Predictive Analytics for Covid-19 Recovery

Covid-19 恢复的预测分析

• 批准号: 93341
• 金额: $ 17.8万
• 依托单位: TWO WORLDS CONSULTING LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=93341
• 关键词: Predictive; Analytics; Covid; 19; Recovery

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+).

重新开放经济和重建社会联系对经济和社会从Covid-19中复苏至关重要。为经济刺激而提前重新开放可能会产生相反的效果，因为在短时间内反复实施的限制可能会造成更多的累积损害，而且时间比维持最初的限制更长。办公室和工厂已投资于重新开放和社交距离措施，个人也已做出承诺，期望能够履行这些承诺。在可预见的未来，考虑到政策变化影响的唯一可用指标是COVID-19 R因子和报告的发病率和死亡率的变化。任何部门的政策制定者都需要能够做出明智和及时的决策，在最短的时间内影响最小区域内最少数量的人。在任何部门-政府，医疗保健，体育和休闲场所，零售商场，工厂等-决策都取决于可用数据的质量和范围，地理，人口和部门细节，最重要的是，综合多种来源的能力，确定相关趋势，预测下一步可能发生的情况，并做出明智的选择，继续，放松或重新实施限制。这就是问题所在：数据分散在不同来源，质量、可获得性、及时性、完整性和准确性各不相同，整理数据以产生有效的地方或部门见解的过程缓慢，而且劳动密集，这仍然只反映了参与者知道要寻找什么--它无助于揭示以前未被怀疑的关系，这些关系可能会影响决策。即便如此，这种关系也需要得到验证，但最大的滞后是在灵感方面--思考寻找特定的相关性。这场大流行给了我们很多例子：种族与死亡率的相关性;生活在高海拔地区对严重程度的影响，或者新冠肺炎患者在明显康复后出现其他疾病的倾向。在这些方面，我们仍然只是简单地确定模式、趋势和关系，并推动具体的指标。所有这些都是有用的，但预测通常是通过眼球或简单的趋势预测。Two Worlds正在开发基于udu的SaaS服务，udu是下一代人工智能驱动的智能平台。我们的成果是一个可根据当地或行业需求定制的系统，该系统将特定数据源与自动发现补充数据动态集成。我们带来了一系列统计，数学和人工智能方法来分析和展示信息以及预测数据趋势。我们的方法既可以创建可重复的报告和预测，也可以自组织地发现新的和潜在的相关模式和关系。在这样做的过程中，它建立在udu的既定市场，两个世界的先前（和持续）的环境分析和第一阶段的Covid-19分析研究，现在接近完成。该项目使该项目能够从原型演示器（TRL 5）转移到初始测试客户（TRL 7+）。