Evaluating the theoretical performance of aircraft wastewater monitoring as a tool for SARS-CoV-2 surveillance (preprint)

Background Air travel plays an import role in the cross-border spread of infectious diseases. During the SARS-CoV-2 pandemic many countries introduced strict border testing protocols to monitor the incursion of the virus. However, the high implementation cost and significant inconvenience to passengers has led public health authorities to consider alternative methods of disease surveillance at borders. Aircraft wastewater monitoring has been proposed as one such alternative. In this paper we assess the theoretical limits of aircraft wastewater monitoring and compare its performance to post-arrival border screening approaches. Methods We use an infectious disease model to simulate an unmitigated SARS-CoV-2 epidemic in a seed country. Seeding of the epidemic into the United Kingdom (UK) is simulated through daily flights between the two countries. We use a probabilistic approach to estimate the time of first detection of the disease in the UK in both aircraft wastewater and respiratory swab screening at the border. Results For simulations across a broad range of model parameters, our analysis indicates that the median time between the first incursion of a pathogen and its detection in wastewater would be approximately 17 days (IQR: 7 - 28 days), resulting in a median of 25 cumulative cases (IQR: 6 - 84 cases) in the UK at the point of detection. Comparisons to respiratory swab screening suggest that aircraft wastewater monitoring is as effective as screening of 20% of passengers at the border, using a test with 95% sensitivity. For testing regimes with sensitivity of 85% or less, the required coverage to outperform wastewater monitoring increases to 30%. These results demonstrate the potential use cases of aircraft wastewater monitoring and its utility in a wider system of public health surveillance.

Comparison of variant callers for wastewater-based epidemiology (preprint)

Wastewater-based epidemiology (WBE) has been extensively used during the COVID-19 pandemic to detect and monitor the spread of the SARS-CoV-2 virus and its variants. It has also proven to be an excellent tool to complement and support insights gained from reported clinical data. Globally, many groups have developed bioinformatics pipelines to analyse sequencing data from wastewater. Accurate calling of mutations from RNA extracted from wastewater samples is key in supporting clinical data to make informed decisions on the prevalence of variants, as well as in the use of WBE as a molecular surveillance tool. However, wastewater samples can be challenging to extract and sequence, and performance of variant-calling algorithms in this context has, so far, not been investigated. Analysis of the data and assignment of circulating variants depends heavily on the accuracy of the variant caller, particularly given the degraded nature of the viral RNA and the heterogeneous nature of metagenomic samples. To address this, we compared the performance of six variant callers (VarScan, iVAR, GATK, FreeBayes, LoFreq and BCFtools), used widely in bioinformatics pipelines, on 19 synthetic samples with a known mix of three different SARS-CoV-2 variant genomes (Alpha, Beta and Delta), as well as 13 wastewater samples collected in London between the 15 th and 18 th December 2021. Using the Quasimodo benchmarking tool to compare the six variant callers, we assessed the fundamental parameters of recall (sensitivity) and precision (specificity) in confirming the presence of a variant within the population. Our results show that BCFtools, FreeBayes and VarScan called the expected mutations with higher precision and recall than iVAR or GATK, although the latter identified more expected defining mutations. LoFreq gave the least reliable results due to the high number of false positive mutations detected, resulting in lower precision. Similar results were obtained for both the synthetic and wastewater samples.