Modelling the disorganised ecology of post-pandemic respiratory viruses

Prior to the COVID-19 pandemic, the circulation of respiratory viruses during the winter was relatively predictable. However, outbreak control measures implemented throughout the pandemic have thrown regular seasonal patterns into disarray. After historically low circulation of seasonal respiratory viruses like influenza and respiratory syncytial virus (RSV), they have returned, characterised by unusual transmission. Various factors may be contributing to this phenomenon, such as the lack of exposure to these viruses during the period of heavy restrictions. Those that would have normally been infected would have gained immunity, protection from infection. However, with most seasonal respiratory viruses, immunity is not lifelong, meaning that after a certain period there is the potential for reinfection, thus creating patterns of infection.

This PhD project will use the disruption to usual seasonal circulation of respiratory viruses, caused by the COVID-19 pandemic, and the following reorganisation of these viruses to better understand how they circulate in the environment and how circulation patterns are created and maintained.

This project will use publicly available data on respiratory infections from the UK Health Security Agency (HSA). In addition, the possibility of using publicly available European datasets hosted at the European Centre for Disease Prevention and Control (ECDC) for analysis will be considered with the supervisors. An analysis on historical circulation of the viruses of interest will be done so that it can be compared to current circulation and during the "pandemic years". Mathematical models will be designed and developed to investigate biological or sociological reasons for changes in post-pandemic seasonal respiratory virus circulation.

The research method will significantly contribute to the development of quantitative skills. In addition, the approach to the research question will be multidisciplinary since elements of public health, virology, and immunology as well as statistics and computer programming will be considered. Therefore, depth of knowledge will be obtained on infectious disease modelling and breadth of knowledge will also be acquired due to the nature of the research and the working environment.

This project offers an opportunity to better understand the drivers of seasonal respiratory virus transmission. This knowledge can be useful to establish strategies to mitigate the impact of respiratory virus infections, reduce their burden on the population and alleviate the strain they put on healthcare systems. The work from this project will be useful to governments and public health; equipped with more knowledge they can provide better guidance and advice as well as design more effective and targeted future interventions.