Quickest Detection and Forecast of Pandemic Outbreaks: Analysis of COVID-19 Waves

TL;DR

This paper presents an integrated framework for rapid detection, estimation, and forecasting of COVID-19 waves using publicly available data, aiming to improve response times and accuracy in pandemic management.

Contribution

It introduces a novel detection-estimation-forecasting framework that learns pandemic features and predicts outbreak dynamics more reliably than existing methods.

Findings

Successfully analyzed COVID-19 second and third waves in the USA.

Demonstrated rapid detection of exponential growth phases.

Provided accurate forecasts of pandemic evolution.

Abstract

The COVID-19 pandemic has, worldwide and up to December 2020, caused over 1.7 million deaths, and put the world's most advanced healthcare systems under heavy stress. In many countries, drastic restrictive measures adopted by political authorities, such as national lockdowns, have not prevented the outbreak of new pandemic's waves. In this article, we propose an integrated detection-estimation-forecasting framework that, using publicly available data, is designed to: (i) learn relevant features of the pandemic (e.g., the infection rate); (ii) detect as quickly as possible the onset (or the termination) of an exponential growth of the contagion; and (iii) reliably forecast the pandemic evolution. The proposed solution is validated by analyzing the COVID-19 second and third waves in the USA.