Research needs for optimising wastewater-based epidemiology monitoring for public health protection

The cutting edge of surveillance: Exploring high-resolution mass spectrometry in wastewater-based epidemiology for monitoring forensic samples

Criminal activity has always been detected through forensic evidence. However, the potential for using such evidence to stop crimes in their tracks or slow them down has not yet been completely realized. There is a lot of potential for assessing trace quantities of chemicals in wastewater systems to provide effective forensic information. Wastewater-based epidemiology (WBE) has emerged in the last decades as a crucial epidemiological information source for collecting data on community-wide health. It can add important knowledge about illicit drug consumption and/or disposal, exposure to pathogens, infectious diseases, industrial pollutants, and antibiotic resistance. The use of high-resolution mass spectrometry (HRMS) in WBE has revolutionized the field by enabling the detection and quantification of these compounds. This review article explores the cutting edge of surveillance in WBE through applying HRMS techniques for forensic sample monitoring. It delves into the most recent WBE applications, examining their advantages and disadvantages. It also explores the potential for obtaining a more comprehensive evaluation of forensic samples. Furthermore, the application of these approaches to generate "forensic intelligence" for surveillance and criminal interruption is discussed, with examples of how this data can be integrated into future work.

Defining alarm thresholds for the load of pathogenic viruses in wastewater for decision making: An application to three French cities

Wastewater monitoring has the potential to complement infectious disease surveillance systems. However, the absence of predefined viral signal thresholds in wastewater is often presented as a limiting factor in triggering public health action. To overcome this issue, the feasibility of defining alarm threshold for viral loads in wastewater samples was assessed by quantifying genome fragments of SARS-CoV-2, influenza A virus (IAV), respiratory syncytial virus (RSV), norovirus (NoV), and rotavirus (RoV) by RT-digital PCR (dPCR) in untreated wastewater samples from three treatment plants. Cut-point values were calculated for periods with a high rate of visits to emergency rooms or at-home visits by SOS Médecins for the related diseases. ROC curves were constructed, and the values of alarm threshold in wastewater were defined using the Youden index. For each targeted virus, alarm thresholds were close to each other across the three WWTPs. As indicated by likelihood ratios, evidence to rule in the diagnosis of high rate of visits when the alarm threshold was exceeded ranged from weak to strong and was highest for RSV and SARS-CoV-2. Evidence to rule out the diagnosis when the alarm threshold was not exceeded was strong or moderate for IAV, SARS-CoV-2 and RSV. Diagnostic performance of the test was not as high for NoV and RoV. Positive predictive value was highest for SARS-CoV-2 and RSV. For SARS-CoV-2 and RSV, the definition of an alarm threshold in wastewater could substantially inform the diagnosis of a period with a high rate of medical visits for COVID-19 and bronchiolitis, respectively.

Monitoring Antibiotic Resistance in Wastewater: Findings from Three Treatment Plants in Sicily, Italy

Antimicrobial resistance (AMR) poses a global public health threat. Wastewater analysis provides valuable insights into antimicrobial resistance genes (ARGs), identifying sources and trends and evaluating AMR control measures. Between February 2022 and March 2023, pre-treatment urban wastewater samples were collected weekly from treatment plants in Pantano D'Arci, Siracusa, and Giarre (Sicily, Italy). Monthly composite DNA extracts were prepared by combining weekly subsamples from each site, yielding 42 composite samples-14 from each treatment plant. Real-time PCR analysis targeted specific ARGs, including blaSHV, erm(A), erm(B), blaOXA, blaNDM, blaVIM, blaTEM, and blaCTX-M. The preliminary findings revealed that blaERM-B, blaOXA, blaTEM, and blaCTX-M were present in all samples, with erm(B) (median value: 8.51; range: 1.67-30.93), blaSHV (0.78; 0.00-6.36), and blaTEM (0.72; 0.34-4.30) showing the highest relative abundance. These results underscore the importance of integrating ARG data with broader research to understand the persistence and proliferation mechanisms of ARGs in wastewater environments. Future studies should employ metagenomic analyses to profile resistomes in urban, hospital, agricultural, and farm wastewater. Comparing these profiles will help identify contamination pathways and inform the development of targeted ARG surveillance programs. Monitoring shifts in ARG abundance could signal cross-sectoral contamination, enabling more effective AMR control strategies.

