A COVINDEX based on a GAM beta regression model with an application to the COVID-19 pandemic in Italy

TL;DR

This paper introduces a new near real-time COVINDEX based on a GAM beta regression model to monitor COVID-19 pandemic evolution in Italy, providing a timely alternative to the traditional Rt index for policy decisions.

Contribution

The paper proposes a novel COVINDEX derived from GAM beta regression predictions, improving real-time epidemic monitoring over existing methods like Rt.

Findings

COVINDEX effectively tracks pandemic trends in Italy.

The index provides timely insights for public health decision-making.

Comparison shows COVINDEX complements Rt with reduced delay.

Abstract

Detecting changes in COVID-19 disease transmission over time is a key indicator of epidemic growth.Near real-time monitoring of the pandemic growth is crucial for policy makers and public health officials who need to make informed decisions about whether to enforce lockdowns or allow certain activities. The effective reproduction number Rt is the standard index used in many countries for this goal. However, it is known that due to the delays between infection and case registration, its use for decision making is somewhat limited. In this paper a near real-time COVINDEX is proposed for monitoring the evolution of the pandemic. The index is computed from predictions obtained from a GAM beta regression for modelling the test positive rate as a function of time. The proposal is illustrated using data on COVID-19 pandemic in Italy and compared with Rt. A simple chart is also proposed for monitoring local and national outbreaks by policy makers and public health officials.