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Background: By March, 2023, 54 countries, areas, and territories (hereafter CAT) in the WHO European Region had reported more than 2·2 million COVID-19-related deaths to the WHO Regional Office for Europe. Here, we estimated how many lives were directly saved by vaccinating adults in the WHO European Region from December, 2020, to March, 2023., Methods: In this retrospective surveillance study, we estimated the number of lives directly saved by age group, vaccine dose, and circulating variant-of-concern (VOC) period, regionally and nationally, using weekly data on COVID-19 mortality and infection, COVID-19 vaccination uptake, and SARS-CoV-2 virus characterisations by lineage downloaded from The European Surveillance System on June 11, 2023, as well as vaccine effectiveness data from the literature. We included data for six age groups (25-49 years, 50-59 years, ≥60 years, 60-69 years, 70-79 years, and ≥80 years). To be included in the analysis, CAT needed to have reported both COVID-19 vaccination and mortality data for at least one of the four older age groups. Only CAT that reported weekly data for both COVID-19 vaccination and mortality by age group for 90% of study weeks or more in the full study period were included. We calculated the percentage reduction in the number of expected and reported deaths., Findings: Between December, 2020, and March, 2023, in 34 of 54 CAT included in the analysis, COVID-19 vaccines reduced deaths by 59% overall (CAT range 17-82%), representing approximately 1·6 million lives saved (range 1·5-1·7 million) in those aged 25 years or older: 96% of lives saved were aged 60 years or older and 52% were aged 80 years or older; first boosters saved 51% of lives, and 60% were saved during the Omicron period., Interpretation: Over nearly 2·5 years, most lives saved by COVID-19 vaccination were in older adults by first booster dose and during the Omicron period, reinforcing the importance of up-to-date vaccination among the most at-risk individuals. Further modelling work should evaluate indirect effects of vaccination and public health and social measures., Funding: US Centers for Disease Control and Prevention., Competing Interests: Declaration of interests GD reports that the predecessor of the organisation he works for, Public Health England, received an unrestricted grant from GSK to undertake a study on the outcome of patients who received parenteral zanamavir. The funder received data and interim reports from Public Health England but did not influence analysis and reporting of the study. GD had no involvement in the GSK-funded study on parenteral zanamavir. Furthermore, the currently submitted work was part of the public health response activities to COVID-19 and had no relationship to GSK or the study on parenteral zanamivir. EP has received a personal grant from the Finnish Medical Foundation for PhD studies. JM declares that Public Health Scotland received funding from the EU Horizon 2020 programme for work in describing the epidemiology of COVID-19 and its impact on primary and secondary care as a partner in the IMOVE-COVID-19 project. MK declares having received consulting fees from Gilead Sciences for advising on development of a clinical module for collection of patient-reported outcome data from people living with HIV, and having received an honoraria from GESIDA for speaking at an annual conference on patient-reported outcome measures for people with HIV. All other authors declare no competing interests., (Copyright © 2024 World Health Organization. Published by Elsevier Ltd. All rights reserved, including those for text and data mining, AI training, and similar technologies.)

Background: The emergence of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in early 2020 and subsequent implementation of public health and social measures (PHSM) disrupted the epidemiology of respiratory viruses. This work describes the epidemiology of respiratory syncytial virus (RSV) observed during two winter seasons (weeks 40-20) and inter-seasonal periods (weeks 21-39) during the pandemic between October 2020 and September 2022., Methods: Using data submitted to The European Surveillance System (TESSy) by countries or territories in the World Health Organization (WHO) European Region between weeks 40/2020 and 39/2022, we aggregated country-specific weekly RSV counts of sentinel, non-sentinel and Severe Acute Respiratory Infection (SARI) surveillance specimens and calculated percentage positivity. Results for both 2020/21 and 2021/22 seasons and inter-seasons were compared with pre-pandemic 2016/17 to 2019/20 seasons and inter-seasons., Results: Although more specimens were tested than in pre-COVID-19 pandemic seasons, very few RSV detections were reported during the 2020/21 season in all surveillance systems. During the 2021 inter-season, a gradual increase in detections was observed in all systems. In 2021/22, all systems saw early peaks of RSV infection, and during the 2022 inter-seasonal period, patterns of detections were closer to those seen before the COVID-19 pandemic., Conclusion: RSV surveillance continued throughout the COVID-19 pandemic, with an initial reduction in transmission, followed by very high and out-of-season RSV circulation (summer 2021) and then an early start of the 2021/22 season. As of the 2022/23 season, RSV circulation had not yet normalised., Competing Interests: The following authors declare having received funding from the Innovative Medicines Initiative (IMI): Adam Meijer (The Netherlands) and Anne Teirlinck (The Netherlands). All other authors have no conflicts of interest to declare., (© 2023 The Authors. Influenza and Other Respiratory Viruses published by John Wiley & Sons Ltd.)