The Crucial Role of Laboratory Medicine in Addressing Future Public Health Infectious Threats: Insights Gained from the COVID-19 Pandemic

Laboratory testing has played a pivotal role throughout the coronavirus disease 2019 (COVID-19) pandemic, exemplifying the importance of in vitro diagnostics in addressing public health threats posed by outbreaks of infectious diseases. This article aims to present key insights from our expertise, derived from evidence gathered during the COVID-19 pandemic, to inform strategies for managing future infectious challenges. Current scientific evidence underscores that patient sample testing not only allows to diagnose an acute severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, but also supports outbreak prediction, improved control measures, anticipation of pressure on the healthcare system, mitigation of adverse clinical outcomes, and early detection of emerging SARS-CoV-2 variants. Additionally, wastewater monitoring has emerged as a powerful tool for forecasting disease burden, including both prevalence and severity. Collectively, these findings underscore the value of diagnostic testing and wastewater surveillance in guiding healthcare planning and optimizing resource allocation during the COVID-19 pandemic, offering a valid framework to be applied to future public health threats, especially to any potential outbreak of "Disease X" that may emerge in the future.

Wastewater-Based Epidemiological Surveillance in France: The SUM'EAU Network

Wastewater surveillance is a powerful public health tool which gained global prominence during the COVID-19 pandemic. This article describes the development and implementation of the national wastewater surveillance network in France: SUM'EAU. Preliminary work included defining a sampling strategy, evaluating/optimising analytical methods, launching a call for tenders to select network laboratories and producing wastewater monitoring indicators. SUM'EAU was then deployed in three stages: (i) a pilot study, (ii) the transfer of analytical activities from the National Reference Laboratory to four selected network laboratories, and (iii) the extension of the system to additional sampling sites. Currently, SUM'EAU monitors SARS-CoV-2 across 54 wastewater treatment plants in mainland France. Once a week on business days, 24 h flow-proportional composite samples are collected at plant inlets and transported at 5 °C (±3 °C) to partner laboratories for analysis. The analytical process involves sample concentration, RNA extraction, and digital RT-PCR/q-RT-PCR to detect and quantify the presence of the SARS-CoV-2 genome in wastewater. Subsequently, data are transferred to Santé publique France, the French National Public Health Agency, for analysis and interpretation. While SUM'EAU has been instrumental in monitoring the COVID-19 pandemic and holds significant potential for broader application, securing sustainable funding for its operation remains a major challenge.

Phenotypic Antibiotic Resistance Patterns of Escherichia coli Isolates from Clinical UTI Samples and Municipal Wastewater in a Grenadian Community

Antimicrobial resistance (AMR) is a growing global health threat. This study investigated antibiotic resistance in E. coli isolates from municipal wastewater (86 isolates) and clinical urinary tract infection (UTI) cases (34 isolates) in a Grenadian community, using data from January 2022 to October 2023. Antibiogram data, assessed per WHO guidelines for Critically Important antimicrobials (CIA), showed the highest resistance levels in both clinical and wastewater samples for ampicillin, followed by amoxicillin/clavulanic acid and nalidixic acid, all classified as Critically Important. Similar resistance was observed for sulfamethoxazole-trimethoprim (highly important) in both groups, with nitrofurantoin showing resistance in the important category. According to the WHO AWaRe classification, ampicillin (ACCESS group) had the highest resistance, while nitrofurantoin had the lowest across all samples. The WATCH group antibiotics, cefuroxime and cefoxitin, showed comparable resistance levels, whereas aztreonam from the RESERVE group (tested only in wastewater) was 100% sensitive. Multiple Antibiotic Resistance (MAR) index analysis revealed that 7% of wastewater and 38.2% of clinical samples had MAR values over 0.2, indicating prior antibiotic exposure in clinical isolates. These parallel patterns in wastewater and clinical samples highlight wastewater monitoring as a valuable tool for AMR surveillance, supporting antibiotic stewardship through ongoing environmental and clinical assessment.

