Challenges in realising the potential of wastewater-based epidemiology to quantitatively monitor and predict the spread of disease

From pandemic to endemic: Divergence of COVID-19 positive-tests and hospitalization numbers from SARS-CoV-2 RNA levels in wastewater of Rochester, Minnesota

Traditionally, public health surveillance relied on individual-level data but recently wastewater-based epidemiology (WBE) for the detection of infectious diseases including COVID-19 became a valuable tool in the public health arsenal. Here, we use WBE to follow the course of the COVID-19 pandemic in Rochester, Minnesota (population 121,395 at the 2020 census), from February 2021 to December 2022. We monitored the impact of SARS-CoV-2 infections on public health by comparing three sets of data: quantitative measurements of viral RNA in wastewater as an unbiased reporter of virus level in the community, positive results of viral RNA or antigen tests from nasal swabs reflecting community reporting, and hospitalization data. From February 2021 to August 2022 viral RNA levels in wastewater were closely correlated with the oscillating course of COVID-19 case and hospitalization numbers. However, from September 2022 cases remained low and hospitalization numbers dropped, whereas viral RNA levels in wastewater continued to oscillate. The low reported cases may reflect virulence reduction combined with abated inclination to report, and the divergence of virus levels in wastewater from reported cases may reflect COVID-19 shifting from pandemic to endemic. WBE, which also detects asymptomatic infections, can provide an early warning of impending cases, and offers crucial insights during pandemic waves and in the transition to the endemic phase.

Integrating wastewater and randomised prevalence survey data for national COVID surveillance

During the COVID-19 pandemic, studies in a number of countries have shown how wastewater can be used as an efficient surveillance tool to detect outbreaks at much lower cost than traditional prevalence surveys. In this study, we consider the utilisation of wastewater data in the post-pandemic setting, in which collection of health data via national randomised prevalence surveys will likely be run at a reduced scale; hence an affordable ongoing surveillance system will need to combine sparse prevalence data with non-traditional disease metrics such as wastewater measurements in order to estimate disease progression in a cost-effective manner. Here, we use data collected during the pandemic to model the dynamic relationship between spatially granular wastewater viral load and disease prevalence. We then use this relationship to nowcast local disease prevalence under the scenario that (i) spatially granular wastewater data continue to be collected; (ii) direct measurements of prevalence are only available at a coarser spatial resolution, for example at national or regional scale. The results from our cross-validation study demonstrate the added value of wastewater data in improving nowcast accuracy and reducing nowcast uncertainty. Our results also highlight the importance of incorporating prevalence data at a coarser spatial scale when nowcasting prevalence at fine spatial resolution, calling for the need to maintain some form of reduced-scale national prevalence surveys in non-epidemic periods. The model framework is disease-agnostic and could therefore be adapted to different diseases and incorporated into a multiplex surveillance system for early detection of emerging local outbreaks.

Assessment of a SARS-CoV-2 wastewater monitoring program in El Paso, Texas, from November 2020 to June 2022

The border city of El Paso, Texas, and its water utility, El Paso Water, initiated a SARS-CoV-2 wastewater monitoring program to assess virus trends and the appropriateness of a wastewater monitoring program for the community. Nearly weekly sample collection at four wastewater treatment facilities (WWTFs), serving distinct regions of the city, was analyzed for SARS-CoV-2 genes using the CDC 2019-Novel coronavirus Real-Time RT-PCR diagnostic panel. Virus concentrations ranged from 86.7 to 268,000 gc/L, varying across time and at each WWTF. The lag time between virus concentrations in wastewater and reported COVID-19 case rates (per 100,00 population) ranged from 4-24 days for the four WWTFs, with the strongest trend occurring from November 2021 - June 2022. This study is an assessment of the utility of a geographically refined SARS-CoV-2 wastewater monitoring program to supplement public health efforts that will manage the virus as it becomes endemic in El Paso.

