In the early days of a pandemic there is no time for complicated data collection. One needs a simple cross-country benchmark approach based on robust data that is easy to understand and easy to collect. The recent pandemic has shown us what early available pandemic data might look like, because statistical data was published every day in standard news outlets in many countries. This paper provides new methodology for the analysis of data where exposure is only vaguely understood and where the very definition of exposure might change over time. The exposure of poor quality is used to analyse and forecast events. Our example of such exposure is daily infections during a pandemic and the events are number of new infected patients in hospitals every day. Examples are given with French Covid-19 data on hospitalized patients and numbers of infected.

翻译：在疫情暴发初期，复杂的数据收集工作往往来不及开展。此时需要一种基于稳健数据的简单跨国基准方法，该方法应易于理解且便于收集。近期的大流行揭示了早期可用疫情数据的可能形态，因为许多国家每日通过标准新闻渠道发布统计数据。本文针对暴露度仅被粗略理解、且其定义可能随时间变化的数据分析提出了新方法。该方法利用低质量暴露度来分析和预测事件。我们以疫情期间的每日感染人数作为此类暴露度的示例，而事件则是每日新增住院感染患者数量。文中以法国COVID-19住院患者及感染人数数据进行了案例演示。