Exploring hepatitis A dynamics in Israel, 2019-2022

BACKGROUND

Continuous monitoring of Hepatitis A Virus (HAV) may assist in identifying local outbreaks. The advent of the COVID-19 pandemic, which affected the circulation of numerous pathogens, may have also impacted the scope of HAV infections.

AIM

To investigate the incidence and environmental dissemination of HAV between 2019 and 2022 in Israel, a country with an anti-HAV vaccination program.

METHODS

HAV RT-PCR analysis was performed for all HAV cases and for 280 sewage samples collected in 2019-2022. Available amplified HAV fragments from clinical (n = 107) and sewage (n = 27) were also assessed by genotyping and phylogenetic analysis.

RESULTS

In 2019-2022, 158 individuals and 12.9 % (36/280) of sewage samples were HAV-RNA positive. Median age was 30 years (IQR 20.5-44); approximately half (51.9 %, 82/158) were males. Almost all patients (98.4 %, 124/126) were not vaccinated. Highest numbers were identified in 2019 (84 cases and 30 %, 21/71, positive sewage samples). In 2020, when three COVID-19 related lockdowns were implemented, 24 cases and 4.3 % (3/69) sewage samples were HAV-RNA positive. The number of HAV-RNA positive cases and positive sewage samples remained low in 2021-2022 (31 and 19 cases, 13.2 %, 9/68 and 4.2 %, 3/72 positive sewage samples, respectively). Sub-genotype IB dominated (90.7 %, 97/107 of cases and 81.5 %, 22/27 of sewage samples), and phylogenetic analysis of HAV samples demonstrated small transmission clusters of sequences from Jews, Bedouin Arabs and foreign workers. Sub-genotype IA was identified in 8.4 % (9/107) of cases and in 18.5 % (5/27) of sewage samples.

CONCLUSION

Combined clinical and environmental surveillance is optimal for monitoring HAV. In 2020, the circulation of HAV decreased, possibly following COVD-19 health restrictions. In subsequent years, the incidence remained low. Adults in risk-groups for HAV infection should be vaccinated to minimize HAV circulation.

Monitoring Influenza A (H1N1, H3N2), RSV, and SARS-CoV-2 Using Wastewater-Based Epidemiology: A 2-Year Longitudinal Study in an Indian Megacity Covering Omicron and Post-Omicron Phases

The bourgeoning field of wastewater-based epidemiology (WBE) for the surveillance of several respiratory viruses which includes Influenza A, H1N1pdm09, H3N2, respiratory syncytial viruses (RSV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is of interest for public health concerns. However, there are few long-term monitoring studies globally. In this study, respiratory viruses were detected and quantified from 11 sewer sheds by utilizing reverse transcription-quantitative polymerase chain reaction analysis in Pune city, India, from Jan 2022 to Dec 2023. The RNA fragments of respiratory viruses were detected in sewage samples before clinical cases were reported, underscoring the potential of WBE for early detection and monitoring within the population. The Spearman correlation of wastewater viral copies was positively and significantly correlated with the clinically positive case of H1N1pdm09 (ρ = 0.55, p = 1.4 × 10-9), H3N2 (ρ = 0.25, p = 9.9 × 10-3), and SARS-CoV-2 (ρ = 0.43, p = 4.1 × 10-6). The impact of public health interventions on the circulation of infectious respiratory diseases showed a significant difference in the viral load during the period when many preventing measures were carried out against the COVID-19 pandemic (restriction phase), compared to the period when no such preventive measures are followed (no-restriction phase) for Influenza A, H1N1pdm09, H3N2, and RSV with p-value < 0.05, which indicates the influence of health policy implementation in controlling disease spread. The present study provides an effective approach to detecting multiple respiratory viruses from wastewater and provides insights into the epidemiology of respiratory illnesses. The WBE aids in providing information on the spread of pathogens (viruses) in the community, offering a proactive strategy for public health management, allowing for timely interventions and implementing targeted measures to mitigate the spread of these viruses under one health approach.