Effectiveness of environmental surveillance of SARS-CoV-2 as an early-warning system: Update of a systematic review during the second year of the pandemic

The aim of this updated systematic review was to offer an overview of the effectiveness of environmental surveillance (ES) of SARS-CoV-2 as a potential early-warning system (EWS) for COVID-19 and new variants of concerns (VOCs) during the second year of the pandemic. An updated literature search was conducted to evaluate the added value of ES of SARS-CoV-2 for public health decisions. The search for studies published between June 2021 and July 2022 resulted in 1,588 publications, identifying 331 articles for full-text screening. A total of 151 publications met our inclusion criteria for the assessment of the effectiveness of ES as an EWS and early detection of SARS-CoV-2 variants. We identified a further 30 publications among the grey literature. ES confirms its usefulness as an EWS for detecting new waves of SARS-CoV-2 infection with an average lead time of 1-2 weeks for most of the publication. ES could function as an EWS for new VOCs in areas with no registered cases or limited clinical capacity. Challenges in data harmonization and variant detection require standardized approaches and innovations for improved public health decision-making. ES confirms its potential to support public health decision-making and resource allocation in future outbreaks.

LoCKAmp: lab-on-PCB technology for <3 minute virus genetic detection

The recent COVID-19 outbreak highlighted the need for lab-on-chip diagnostic technology fit for real-life deployment in the field. Existing bottlenecks in multistep analytical microsystem integration and upscalable, standardized fabrication techniques delayed the large-scale deployment of lab-on-chip solutions during the outbreak, throughout a global diagnostic test shortage. This study presents a technology that has the potential to address these issues by redeploying and repurposing the ubiquitous printed circuit board (PCB) technology and manufacturing infrastructure. We demonstrate the first commercially manufactured, miniaturised lab-on-PCB device for loop-mediated isothermal amplification (LAMP) genetic detection of SARS-CoV-2. The system incorporates a mass-manufactured, continuous-flow PCB chip with ultra-low cost fluorescent detection circuitry, rendering it the only continuous-flow μLAMP platform with off-the-shelf optical detection components. Ultrafast, SARS-CoV-2 RNA amplification in wastewater samples was demonstrated within 2 min analysis, at concentrations as low as 17 gc μL-1. We further demonstrate our device operation by detecting SARS-CoV-2 in 20 human nasopharyngeal swab samples, without the need for any RNA extraction or purification. This renders the presented miniaturised nucleic-acid amplification-based diagnostic test the fastest reported SARS-CoV-2 genetic detection platform, in a practical implementation suitable for deployment in the field. This technology can be readily extended to the detection of alternative pathogens or genetic targets for a very broad range of applications and matrices. LoCKAmp lab-on-PCB chips are currently mass-manufactured in a commercial, ISO-compliant PCB factory, at a small-scale production cost of £2.50 per chip. Thus, with this work, we demonstrate a high technology-readiness-level lab-on-chip-based genetic detection system, successfully benchmarked against standard analytical techniques both for wastewater and nasopharyngeal swab SARS-CoV-2 detection.

Trends in SARS-CoV-2 clinically confirmed cases and viral load in wastewater: A critical alignment for Padua city (NE Italy)

Since the beginning of the COVID-19 pandemic, wastewater-based epidemiology (WBE) has been depicted as a promising environmental surveillance tool and early warning system. Predictive models for the estimate of COVID-19 cases from wastewater viral loads also earned lot of interest and are currently under development. Hereby a pilot study that compares WBE surveillance data with confirmed cases, total hospitalizations, doses of vaccine administered and predominance of coronavirus variants. Composite 24hrs wastewater samples were collected weekly between September 2021 and July 2022 from Padua wastewater treatment plant. Samples were processed following a previously published method. One-step RT-qPCR was performed for quantification, adapting an Orf1b-nsp14 gene assay. Variant replacement was derived from the monthly bulletins of the Italian National Health Institute. Aggregate data on vaccine doses administered and on COVID-19 prevalence and hospitalizations were retrieved from official reports. Eighty-two samples were processed. Viral loads highlighted 3 major peaks in January, April and July 2022. Quantitation of SARS-CoV-2 in wastewater and clinical surveillance resulted temporally juxtaposable. However, variation of the two curves is not proportional. SARS-CoV-2 showed its highest peak in April, whereas maximum COVID-19 prevalence was achieved in January. Total hospitalizations followed the prevalence trend. Omicron BA.1 started to replace the Delta variant in December 2021. Subsequently, the shift towards Omicron BA.2 occurred between February and April 2022. Finally, BA.4/5 attested around June, somehow preceding the summer peak. Emergence of Omicron BA.1 over Delta could be a possible driver of the increase in both clinical cases and wastewater viral load in January 2022. In late March 2022, Omicron BA.2 replaced BA.1: this reflected in a steep increase of wastewater viral load, but not of COVID-19 confirmed cases. When a dramatic drop in the testing capacity of clinical surveillance occurred, WBE was possibly capable of detecting a substantial increase in viral circulation.

