support anyone planning activities that affect the natural environment, to help them understand the environmental impact of their proposal, to advise them on what they could do to mitigate any impact and to outline what consents and processes they’d then need to follow. It will also be possible, over time, to build a dynamic picture of the impact of human activity on the natural environment by a wide range of measures, including climate impacts. Continue reading Two Worlds completes R&D Project with SNH→
Firstly, a disclaimer: this isn’t a political piece – it’s simply a take on the Labour Party’s pledge today to provide a free full fibre (FTTP) service to every home and business in the UK by 2030. The method by which they will do so is to nationalise OpenReach, and subsidise the rollout and running of a universal fibre infrastructure through a tax on largely non-domiciled tech companies.
Most AI practitioners will argue that the risk to humanity from AI doesn’t (and won’t) come from an AI waking up one day, deciding that the best way to solve the world’s problems is to wipe out humanity and then serendipitously finding that it’s in control of the world’s nuclear weapons. On the principle that cock-up trumps conspiracy, pretty much every time, we’re far more likely to take a range of hits from the misapplication of an AI that’s either too stupid1 to do the job that’s been asked of it or where those deploying it are incapable of understanding its limitations (or indeed don’t care, as long as they’ve cashed out before it all falls apart). Broadly speaking, machine systems fail for one or more of these reasons: Continue reading Wye AI, Man!→
Two Worlds is an AI research consultancy and incubator, specialising in innovation support in the area of complex, adaptive systems. We help create strategies, technologies and services that encompass advanced data discovery and fusion AI/Machine Learning, IoT networks, augmented and mixed reality systems.