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<p>Although a worldwide health crisis, the <i>COVID-19</i> pandemic affected several geographical areas in Italy in very different ways in terms of infection rate, morbidity, and death. In the present work, we carefully studied the incidence rate in several Italian provinces and propose a complete data analysis strategy to explore, preprocess, and analyse the time series of <i>COVID-19</i> positive and hospitalised cases with a daily cadency. We applied a new procedure, developed for unevenly sampled data (Discrete Correlation Function), to perform the cross-correlation analysis looking at possible correlation between <i>COVID-19</i> positive and hospitalised cases with the air quality during the first pandemic wave. It is a completely new approach, that makes use of techniques used in transversal fields, such as signal processing and astronomy. The study suggests some plausible correlations between <i>COVID-19</i> time series and NO related air pollutants. Instead, differently from what often has been reported, we did not find any specific correlation between <i>COVID-19</i> infection and PM<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_4887_Article_IEq1.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="13" /> </InlineMediaObject> <EquationSource Format="TEX">\(_{10}\)</EquationSource> </InlineEquation> air pollutant. We further corroborate the results using a Machine Learning approach that uses Random Forest and the Permutation Feature Importance Analysis to include a wider set of possible risk factors, founding the same dependence between <i>COVID-19</i> cases and NO related air pollutants.</p>

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Dynamic and static analysis of environmental variables in coronavirus spread

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月5日

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  • Mirko Di Stefano,
  • Leonardo Aragão,
  • Giuseppe Ambrosio,
  • Diego Ciangottini,
  • Cristina Duma,
  • Pasquale Lubrano,
  • Barbara Martelli,
  • Davide Salomoni,
  • Giusy Sergi,
  • Daniele Spiga,
  • Fabrizio Stracci,
  • Elisabetta Ronchieri,
  • Loriano Storchi,
  • Sara Cutini

摘要

Although a worldwide health crisis, the COVID-19 pandemic affected several geographical areas in Italy in very different ways in terms of infection rate, morbidity, and death. In the present work, we carefully studied the incidence rate in several Italian provinces and propose a complete data analysis strategy to explore, preprocess, and analyse the time series of COVID-19 positive and hospitalised cases with a daily cadency. We applied a new procedure, developed for unevenly sampled data (Discrete Correlation Function), to perform the cross-correlation analysis looking at possible correlation between COVID-19 positive and hospitalised cases with the air quality during the first pandemic wave. It is a completely new approach, that makes use of techniques used in transversal fields, such as signal processing and astronomy. The study suggests some plausible correlations between COVID-19 time series and NO related air pollutants. Instead, differently from what often has been reported, we did not find any specific correlation between COVID-19 infection and PM \(_{10}\) air pollutant. We further corroborate the results using a Machine Learning approach that uses Random Forest and the Permutation Feature Importance Analysis to include a wider set of possible risk factors, founding the same dependence between COVID-19 cases and NO related air pollutants.