Exploring the depth and breadth of the genomics toolbox during the COVID-19 pandemic: insights from Aotearoa New Zealand

BACKGROUND

Genomic technologies have become routine in the surveillance and monitoring of the coronavirus disease 2019 (COVID-19) pandemic, as evidenced by the millions of SARS-CoV-2 sequences uploaded to international databases. Yet the ways in which these technologies have been applied to manage the pandemic are varied.

MAIN TEXT

Aotearoa New Zealand was one of a small number of countries to adopt an elimination strategy for COVID-19, establishing a managed isolation and quarantine system for all international arrivals. To aid our response, we rapidly set up and scaled our use of genomic technologies to help identify community cases of COVID-19, to understand how they had arisen, and to determine the appropriate action to maintain elimination. Once New Zealand pivoted from elimination to suppression in late 2021, our genomic response changed to focusing on identifying new variants arriving at the border, tracking their incidence around the country, and examining any links between specific variants and increased disease severity. Wastewater detection, quantitation and variant detection were also phased into the response. Here, we explore New Zealand's genomic journey through the pandemic and provide a high-level overview of the lessons learned and potential future capabilities to better prepare for future pandemics.

CONCLUSIONS

Our commentary is aimed at health professionals and decision-makers who might not be familiar with genetic technologies, how they can be used, and why this is an area with great potential to assist in disease detection and tracking now and in the future.

A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic

The potential utility of wastewater-based epidemiology as an early warning tool has been explored widely across the globe during the current COVID-19 pandemic. Methods to detect the presence of SARS-CoV-2 RNA in wastewater were developed early in the pandemic, and extensive work has been conducted to evaluate the relationship between viral concentration and COVID-19 case numbers at the catchment areas of sewage treatment works (STWs) over time. However, no attempt has been made to develop a model that predicts wastewater concentration at fine spatio-temporal resolutions covering an entire country, a necessary step towards using wastewater monitoring for the early detection of local outbreaks. We consider weekly averages of flow-normalised viral concentration, reported as the number of SARS-CoV-2N1 gene copies per litre (gc/L) of wastewater available at 303 STWs over the period between 1 June 2021 and 30 March 2022. We specify a spatially continuous statistical model that quantifies the relationship between weekly viral concentration and a collection of covariates covering socio-demographics, land cover and virus associated genomic characteristics at STW catchment areas while accounting for spatial and temporal correlation. We evaluate the model's predictive performance at the catchment level through 10-fold cross-validation. We predict the weekly viral concentration at the population-weighted centroid of the 32,844 lower super output areas (LSOAs) in England, then aggregate these LSOA predictions to the Lower Tier Local Authority level (LTLA), a geography that is more relevant to public health policy-making. We also use the model outputs to quantify the probability of local changes of direction (increases or decreases) in viral concentration over short periods (e.g. two consecutive weeks). The proposed statistical framework can predict SARS-CoV-2 viral concentration in wastewater at high spatio-temporal resolution across England. Additionally, the probabilistic quantification of local changes can be used as an early warning tool for public health surveillance.

Wastewater pandemic preparedness: Toward an end-to-end pathogen monitoring program

Molecular analysis of public wastewater has great potential as a harbinger for community health and health threats. Long-used to monitor the presence of enteric viruses, in particular polio, recent successes of wastewater as a reliable lead indicator for trends in SARS-CoV-2 levels and hospital admissions has generated optimism and emerging evidence that similar science can be applied to other pathogens of pandemic potential (PPPs), especially respiratory viruses and their variants of concern (VOC). However, there are substantial challenges associated with implementation of this ideal, namely that multiple and distinct fields of inquiry must be bridged and coordinated. These include engineering, molecular sciences, temporal-geospatial analytics, epidemiology and medical, and governmental and public health messaging, all of which present their own caveats. Here, we outline a framework for an integrated, state-wide, end-to-end human pathogen monitoring program using wastewater to track viral PPPs.