Pandemics have the potential to incur significant health and economic impacts, and can reach a large number of countries from their origin within weeks. Early identification and containment of a newly emerged pandemic within the source country is key for minimising global impact. To identify a country's potential to control and contain a pathogen with pandemic potential, we compared the quality of a country's healthcare system against its global airline connectivity. Healthcare development was determined using three multi-factorial indices, while detailed airline passenger data was used to identify the global connectivity of all countries. Proximities of countries to a putative 'Worst Case Scenario' (extreme high-connectivity and low-healthcare development) were calculated. We found a positive relationship between a country's connectivity and healthcare metrics. We also identified countries that potentially pose the greatest risk for pandemic dissemination, notably Dominican Republic, India and Pakistan. China and Mexico, both sources of recent influenza and coronavirus pandemics were also identified as among the highest risk countries. Collectively, lower-middle and upper-middle income countries represented the greatest risk, while high income countries represented the lowest risk. Our analysis represents an alternative approach to identify countries where increased within-country disease surveillance and pandemic preparedness may benefit global health., (© 2022. The Author(s).)

BackgroundA variety of airline passenger data sources are used for modelling the international spread of infectious diseases. Questions exist regarding the suitability and validity of these sources.AimWe conducted a systematic review to identify the sources of airline passenger data used for these purposes and to assess validation of the data and reproducibility of the methodology.MethodsArticles matching our search criteria and describing a model of the international spread of human infectious disease, parameterised with airline passenger data, were identified. Information regarding type and source of airline passenger data used was collated and the studies' reproducibility assessed.ResultsWe identified 136 articles. The majority (n = 96) sourced data primarily used by the airline industry. Governmental data sources were used in 30 studies and data published by individual airports in four studies. Validation of passenger data was conducted in only seven studies. No study was found to be fully reproducible, although eight were partially reproducible.LimitationsBy limiting the articles to international spread, articles focussed on within-country transmission even if they used relevant data sources were excluded. Authors were not contacted to clarify their methods. Searches were limited to articles in PubMed, Web of Science and Scopus.ConclusionWe recommend greater efforts to assess validity and biases of airline passenger data used for modelling studies, particularly when model outputs are to inform national and international public health policies. We also recommend improving reporting standards and more detailed studies on biases in commercial and open-access data to assess their reproducibility.

Mathematical models can aid in the understanding of the risks associated with the global spread of infectious diseases. To assess the current state of mathematical models for the global spread of infectious diseases, we reviewed the literature highlighting common approaches and good practice, and identifying research gaps. We followed a scoping study method and extracted information from 78 records on: modelling approaches; input data (epidemiological, population, and travel) for model parameterization; model validation data. We found that most epidemiological data come from published journal articles, population data come from a wide range of sources, and travel data mainly come from statistics or surveys, or commercial datasets. The use of commercial datasets may benefit the modeller, however makes critical appraisal of their model by other researchers more difficult. We found a minority of records (26) validated their model. We posit that this may be a result of pandemics, or far-reaching epidemics, being relatively rare events compared with other modelled physical phenomena (e.g. climate change). The sparsity of such events, and changes in outbreak recording, may make identifying suitable validation data difficult. We appreciate the challenge of modelling emerging infections given the lack of data for both model parameterisation and validation, and inherent complexity of the approaches used. However, we believe that open access datasets should be used wherever possible to aid model reproducibility and transparency. Further, modellers should validate their models where possible, or explicitly state why validation was not possible., (Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.)