The COVID-19 pandemic has put unprecedented pressure on public health resources around the world. From adversity, opportunities have arisen to measure the state and dynamics of human disease at a scale not seen before. In the United Kingdom, the evidence that wastewater could be used to monitor the SARS-CoV-2 virus prompted the development of National wastewater surveillance programmes. The scale and pace of this work has proven to be unique in monitoring of virus dynamics at a national level, demonstrating the importance of wastewater-based epidemiology (WBE) for public health protection. Beyond COVID-19, it can provide additional value for monitoring and informing on a range of biological and chemical markers of human health. A discussion of measurement uncertainty associated with surveillance of wastewater, focusing on lessons-learned from the UK programmes monitoring COVID-19 is presented, showing that sources of uncertainty impacting measurement quality and interpretation of data for public health decision-making, are varied and complex. While some factors remain poorly understood, we present approaches taken by the UK programmes to manage and mitigate the more tractable sources of uncertainty. This work provides a platform to integrate uncertainty management into WBE activities as part of global One Health initiatives beyond the pandemic.

Accurate surveillance of the COVID-19 pandemic can be weakened by under-reporting of cases, particularly due to asymptomatic or pre-symptomatic infections, resulting in bias. Quantification of SARS-CoV-2 RNA in wastewater can be used to infer infection prevalence, but uncertainty in sensitivity and considerable variability has meant that accurate measurement remains elusive. Here, we use data from 45 sewage sites in England, covering 31% of the population, and estimate SARS-CoV-2 prevalence to within 1.1% of estimates from representative prevalence surveys (with 95% confidence). Using machine learning and phenomenological models, we show that differences between sampled sites, particularly the wastewater flow rate, influence prevalence estimation and require careful interpretation. We find that SARS-CoV-2 signals in wastewater appear 4–5 days earlier in comparison to clinical testing data but are coincident with prevalence surveys suggesting that wastewater surveillance can be a leading indicator for symptomatic viral infections. Surveillance for viruses in wastewater complements and strengthens clinical surveillance, with significant implications for public health.

Little is known about the forces that determine the assembly of diverse bacterial communities inhabiting drinking water treatment filters and how this affects drinking water quality. Two contrasting ecological theories can help to understand how natural microbial communities assemble; niche theory and neutral theory, where environmental deterministic factors or stochastic factors predominate respectively. This study investigates the development of the microbial community on two common contrasting filter materials (quartz sand and granular activated carbon-GAC), to elucidate the main factors governing their assembly, through the evaluation of environmental (i.e. filter medium type) and stochastic forces (random deaths, births and immigration). Laboratory-scale filter columns were used to mimic a rapid gravity filter; the microbiome of the filter materials, and of the filter influent and effluent, was characterised using next generation 16S rRNA gene amplicon sequencing and flow-cytometry. Chemical parameters (i.e. dissolved organic carbon, trihalomethanes formation) were also monitored to assess the final effluent quality. The filter communities seemed to be strongly assembled by selection rather than neutral processes, with only 28% of those OTUs shared with the source water detected on the filter medium following predictions using a neutral community model. GAC hosted a phylogenetically more diverse community than sand. The two filter media communities seeded the effluent water, triggering differences in both water quality and community composition of the effluents. Overall, GAC proved to be better than sand in controlling microbial growth, by promoting higher bacterial decay rates and hosting less bacterial cells, and showed better performance for putative pathogen control by leaking less Legionella cells into the effluent water.

Although mathematical modelling has reached a degree of maturity in the last decades, microbial ecology is still developing, albeit at a rapid pace thanks to new insights provided by modern molecular tools. However, whilst microbiologists have long enjoyed the perspectives that particular mathematical frameworks can provide, there remains a reluctance to fully embrace the potential of models, which appear too complex, esoteric or distant from the “real-world”. Nevertheless there is a strong case for pursuing the development of mathematical models to describe microbial behaviour and interactions, dynamically, spatially and across scales. Here we put forward perspectives on the current state of mathematical modelling in microbial ecology, looking back at the developments that have defined the synergies between the disciplines, and outline some of the existing challenges that motivate us to provide practical models in the hope that greater engagement with empiricists and practitioners in the microbiological domain may be achieved. We also indicate recent advances in modelling that have had impact in both the fundamental understanding of microbial ecology and its practical application in engineered biological systems. In this way, it is anticipated that interest can be garnered from across the microbiological spectrum resulting in a broader uptake of mathematical concepts in lecture theatres, laboratories and industrial systems