In the early days of development 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 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：在大流行发展的初期，没有时间进行复杂的数据收集。我们需要一种基于稳健数据的简单跨国基准方法，这些数据易于理解且易于收集。最近的疫情向我们展示了早期可用的疫情数据可能是什么样子，因为许多国家的标准新闻媒体每天都会发布统计数据。本文提供了分析数据的新方法，其中暴露仅被模糊理解，且暴露的定义本身可能随时间变化。我们利用这种低质量的暴露来分析和预测事件。以疫情为例，暴露是每日感染人数，而事件是医院每天新增感染患者数。本文给出了法国新冠数据中住院患者与感染人数的实例。