Integrating innovation is key to system resilience against pandemics & antimicrobial resistance – risk is high

Emerging technologies and future directions in pandemic preparedness The landscape of pandemic threats continues to evolve, necessitating a dynamic and informed response that extends beyond our current knowledge base. The WHO blueprint list for pathogens with pandemic potential (revision in consultation) currently includes eight viral pathogens (COVID-19, Crimean-Congo haemorrhagic fever, Ebola virus disease and Marburg virus disease, Lassa fever, Middle East respiratory syndrome coronavirus (MERS-CoV) and Severe Acute Respiratory Syndrome (SARS), Nipah and henipaviral diseases, Rift Valley fever, Zika and ``Disease X'', representing a pathogen or pathogens that have not yet been shown to cause disease in humans.28).We know from genomic modelling that there are as many as 320,000 yet-to-be-characterized mammalian viruses, many of which possess zoonotic `Disease X' potential.29 This vast, unexplored virosphere represents a substantial challenge and underscores the need for continuous enhanced surveillance using for example, advanced next generation sequencing technologies. Scientific progress will be pivotal in shaping our preparedness for future pandemics, which are likely to differ significantly from previous outbreaks. The next response to a pandemic threat should leverage the latest advances in technology, including the expanding field of artificial intelligence (AI). AI's integration into pandemic preparedness and response strategies represents a paradigm shift in how we will approach future outbreaks, which are likely to differ significantly from those in the past. For example, the use of AI to predict protein structure and functionality from genomic data is currently being revolutionised by tools such as AlphaFold.30 In a pandemic scenario, rapid insights into the protein structure of a novel virus could significantly shorten the timeline for developing effective therapeutics and vaccines. AI algorithms are also adept at analysing vast amounts of data to model disease spread and predict future outbreaks, incorporating international travel patterns and climate change. AI tools can be used to enhance disease surveillance systems by analysing data from multiple sources, such as social media, news reports, and healthcare databases. AI-driven diagnostic tools also have the potential to provide rapid analysis of clinical data, such as imaging and laboratory results, to assist in the early detection of infectious diseases.31 During a pandemic, these tools could be pivotal in screening large populations quickly and efficiently. Conclusions Infectious diseases have been a significant health concern in the UK and around the world since the emergence of modern medical practice. The 20th century witnessed remarkable progress in combating such diseases, with the advent of antibiotics, vaccination, and improved hygiene. Today's infectious disease threats are increasingly shaped by anthropogenic change. The rise of antimicrobial resistance, the expansion of vector habitats due to climate change and the global growth and movement of people are increasing the risk of emerging infectious diseases. Accelerated innovation with new technologies and team science will be required to monitor and control the threat.