Monitoring and preventing foodborne outbreaks: are we missing wastewater as a key data source?

In 2022, the number of foodborne outbreaks in Europe increased by 43.9%, highlighting the need to improve surveillance systems and design outbreak predictive tools. This review aims to assess the scientific literature describing wastewater surveillance to monitor foodborne pathogens in association with clinical data. In the selected studies, the relationship between peaks of pathogen concentration in wastewater and reported clinical cases is described. Moreover, details on analytical methods to detect and quantify pathogens as well as wastewater sampling procedures are discussed. Few papers show a statistically significant correlation between high concentrations of foodborne pathogens in wastewater and the occurrence of clinical cases. However, monitoring pathogen concentration in wastewater looks like a promising and cost-effective strategy to improve foodborne outbreak surveillance. Such a strategy can be articulated in three steps, where the first one is testing wastewater with an untargeted method, like shotgun metagenomic, to detect microorganisms belonging to different domains. The second consists of testing wastewater with a targeted method, such as quantitative polymerase chain reaction, to quantify those specific pathogens that in the metagenomic dataset display an increasing trend or exceed baseline concentration thresholds. The third involves the integrated wastewater and clinical data analysis and modeling to find meaningful epidemiological correlations and make predictions.

National-scale antimicrobial resistance surveillance in wastewater: A comparative analysis of HT qPCR and metagenomic approaches

Wastewater serves as an important reservoir of antimicrobial resistance (AMR), and its surveillance can provide insights into population-level trends in AMR to inform public health policy. This study compared two common high-throughput screening approaches, namely (i) high-throughput quantitative PCR (HT qPCR), targeting 73 antimicrobial resistance genes, and (ii) metagenomic sequencing. Weekly composite samples of wastewater influent were taken from 47 wastewater treatment plants (WWTPs) across Wales, as part of a national AMR surveillance programme, alongside 4 weeks of daily wastewater effluent samples from a large municipal hospital. Metagenomic analysis provided more comprehensive resistome coverage, detecting 545 genes compared to the targeted 73 genes by HT qPCR. It further provided contextual information critical to risk assessment (i.e. potential bacterial hosts). In contrast, HT qPCR exhibited higher sensitivity, quantifying all targeted genes including those of clinical relevance present at low abundance. When limited to the HT qPCR target genes, both methods were able to reflect the spatiotemporal dynamics of the complete metagenomic resistome, distinguishing that of the hospital and the WWTPs. Both approaches revealed correlations between resistome compositional shifts and environmental variables like ammonium wastewater concentration, though differed in their interpretation of some potential influencing factors. Overall, metagenomics provides more comprehensive resistome profiling, while qPCR permits sensitive quantification of genes significant to clinical resistance. We highlight the importance of selecting appropriate methodologies aligned to surveillance aims to guide the development of effective wastewater-based AMR monitoring programmes.

