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

arXiv: https://arxiv.org/abs/2101.04620 · HTML: https://mlsanity.com/paper/2101.04620/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2021.

## Built on by (most cited first, 1 of 1)

- [Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis](https://mlsanity.com/paper/2207.10939/) (2022)

## Abstract (arXiv)

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.