Predictive Analytics for Covid-19 Recovery

重新开放经济，重新建立社会联系，对于从新冠肺炎中恢复经济和社会至关重要。为刺激经济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限制可能会造成更大的累积损害，而且比维持最初的限制更长时间--商店、办公室和工厂在重新开放和社会疏远措施上投入了资金，个人也做出了承诺，希望能够履行这些措施。鉴于衡量政策变化影响的唯一现成指标是新冠肺炎R因子的变化以及报告的发病率和死亡率，在可预见的未来，限制措施不可避免地需要来来去去。对于任何部门的政策制定者来说，需要能够在最短的时间内做出明智和及时的决策，在最小的区域内影响到最少的人。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--决策取决于可用数据的质量和范围，取决于其地理、人口和部门细节，最重要的是，取决于整合多源、识别相关趋势、预测下一步可能发生的事情并做出明智选择以继续、放松或重新实施限制的能力。这就是问题所在：数据分散在不同的来源，质量参差不齐，信息的可获得性、及时性、完整性和准确性，以及对其进行策划以产生有效的地方或部门洞察是缓慢的和劳动密集型的，往往使用本身具有限制性和/或操作成本的平台，这仍然只是反映了参与者知道要寻找的东西--它无助于揭示可能随后影响决策的以前未被怀疑的关系。即使到了那时，这种关系也需要得到验证，但最大的滞后是灵感--寻找特定的相关性。此次疫情为我们提供了许多例子：种族与死亡率的相关性；居住在海拔高度与病情的影响，或者新冠肺炎患者在明显康复后出现其他疾病的倾向。我们仍在简单地识别模式、趋势和关系，并推动特定的指标。所有这些都很有用，但预测通常是通过眼球或对趋势的简单预测。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。其结果是一个可根据本地或行业需求定制的系统，它动态地将特定数据源与补充数据的自动发现集成在一起。我们将一系列统计、数学和人工智能方法引入信息分析和呈现以及数据趋势预测。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相关的模式和关系。通过这样做，它建立在优度的既定市场、两个世界先前(和继续)的环境分析以及即将完成的第一阶段新冠肺炎分析研究的基础上。该项目使该项目能够从原型演示器(TRL5)转移到可供初始测试客户使用(TRL7+)。

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+).