The utilization of environmental genomic data in the prediction of emerging and endemic pathogen dynamics

The current and future trends of climate change will lead to increasing challenges for public health. Changes in the environment, extreme weather events, and pressure on the human/wildlife interface are leading to a concerning rise in new and re-emerging zoonotic diseases. In addition, changes in population-level immunity after the Covid-19 pandemic have led to increasingly complex dynamics for pathogens, including respiratory syncytial virus (RSV) and Influenza, and the evolving situation of (re)-emergent pathogens, such as mpox and potentially the highly pathogenic avian influenza (H5N1). Understanding these complex health challenges requires expertise in each constituent part of the system, such as genomics, microbiology, ecology, and epidemiology, as well as cutting-edge statistical and mathematical techniques to provide a means to synthesize and make sense of the whole system. With the ongoing growth in environmental and animal data availability, such as wastewater data, there is a need to develop an ecosystem view of all exposures and their effects and to deploy new analytical tools which could combine environmental, clinical, and animal data to promote evidence-based public health decisions. Developing such methods will allow for a better understanding of disease dynamics that accounts for its full ecology within the one health context and has the potential to provide a robust and reliable way to estimate incidence, severe outcomes, growth rate, and forecasting. This research program will ultimately support the modelling of the relationship between wastewater data and dynamics of disease to inform early warning systems. The primary goal is to enable rapid response and evidence-based decisions by decision-makers and, ultimately, to inform public health officials and support surveillance.