Seasonal and inter-seasonal RSV activity in the European Region during the COVID-19 pandemic from autumn 2020 to summer 2022.

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.

Estimating the potential for global dissemination of pandemic pathogens using the global airline network and healthcare development indices.

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.

Modelling the global spread of diseases: A review of current practice and capability.

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.