Low quality exposure and point processes with a view to the first phase of a pandemic

TL;DR

This paper introduces a simple, robust methodology for analyzing early pandemic data characterized by uncertain and evolving exposure definitions, demonstrated through French COVID-19 hospitalization and infection data.

Contribution

It proposes a new approach for analyzing low-quality, changing exposure data during the initial phase of a pandemic, facilitating early forecasting and understanding.

Findings

Effective analysis of early pandemic data with uncertain exposure.

Forecasting of infection and hospitalization trends using the proposed method.

Demonstration with French COVID-19 data showing practical applicability.

Abstract

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.