Public Funding for TWO Worlds Consulting Limited

Reopening the economy and reestablishing social contact are vital to the recovery of the economy and society from Covid-19\. Early reopening for economic stimulus risks having the opposite effect, as repeated restrictions imposed at short notice potentially do more cumulative damage, and for longer, than maintaining initial restrictions -- shops, offices and factories have invested in reopening and social distancing measures and individuals have made commitments on the expectation of being able to fulfil them. It is inevitable that restrictions will need to come and go for the foreseeable future, given that the only readily available metrics for the impact of changes in policy are changes in the Covid-19 R-Factor and reported incidence and mortality. The need is for policy-makers in any sector is to be able to make informed and timely decisions that impact the least number of people in the smallest area for the shortest period of time. In any sector -- government, healthcare, sports and leisure venues, retail malls, factories etc -- decisions are dependent on the quality and range of data available, on its geographical, demographic and sector detail and, crucially, on the ability to integrate multiple sources, identify relevant trends, anticipate what may happen next and make informed choices to continue, relax or reimpose restrictions. This is where the problem arises: data is scattered across diverse sources, is of variable quality, accessibility, timeliness, completeness and accuracy, and curating it to generate effective local or sector insight is slow and labour-intensive, often using platforms that are themselves restrictive and/or expensive to operate. This still only reflects what participants knew to look for -- it does not help surface previously unsuspected relationships that might then influence decision-making. Even then, such relationships need to be validated, but the biggest lag is in inspiration -- thinking to look for particular correlations. The pandemic has given us many examples: the correlation of ethnicity with mortality; the impact of living at altitude with severity or the propensity of Covid-19 patients to develop other conditions following apparent recovery. There we are still simply identifying patterns, trends and relationships and driving specific metrics. All are useful, but prediction is usually by eyeball or simple projection of a trend. Two Worlds is developing an SaaS service based on udu, a next-generation, AI-driven intelligence platform. The outcome is a system customisable to local or sector need and which dynamically integrates specific data sources with the automated discovery of supplementary data. We bring a range of statistical, mathematical and AI approaches to the analysis and presentation of information and to the prediction of trends in data. Our approach enables both the creation of repeatable reporting and prediction and the self-organising discovery of new and potentially relevant patterns and relationships. In doing so, it builds on udu's established market, Two Worlds' prior (and continuing) environmental analytics and a first stage Covid-19 analytical study, now approaching completion. This project enables the project to move from prototype demonstrator (TRL5) to being usable by initial test customers (TRLs 7+).

The UK, amongst others, has been shown to lack a coherent and reliable infrastructure to support effective direct and timely collection and analysis of pandemic data, about both the progressIon of Covid-19 itself and the population response to public policy aimed at mitigating its progress. Refining policy and informing the judgement calls required to navigate the balance between lockdown and economic damage requires both accurate data and the ability to rapidly model and project the outcomes of multiple, 'What if?' scenarios. Current data intelligence systems are partial, fragmented, incomplete, lag reality and, in most cases can only surface what they have specifically been asked to look for. AI systems used to look for patterns are often constrained by the quality and range of data available to them. Existing hypothesis-driven models tend to look at single factors in isolation, and lack the flexibility to take into account multiple sources of mortality data or factors such as population mobility and behaviour, the impact of events such as Cheltenham races, sunny bank holiday weather or other regional and seasonal variations. This can only be addressed through a more holistic approach to data collection and integration. This project therefore uses an advanced data intelligence platform, udu, which has been used to integrate a wide range of data from multiple sources and of multiple types to create a unified, readily extensible and automatically updated data repository. This incorporates geospatial, pandemic, demographic and census data, population behavioural data from a number of sectors throughout the pandemic and makes it available for analysis. The project then uses a combination of mathematical modelling and inferential discovery to adjust for variations in data recording and then create daily projections of the course of the pandemic for all areas (currently in the UK), for up to two weeks ahead. These projections have been validated against the historical data for the pandemic and can be shown to provide predictions that are, for most areas, within +/-15% of the actual outcomes. Based on this breakthrough achievement, the project is now refining its analytic capabilities and projections by enabling segmentation by demographic, population behaviour and public policy (ie, which restrictions apply where, and when). An early outcome from its existing data will be a 'Smartcast' system to allow anyone to find out, on demand, what is happening and what restrictions apply in their area. The resulting system is intended to be capable of supporting direct exploration by human users and providing them with actionable predictions, as well as providing an interface (API) to allow other teams to access the datascape created, to then support third party analytics that further extend capability.