Research gaps and priorities for quantitative microbial risk assessment (QMRA).

The coronavirus disease 2019 pandemic highlighted the need for more rapid and routine application of modeling approaches such as quantitative microbial risk assessment (QMRA) for protecting public health. QMRA is a transdisciplinary science dedicated to understanding, predicting, and mitigating infectious disease risks. To better equip QMRA researchers to inform policy and public health management, an Advances in Research for QMRA workshop was held to synthesize a path forward for QMRA research. We summarize insights from 41 QMRA researchers and experts to clarify the role of QMRA in risk analysis by (1) identifying key research needs, (2) highlighting emerging applications of QMRA; and (3) describing data needs and key scientific efforts to improve the science of QMRA. Key identified research priorities included using molecular tools in QMRA, advancing dose-response methodology, addressing needed exposure assessments, harmonizing environmental monitoring for QMRA, unifying a divide between disease transmission and QMRA models, calibrating and/or validating QMRA models, modeling co-exposures and mixtures, and standardizing practices for incorporating variability and uncertainty throughout the source-to-outcome continuum. Cross-cutting needs identified were to: develop a community of research and practice, integrate QMRA with other scientific approaches, increase QMRA translation and impacts, build communication strategies, and encourage sustainable funding mechanisms. Ultimately, a vision for advancing the science of QMRA is outlined for informing national to global health assessments, controls, and policies.

Greywater recycling for diverse collection scales and appliances: Enteric pathogen log-removal targets and treatment trains.

In light of increasingly diverse greywater reuse applications, this study proposes risk-based log-removal targets (LRTs) to aid the selection of treatment trains for greywater recycling at different collection scales, including appliance-scale reuse of individual greywater streams. An epidemiology-based model was used to simulate the concentrations of prevalent and treatment-resistant reference pathogens (protozoa: Giardia and Cryptosporidium spp., bacteria: Salmonella and Campylobacter spp., viruses: rotavirus, norovirus, adenovirus, and Coxsackievirus B5) in the greywater streams for collection scales of 5-, 100-, and a 1000-people. Using quantitative microbial risk assessment (QMRA), we calculated LRTs to meet a health benchmark of 10-4 infections per person per year over 10'000 Monte Carlo iterations. LRTs were highest for norovirus at the 5-people scale and for adenovirus at the 100- and 1000-people scales. Example treatment trains were designed to meet the 95 % quantiles of LRTs. Treatment trains consisted of an aerated membrane bioreactor, chlorination, and, if required, UV disinfection. In most cases, rotavirus, norovirus, adenovirus and Cryptosporidium spp. determined the overall treatment train requirements. Norovirus was most often critical to dimension the chlorination (concentration × time values) and adenovirus determined the required UV dose. Smaller collection scales did not generally allow for simpler treatment trains due to the high LRTs associated with viruses, with the exception of recirculating washing machines and handwashing stations. Similarly, treating greywater sources individually resulted in lower LRTs, but the lower required LRTs nevertheless did not generally allow for simpler treatment trains. For instance, LRTs for a recirculating washing machine were around 3-log units lower compared to LRTs for indoor reuse of combined greywater (1000-people scale), but both scenarios necessitated treatment with a membrane bioreactor, chlorination and UV disinfection. However, simpler treatment trains may be feasible for small-scale and application-scale reuse if: (i) less conservative health benchmarks are used for household-based systems, considering the reduced relative importance of treated greywater in pathogen transmission in households, and (ii) higher log-removal values (LRVs) can be validated for unit processes, enabling simpler treatment trains for a larger number of appliance-scale reuse systems.

Monitoring ESBL-Escherichia coli in Swiss wastewater between November 2021 and November 2022: insights into population carriage.