Pathogen prioritisation for wastewater surveillance ahead of the Paris 2024 Olympic and Paralympic Games, France

BackgroundWastewater surveillance is an effective approach to monitor population health, as exemplified by its role throughout the COVID-19 pandemic.AimThis study explores the possibility of extending wastewater surveillance to the Paris 2024 Olympic and Paralympic Games, focusing on identifying priority pathogen targets that are relevant and feasible to monitor in wastewater for these events.MethodsA list of 60 pathogens of interest for general public health surveillance for the Games was compiled. Each pathogen was evaluated against three inclusion criteria: (A) analytical feasibility; (B) relevance, i.e. with regards to the specificities of the event and the characteristics of the pathogen; and (C) added value to inform public health decision-making. Analytical feasibility was assessed through evidence from peer-reviewed publications demonstrating the detectability of pathogens in sewage, refining the initial list to 25 pathogens. Criteria B and C were evaluated via expert opinion using the Delphi method. The panel consisting of some 30 experts proposed five additional pathogens meeting criterion A, totalling 30 pathogens assessed throughout the three-round iterative questionnaire. Pathogens failing to reach 70% group consensus threshold underwent further deliberation by a subgroup of experts.ResultsSix priority targets suitable for wastewater surveillance during the Games were successfully identified: poliovirus, influenza A virus, influenza B virus, mpox virus, SARS-CoV-2 and measles virus.ConclusionThis study introduced a model framework for identifying context-specific wastewater surveillance targets for a mass gathering. Successful implementation of a wastewater surveillance plan for Paris 2024 could incentivise similar monitoring efforts for other mass gatherings globally.

Wastewater-based surveillance is an efficient monitoring tool for tracking influenza A in the community

Around the world, influenza A virus has caused severe pandemics, and the risk of future pandemics remains high. Currently, influenza A virus surveillance is based on the clinical diagnosis and reporting of disease cases. In this study, we apply wastewater-based surveillance to monitor the amount of the influenza A virus RNA at the population level. We report the influenza A virus RNA levels in 10 wastewater treatment plant catchment areas covering 40 % of the Finnish population. Altogether, 251 monthly composite influent wastewater samples (collected between February 2021 and February 2023) were analysed from supernatant fraction using influenza A virus specific RT-qPCR method. During the study period, an influenza A virus epidemic occurred in three waves in Finland. This study shows that the influenza A virus RNA can be detected from the supernatant fraction of 24 h composite influent wastewater samples. The influenza A virus RNA gene copy number in wastewater correlated with the number of confirmed disease cases in the Finnish National Infectious Diseases Register. The median Kendall's τ correlation strength was 0.636 (min= 0.486 and max=0.804) and it was statistically significant in all 10 WTTPs. Wastewater-based surveillance of the influenza A virus RNA is an independent from individual testing method and cost-efficiently reflects the circulation of the virus in the entire population. Thus, wastewater monitoring complements the available, but often too sparse, information from individual testing and improves health care and public health preparedness for influenza A virus pandemics.

Nursing home wastewater surveillance for early warning of SARS-CoV-2-positive occupants-Insights from a pilot project at 8 facilities

BACKGROUND

Wastewater surveillance for SARS-CoV-2 has been used widely in the United States for indication of community incidence during the COVID-19 pandemic, but less is known about the feasibility of its use on a building level in nursing homes to provide early warning and prevent transmission.

METHODS

A pilot study was conducted at 8 Department of Veterans Affairs nursing homes across the United States to examine operational feasibility. Wastewater from the participating facilities was sampled daily during the week for 6 months (January 11, 2021-July 2, 2021) and analyzed for SARS-CoV-2 genetic material. Wastewater results were compared to new SARS-CoV-2 infections in nursing home residents and employees to determine if wastewater surveillance could provide early warning of a COVID-19-positive occupant.

RESULTS

All 8 nursing homes had wastewater samples positive for SARS-CoV-2 and COVID-19-positive occupants. The sensitivity of wastewater surveillance for early warning of COVID-19-positive residents was 60% (3/5) and for COVID-19-positive employees was 46% (13/28).

CONCLUSIONS

Wastewater surveillance may provide additional information for reinforcing infection control practices and lead to preventing transmission in a setting with high-risk residents. The low sensitivity for early warning in this real-world pilot highlights limitations and insights for applicability in buildings.

