Measuring COVID-19 spreading speed through the mean time between infections indicator

TL;DR

This paper introduces the mean time between infections (MTBI) as a new metric to measure COVID-19 spreading speed, demonstrating its robustness across different data inputs and stages of the epidemic.

Contribution

The study proposes and validates the MTBI metric derived from a non-homogeneous Markov model as a reliable indicator of epidemic spreading speed.

Findings

MTBI remains consistent across different data inputs

Model parameters vary significantly with data calibration

MTBI effectively tracks epidemic waves over time

Abstract

We propose to use the mean time between infections (MTBI) metric as obtained from a recently introduced non-homogeneous Markov stochastic model. Different types of parameter calibration are performed. We estimate the MTBI using data from different time windows and from the whole stage history and compare the results. In order to detect waves and stages in the input data, a preprocessing filtering technique is applied. The results of applying this indicator to the COVID-19 reported data of infections from Argentina, Germany and the United States are shown. We find that the MTBI behaves similarly with respect to the different data inputs, whereas the model parameters completely change their behaviour. Evolution over time of the parameters and the MTBI indicator is also shown. We show evidence to support the claim that the MTBI is a rather good indicator in order to measure the spreading speed of an epidemic, having similar values whatever the input data size.