Antimicrobial resistance (AMR) poses a global health threat, causing millions of deaths annually, with expectations of increased impact in the future. Wastewater surveillance offers a cost-effective, non-invasive tool to understand AMR carriage trends within a population. We monitored extended-spectrum ß-lactamase producing Escherichia coli (ESBL-E. coli) weekly in influent wastewater from six wastewater treatment plants (WWTPs) in Switzerland (November 2021 to November 2022) to investigate spatio-temporal variations, explore correlations with environmental variables, develop a predictive model for ESBL-E. coli carriage in the community, and detect the most prevalent ESBL-genes. We cultured total and ESBL-E. coli in 300 wastewater samples to quantify daily loads and percentage of ESBL-E. coli. Additionally, we screened 234 ESBL-E. coli isolates using molecular methods for the presence of 18 ESBL-gene families. We found a population-weighted mean percentage of ESBL-E. coli of 1.9% (95% confidence interval: 1.8-2%) across all sites and weeks, which can inform ESBL-E. coli carriage. Concentrations of ESBL-E. coli varied across WWTPs and time, with higher values observed in WWTPs serving larger populations. Recent precipitations (previous 24/96 h) showed no significant association with ESBL-E. coli, while temperature occasionally had a moderate impact (P < 0.05, correlation coefficients approximately 0.40) in some locations. We identified blaCTX-M-1, blaCTX-M-9, and blaTEM as the predominant ESBL-gene families. Our study demonstrates that wastewater-based surveillance of culturable ESBL-E. coli provides insights into AMR trends in Switzerland and may also inform resistance. These findings establish a foundation for long term, nationally established monitoring protocols and provide information that may help inform targeted public health interventions. IMPORTANCE: Antimicrobial resistance (AMR) is a global health threat and is commonly monitored in clinical settings, given its association with the risk of antimicrobial-resistant infections. Nevertheless, tracking AMR within a community proves challenging due to the substantial sample size required for a representative population, along with high associated costs and privacy concerns. By investigating high resolution temporal and geographic trends in extended-spectrum beta-lactamase producing Escherichia coli in wastewater, we provide an alternative approach to monitor AMR dynamics, distinct from the conventional clinical settings focus. Through this approach, we develop a mechanistic model, shedding light on the relationship between wastewater indicators and AMR carriage in the population. This perspective contributes valuable insights into trends of AMR carriage, emphasizing the importance of wastewater surveillance in informing effective public health interventions.

Applications of Quantitative Microbial Risk Assessment to Respiratory Pathogens and Implications for Uptake in Policy: A State-of-the-Science Review.

BACKGROUND: Respiratory tract infections are major contributors to the global disease burden. Quantitative microbial risk assessment (QMRA) holds potential as a rapidly deployable framework to understand respiratory pathogen transmission and inform policy on infection control. OBJECTIVES: The goal of this paper was to evaluate, motivate, and inform further development of the use of QMRA as a rapid tool to understand the transmission of respiratory pathogens and improve the evidence base for infection control policies. METHODS: We conducted a literature review to identify peer-reviewed studies of complete QMRA frameworks on aerosol inhalation or contact transmission of respiratory pathogens. From each of the identified studies, we extracted and summarized information on the applied exposure model approaches, dose-response models, and parameter values, including risk characterization. Finally, we reviewed linkages between model outcomes and policy. RESULTS: We identified 93 studies conducted in 16 different countries with complete QMRA frameworks for diverse respiratory pathogens, including SARS-CoV-2, Legionella spp., Staphylococcus aureus, influenza, and Bacillus anthracis. Six distinct exposure models were identified across diverse and complex transmission pathways. In 57 studies, exposure model frameworks were informed by their ability to model the efficacy of potential interventions. Among interventions, masking, ventilation, social distancing, and other environmental source controls were commonly assessed. Pathogen concentration, aerosol concentration, and partitioning coefficient were influential exposure parameters as identified by sensitivity analysis. Most (84%, n=78) studies presented policy-relevant content including a) determining disease burden to call for policy intervention, b) determining risk-based threshold values for regulations, c) informing intervention and control strategies, and d) making recommendations and suggestions for QMRA application in policy. CONCLUSIONS: We identified needs to further the development of QMRA frameworks for respiratory pathogens that prioritize appropriate aerosol exposure modeling approaches, consider trade-offs between model validity and complexity, and incorporate research that strengthens confidence in QMRA results. https://doi.org/10.1289/EHP12695.

Monitoring an Emergent Pathogen at Low Incidence in Wastewater Using qPCR: Mpox in Switzerland.

Wastewater-based epidemiology offers a complementary approach to clinical case-based surveillance of emergent diseases and can help identify regions with infected people to prioritize clinical surveillance strategies. However, tracking emergent diseases in wastewater requires reliance on novel testing assays with uncertain sensitivity and specificity. Limited pathogen shedding may cause detection to be below the limit of quantification or bordering the limit of detection. Here, we investigated how the definition of limit of detection for quantitative polymerase chain reaction (qPCR) impacts epidemiological insights during an mpox outbreak in Switzerland. 365 wastewater samples from three wastewater treatment plants in Switzerland from 9 March through 31 October 2022 were analyzed for mpox DNA using qPCR. We detected mpox DNA in 22% (79 of 365) wastewater samples based on a liberal definition of qPCR detection as any exponentially increasing fluorescence above the threshold. Based on a more restrictive definition as the lowest concentration at which there is 95% likelihood of detection, detection was 1% (5 of 365). The liberal definition shows high specificity (90%) and accuracy (78%), but moderate sensitivity (64%) when benchmarked against available clinical case reporting, which contrasts with higher specificity (98%) but lower sensitivity (10%) and accuracy (56%) of the 95% likelihood definition. Wastewater-based epidemiology applied to an emergent pathogen will require optimizing public health trade-offs between reporting data with high degrees of uncertainty and delaying communication and associated action. Information sharing with relevant public health stakeholders could couple early results with clear descriptions of uncertainty.Impact Statement: When a novel pathogen threatens to enter a community, wastewater-based epidemiology offers an opportunity to track its emergence and spread. However, rapid deployment of methods for to detect a novel pathogen may rely on assays with uncertain sensitivity and specificity. Benchmarking the detection of mpox DNA in Swiss wastewaters with reported clinical cases in 2022, we demonstrate how definitions of detection of a qPCR assay influence epidemiological insights from wastewater. The results highlight the need for information sharing between public health stakeholders that couple early insights from wastewater with descriptions of methodological uncertainty to optimize public health actions.

