We all want a normal life. And politicians feel the pressure from their parties and constituents to restore normality as rapidly as possible. Unfortunately, there’s a dissonance between their reluctance to then take needful and decisive action at the earliest possible opportunity and the long-term consequences from the pandemic, where a tendency to treat the pandemic as transactional – something you can bargain with – has driven a patchwork, limited and often counterproductive response to the pandemic. Continue reading The (Long and Winding) Road to Normal
We are pleased to announced that our new company, Intelligent Reality, has been accepted into EIE’s 2021 cohort, which showcases the most innovative, data-driven tech companies from Scotland, the UK and beyond. EIE’s (Engage, Invest, Exploit) annual conference features the the most promising high-growth companies who are seeking funding, from seed to series A.
Over the last 18 months we have successfully developed both our generic platform and environmental and geospatial applications, with an SBRI-funded R&D programme for Scottish Natural Heritage (now NatureScot) and, latterly, with three rounds of funding from InnovateUK. These have led to the successful development of inferential tools for exploratory, analytic and predictive modelling of the Covid-19 pandemic, as well as enhancing our core platform.
We have not only developed our analytic and predictive pipeline, but have further developed our relationship with udu, the revolutionary, discovery-based data intelligence platform, itself co-founded by Two Worlds.
With academic and industrial partners, we have also explored real time applications in precision agriculture and pollution monitoring.
Intelligent Reality, with staff in Scotland and Germany, has been formed to exploit the successful outcomes of Two Worlds’ incubation of its adaptive, self-organising approach to data analytics, providing insight and predictive intelligence for real world applications in dynamic environments.
EIE, which is run by the Bayes Centre at Edinburgh University in partnership with the DDI (Data-Driven Innovation) initiative, is a year-round programme highlighted by a day of pitching to investors from across the globe. EIE21 takes place on 10 June.
There’s been a lot of covoptimism this past week, from assorted government spokesfolks, including from people who do know what they’re talking about – a prime example being Prof. Neil Ferguson of Imperial. The theme here is that cases, case rates and the R number have been falling strongly and appear to be continuing to do so.
That’s true, to a point. But our modelling suggests that the immediate future is less rosy.
It’s not about the data – we use the same published sources as the government, albeit that they’ve got access to more sources than we do – it’s more about what you do with it.
Another Friday update: we’re well into our private Beta of our predictive analytics and what-if? modelling system for Covid-19 analytics.
So what is it telling us today?
As of 3rd February our projections are (within their confidence limits, which of course become broader the further out we look, even if the central projection is tracking the reality curve well), that the R number bottoms out about now for the UK as a whole, with case numbers continuing to fall until around the 9th, by which time R number is back to .92 and, by the 13th, it’s more likely to be above 1 again, mostly driven by the SE (Essex particularly) and Merseyside (see header picture).
On Friday 29th January, the Scottish Government announced that Na h-Eileanan Siar (the Western Isles) is being put into Level 4 lockdown, following a surge of new cases.
On the basis of the data available to us and our modelling approach, we’re not convinced about this decision: it appears to have be made on the basis of out-of-date analysis in an area which turned the corner on this outbreak some time ago.
Our emergent analytics, which generate fresh outlooks every day, suggest that the peak of the outbreak here was passed on 19th January and that it has declined, on multiple metrics, since then.
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. Continue reading Kinetics of a Pandemic
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…