Smyth Barry
Insight SFI Research Centre for Data Analytics, University College Dublin, Dublin, Ireland.
Front Digit Health. 2021 May 28;3:655745. doi: 10.3389/fdgth.2021.655745. eCollection 2021.
The end of 2020 and the beginning of 2021 was a challenging time for many countries in Europe, as the combination of colder weather, holiday celebrations, and the emergence of more transmissible virus variants conspired to create a perfect storm for virus transmission across the continent. At the same time lockdowns appeared to be less effective than they were earlier in the pandemic. In this paper we argue that one contributing factor is that existing ways of communicating risk-case numbers, test positivity rates, hospitalisations etc.-are difficult for individuals to translate into a level of personal risk, thereby limiting the ability of individuals to properly calibrate their own behaviour. We propose an new more direct measure of personal risk, , to estimate the likelihood that an individual will come into contact with an infected person, and we argue that it can play an important role, alongside more conventional statistics, to help translate complex epidemiological data into a simple measure to guide pandemic behaviour. We describe how exposure risk can be calculated using existing data and infection prediction models, and use it to evaluate and compare the exposure risk associated with 39 European countries.
2020年末至2021年初，对欧洲许多国家来说是一段充满挑战的时期，寒冷天气、节日庆祝活动以及更具传播性的病毒变种共同出现，为病毒在整个欧洲大陆的传播营造了一场完美风暴。与此同时，封锁措施似乎比疫情早期的效果要差。在本文中，我们认为一个促成因素是，现有的传达风险病例数、检测阳性率、住院人数等信息的方式，个人很难将其转化为个人风险水平，从而限制了个人适当调整自身行为的能力。我们提出了一种新的更直接的个人风险衡量指标，以估计个人接触感染者的可能性，并认为它可以与更传统的统计数据一起发挥重要作用，帮助将复杂的流行病学数据转化为一种简单的衡量指标，以指导应对疫情的行为。我们描述了如何利用现有数据和感染预测模型计算接触风险，并使用它来评估和比较39个欧洲国家的接触风险。
