Making waves: Plausible lead time for wastewater based epidemiology as an early warning system for COVID-19

Wastewater-based epidemiology in countries with poor wastewater treatment - Epidemiological indicator function of SARS-CoV-2 RNA in surface waters

Wastewater-based epidemiology (WBE) surveillance of COVID-19 and other future outbreaks is a challenge for developing countries as most households are not connected to a sewerage system. In December 2019, SARS-CoV-2 RNA was detected in the Danube River at a site severely affected by wastewaters from Belgrade. Rivers are much more complex systems than wastewater systems, and efforts are needed to address all the factors influencing the adoption of WBE as an alternative to targeting raw wastewater. Our objective was to provide a more detailed insight into the potential of SARS-CoV-2 surveillance in Serbian surface waters for epidemiological purposes. Water samples were collected at 12 sites along the Sava and Danube rivers in Belgrade during the fourth COVID-19 wave in Serbia that started in late February 2021. RNA was concentrated using Amicon Ultra-15 centrifugal filters and quantified using RT-qPCR with primer sets targeting nucleocapsid (N1 and N2) and envelope (E) protein genes. