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→
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→
This is aimed, in no small part, at my many friends in the US, who I see alternating between despair at their own ‘government’’s behaviour and angst-laden apology to the world for their current Liar-in-Chief. Please, folks, relax, a little at least.
Yes, the withdrawal of the US from the Paris Agreement will do damage – indeed, it already has – but that damage is less to the mitigation of anthropogenic climate change than to US influence in the world.
Rewind: Sixteen years or so ago, I was interested in how we use software to help us solve the compound, iterative and ever-changing problems we face every day: juggling complex trip schedules, working out where we need to be and when to co-ordinate with our friends or colleagues and, of course, how we find out about stuff that we’d want if only we knew to ask for it. I’m still thinking about it.
Two Worlds is an AI research consultancy and incubator, specialising in complex, adaptive systems. We create strategies, technologies and services that encompass advanced data discovery and fusion AI/Machine Learning, IoT networks, augmented and mixed reality systems.
