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Global patterns and drivers of influenza decline during the COVID-19 pandemic

  • Francesco Bonacina
  • , Pierre Yves Boëlle
  • , Vittoria Colizza
  • , Olivier Lopez
  • , Maud Thomas
  • , Chiara Poletto

Research output: Contribution to journalArticlepeer-review

Abstract

Objectives: The influenza circulation reportedly declined during the COVID-19 pandemic in many countries. The occurrence of this change has not been studied worldwide nor its potential drivers. Methods: The change in the proportion of positive influenza samples reported by country and trimester was computed relative to the 2014-2019 period using the FluNet database. Random forests were used to determine predictors of change from demographical, weather, pandemic preparedness, COVID-19 incidence, and pandemic response characteristics. Regression trees were used to classify observations according to these predictors. Results: During the COVID-19 pandemic, the influenza decline relative to prepandemic levels was global but heterogeneous across space and time. It was more than 50% for 311 of 376 trimesters-countries and even more than 99% for 135. COVID-19 incidence and pandemic preparedness were the two most important predictors of the decline. Europe and North America initially showed limited decline despite high COVID-19 restrictions; however, there was a strong decline afterward in most temperate countries, where pandemic preparedness, COVID-19 incidence, and social restrictions were high; the decline was limited in countries where these factors were low. The “zero-COVID” countries experienced the greatest decline. Conlusion: Our findings set the stage for interpreting the resurgence of influenza worldwide.

Original languageEnglish
Pages (from-to)132-139
Number of pages8
JournalInternational Journal of Infectious Diseases
Volume128
DOIs
Publication statusPublished - 1 Mar 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • COVID-19 pandemic
  • Global analysis
  • Influenza
  • Regression trees

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