Correlation analysis of the dispersion of SARS-CoV-2 in Mexico

TL;DR

This paper introduces a correlation analysis method for non-stationary pandemic data across regions, focusing on societal responses' impact on disease spread and informing future public health strategies.

Contribution

It presents a novel correlation analysis approach tailored for non-stationary pandemic data, emphasizing post-pandemic societal response effects.

Findings

Identifies how societal responses influence regional pandemic dynamics

Provides insights into the collective behavior of regions during pandemics

Enhances understanding of complex pandemic spread mechanisms

Abstract

In this paper, we propose a method to analyze correlations in pandemic-related data across different geographical regions, relying on the analysis of correlations for non-stationary time series, which are typical of pandemic data. Unlike traditional epidemiological approaches focused on medical and modeling perspectives during a pandemic, our method emphasizes post-pandemic analysis to assess how societal responses; such as lockdowns, travel restrictions, mobility patterns, and vaccination campaigns, manifest in the collective behavior of regions. These insights can inform future public health strategies and enhance understanding of the complex dynamics underlying pandemic spread and control.