Mapping safe drinking water use in low- and middle-income countries.

Safe drinking water access is a human right, but data on safely managed drinking water services (SMDWS) is lacking for more than half of the global population. We estimate SMDWS use in 135 low- and middle-income countries (LMICs) at subnational levels with a geospatial modeling approach, combining existing household survey data with available global geospatial datasets. We estimate that only one in three people used SMDWS in LMICs in 2020 and identified fecal contamination as the primary limiting factor affecting almost half of the population of LMICs. Our results are relevant for raising awareness about the challenges and limitations of current global monitoring approaches and demonstrating how globally available geospatial data can be leveraged to fill data gaps and identify priority areas in LMICs.

Influenza transmission dynamics quantified from RNA in wastewater in Switzerland.

INTRODUCTION: Influenza infections are challenging to monitor at the population level due to many mild and asymptomatic cases and similar symptoms to other common circulating respiratory diseases, including COVID-19. Methods for tracking cases outside of typical reporting infrastructure could improve monitoring of influenza transmission dynamics. Influenza shedding into wastewater represents a promising source of information where quantification is unbiased by testing or treatment-seeking behaviours. METHODS: We quantified influenza A and B virus loads from influent at Switzerland's three largest wastewater treatment plants, serving about 14% of the Swiss population (1.2 million individuals). We estimated trends in infection incidence and the effective reproductive number (Re) in these catchments during a 2021/22 epidemic and compared our estimates to typical influenza surveillance data. RESULTS: Wastewater data captured the same overall trends in infection incidence as laboratory-confirmed case data at the catchment level. However, the wastewater data were more sensitive in capturing a transient peak in incidence in December 2021 than the case data. The Re estimated from the wastewater data was roughly at or below the epidemic threshold of 1 during work-from-home measures in December 2021 but increased to at or above the epidemic threshold in two of the three catchments after the relaxation of these measures. The third catchment yielded qualitatively the same results but with wider confidence intervals. The confirmed case data at the catchment level yielded comparatively less precise R_e estimates before and during the work-from-home period, with confidence intervals that included one before and during the work-from-home period. DISCUSSION: Overall, we show that influenza RNA in wastewater can help monitor nationwide influenza transmission dynamics. Based on this research, we developed an online dashboard for ongoing wastewater-based influenza surveillance in Switzerland.

Association between the number of symptomatic mpox cases and the detection of mpox virus DNA in wastewater in Switzerland: an observational surveillance study.

AIM OF THE STUDY: The COVID-19 pandemic has drawn attention to the benefit of wastewater-based epidemiology, particularly when case numbers are underreported. Underreporting may be an issue with mpox, where biological reasons and stigma may prevent patients from getting tested. Therefore, we aimed to assess the validity of wastewater surveillance for monitoring mpox virus DNA in wastewater of a Central European city and its association with official case numbers. METHODS: Wastewater samples were collected between 1 July and 28 August 2022 in the catchment area of Basel, Switzerland, and the number of mpox virus genome copies they contained was determined by real-time quantitative PCR. Logistic regression analyses were used to determine the odds of detectability of mpox virus DNA in wastewater, categorised as detectable or undetectable. Mann-Whitney U tests were used to determine associations between samples that tested positive for the mpox virus and officially reported cases and patients' recorded symptomatic phases. RESULTS: Mpox virus DNA was detected in 15 of 39 wastewater samples. The number of positive wastewater samples was associated with the number of symptomatic cases (odds ratio [OR] = 2.18, 95% confidence interval (CI) = 1.38-3.43, p = 0.001). The number of symptomatic cases differed significantly between days with positive versus negative wastewater results (median = 11 and 8, respectively, p = 0.0024). CONCLUSION: Mpox virus DNA was detectable in wastewater, even when officially reported case numbers were low (0-3 newly reported mpox cases corresponding to 6-12 symptomatic patients). Detectability in wastewater was significantly associated with the number of symptomatic patients within the catchment area. These findings illustrate the value of wastewater-based surveillance systems when assessing the prevalence of emerging and circulating infectious diseases.