🤖 AI Summary
During the early phase of epidemics, modeling is hindered by ambiguously defined exposure variables (e.g., infection counts), severe data scarcity, and poor data quality. Method: This paper proposes a point-process modeling framework for “low-quality exposure,” based on an inhomogeneous Poisson process that incorporates a time-varying exposure function and robust statistical estimation—enabling dynamic evolution of exposure definitions over time without reliance on high-fidelity epidemiological parameters. Contribution/Results: We formally define “low-quality exposure” for the first time and develop a lightweight, real-time deployable cross-national early-warning model. Evaluated on early-phase French COVID-19 hospitalization and infection data, the method significantly improves short-term forecasting stability and interpretability of daily new hospitalizations, while demonstrating strong cross-country adaptability.
📝 Abstract
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.