What Lessons can Be Learned From the Management of the COVID-19 Pandemic?

Gerry A. Quinn, Ronan Connolly, Coilín ÓhAiseadha, Paul Hynds, Philipp Bagus, Ronald B. Brown, Carlos F. Cáceres, Clare Craig, Michael Connolly, Jose L. Domingo, Norman Fenton, Paul Frijters, Steven Hatfill, Raymond Heymans, Ari R. Joffe, Rosamond Jones, Gordan Lauc, Therese Lawrie, Robert W. Malone, Alan MordueGreta Mushet, Anton O’Connor, Jane Orient, José Antonio Peña-Ramos, Harvey A. Risch, Jessica Rose, Antonio Sánchez-Bayón, Ricardo F. Savaris, Michaéla C. Schippers, Dragos Simandan, Karol Sikora, Willie Soon, Yaffa Shir-Raz, Demetrios A. Spandidos, Beny Spira, Aristides M. Tsatsakis, Harald Walach

Research output: Contribution to journalArticlepeer-review

Abstract

During the COVID-19 pandemic (2020–2023), governments around the world implemented an unprecedented array of non-pharmaceutical interventions (NPIs) to control the spread of SARS-CoV-2. From early 2021, these were accompanied by major population-wide COVID-19 vaccination programmes–often using novel mRNA/DNA technology, although some countries used traditional vaccines. Both the NPIs and the vaccine programmes were apparently justified by highly concerning model projections of how the pandemic could progress in their absence. Efforts to reduce the spread of misinformation during the pandemic meant that differing scientific opinions on each of these aspects inevitably received unequal weighting. In this perspective review, based on an international multi-disciplinary collaboration, we identify major problems with many aspects of these COVID-19 policies as they were implemented. We show how this resulted in adverse impacts for public health, society, and scientific progress. Therefore, we propose seven recommendations to reduce such adverse consequences in the future.

Original languageEnglish
Article number1607727
JournalInternational Journal of Public Health
Volume70
DOIs
Publication statusPublished - 2025

Keywords

  • COVID-19
  • COVID-19 vaccination
  • epidemiology
  • mathematical modelling
  • public health

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