Combating antibiotic resistance using wastewater surveillance: Significance, applications, challenges, and future directions

The global increase of antibiotic resistance (AR) and resistant infections call for effective surveillance methods for understanding and mitigating (re)-emerging public health risks. Wastewater surveillance (WS) of antibiotic resistance is an emerging, but currently under-utilized decision-support tool in public health systems. Recent years have witnessed an increase in evidence linking antibiotic resistance in wastewaters to that of the community. To date, very few comprehensive reviews exist on the application of WS to understand AR and resistant infections in population. Current and emerging AR detection methods, and their merits and limitations are discussed. Wastewater surveillance has several merits relative to individual testing, including; (1) low per capita testing cost, (2) high spatial coverage, (3) low requirement for diagnostic equipment, and (4) detection of health threats ahead of real outbreaks. The applications of WS as an early warning system and decision support tool to understand and mitigate AR are discussed. Wastewater surveillance could be a tool of choice in low-income settings lacking resources and diagnostic facilities for individual testing. To demonstrate the utility of WS, empirical evidence from field case studies is presented. However, constraints still exist, including; (1) lack of standardized protocols, (2) the clinical utility and sensitivity of WS-based data, (3) uncertainties in relating WS data to pathogenic and virulent bacteria, and (4) whether or not AR in stools and ultimately wastewater represent the complete human resistome. Finally, further prospects are presented, include knowledge gaps on; (1) development of low-cost biosensors for AR, (2) development of WS protocols (sampling, processing, interpretation), (3) further pilot scale studies to understand the opportunities and limits of WS, and (4) development of computer-based analytical tools to facilitate rapid data collection, visualization and interpretation. Therefore, the present paper discusses the principles, opportunities, and constraints of wastewater surveillance applications to understand AR and safeguard public health.

The role of wastewater-based epidemiology for SARS-CoV-2 in developing countries: Cumulative evidence from South Africa supports sentinel site surveillance to guide public health decision-making

The uptake of wastewater-based epidemiology (WBE) for SARS-CoV-2 as a complementary tool for monitoring population-level epidemiological features of the COVID-19 pandemic in low-and-middle-income countries (LMICs) is low. We report on the findings from the South African SARS-CoV-2 WBE surveillance network and make recommendations regarding the implementation of WBE in LMICs. Eight laboratories quantified influent wastewater collected from 87 wastewater treatment plants in all nine South African provinces from 01 June 2021 to 31 May 2022 inclusive, during the 3rd and 4th waves of COVID-19. Correlation and regression analyses between wastewater levels of SARS-CoV-2 and district laboratory-confirmed caseloads were conducted. The sensitivity and specificity of novel 'rules' based on WBE data to predict an epidemic wave were determined. Amongst 2158 wastewater samples, 543/648 (85 %) samples taken during a wave tested positive for SARS-CoV-2 compared with 842 positive tests from 1512 (55 %) samples taken during the interwave period. Overall, the regression-co-efficient was 0,66 (95 % confidence interval = 0,6-0,72, R2 = 0.59), ranging from 0.14 to 0.87 by testing laboratory. Early warning of the 4th wave of SARS-CoV-2 in Gauteng Province in November-December 2021 was demonstrated. A 50 % increase in log copies of SARS-CoV-2 compared with a rolling mean over the previous five weeks was the most sensitive predictive rule (58 %) to predict a new wave. Our findings support investment in WBE for SARS-CoV-2 surveillance in LMICs as an early warning tool. Standardising test methodology is necessary due to varying correlation strengths across laboratories and redundancy across testing plants. A sentinel site model can be used for surveillance networks without affecting WBE finding for decision-making. Further research is needed to identify optimal test frequency and the need for normalisation to population size to identify predictive and interpretive rules to support early warning and public health action.

Analyzing community wastewater in sub-sewersheds for the small-scale detection of SARS-CoV-2 variants in a German metropolitan area

Wastewater surveillance of SARS-CoV-2 proved useful, including for identifying the local appearance of newly identified virus variants. Previous studies focused on wastewater treatment plants (WWTP) with sewersheds of several hundred thousand people or at single building level, representing only a small number of people. Both approaches may prove inadequate for small-scale intra-urban inferences for early detection of emerging or novel virus variants. Our study aims (i) to analyze SARS-CoV-2 single nucleotide variants (SNVs) in wastewater of sub-sewersheds and WWTP using whole genome sequencing in order to (ii) investigate the potential of small-scale detection of novel known SARS-CoV-2 variants of concern (VOC) within a metropolitan wastewater system. We selected three sub-sewershed sampling sites, based on estimated population- and built environment-related indicators, and the inlet of the receiving WWTP in the Ruhr region, Germany. Untreated wastewater was sampled weekly between October and December 2021, with a total of 22 samples collected. SARS-CoV-2 RNA was analyzed by RT-qPCR and whole genome sequencing. For all samples, genome sequences were obtained, while only 13 samples were positive for RT-qPCR. We identified multiple specific SARS-CoV-2 SNVs in the wastewater samples of the sub-sewersheds and the WWTP. Identified SNVs reflected the dominance of VOC Delta at the time of sampling. Interestingly, we could identify an Omicron-specific SNV in one sub-sewershed. A concurrent wastewater study sampling the same WWTP detected the VOC Omicron one week later. Our observations suggest that the small-scale approach may prove particularly useful for the detection and description of spatially confined emerging or existing virus variants circulating in populations. Future studies applying small-scale sampling strategies taking into account the specific features of the wastewater system will be useful to analyze temporal and spatial variance in more detail.

Detection of SARS-CoV-2 in Environment: Current Surveillance and Effective Data Management of COVID-19

Since diagnostic laboratories handle large COVID-19 samples, researchers have established laboratory-based assays and developed biosensor prototypes. Both share the same purpose; to ascertain the occurrence of air and surface contaminations by the SARS-CoV-2 virus. However, the biosensors further utilize internet-of-things (IoT) technology to monitor COVID-19 virus contamination, specifically in the diagnostic laboratory setting. The IoT-capable biosensors have great potential to monitor for possible virus contamination. Numerous studies have been done on COVID-19 virus air and surface contamination in the hospital setting. Through reviews, there are abundant reports on the viral transmission of SARS-CoV-2 through droplet infections, person-to-person close contact and fecal-oral transmission. However, studies on environmental conditions need to be better reported. Therefore, this review covers the detection of SARS-CoV-2 in airborne and wastewater samples using biosensors with comprehensive studies in methods and techniques of sampling and sensing (2020 until 2023). Furthermore, the review exposes sensing cases in public health settings. Then, the integration of data management together with biosensors is well explained. Last, the review ended with challenges to having a practical COVID-19 biosensor applied for environmental surveillance samples.

Sampling Considerations for Wastewater Surveillance of Antibiotic Resistance in Fecal Bacteria

Wastewaters can be analyzed to generate population-level data for public health surveillance, such as antibiotic resistance monitoring. To provide representative data for the contributing population, bacterial isolates collected from wastewater should originate from different individuals and not be distorted by a selection pressure in the wastewater. Here we use Escherichia coli diversity as a proxy for representativeness when comparing grab and composite sampling at a major municipal wastewater treatment plant influent and an untreated hospital effluent in Gothenburg, Sweden. All municipal samples showed high E. coli diversity irrespective of the sampling method. In contrast, a marked increase in diversity was seen for composite compared to grab samples from the hospital effluent. Virtual resampling also showed the value of collecting fewer isolates on multiple occasions rather than many isolates from a single sample. Time-kill tests where individual E. coli strains were exposed to sterile-filtered hospital wastewater showed rapid killing of antibiotic-susceptible strains and significant selection of multi-resistant strains when incubated at 20 °C, an effect which could be avoided at 4 °C. In conclusion, depending on the wastewater collection site, both sampling method and collection/storage temperature could significantly impact the representativeness of the wastewater sample.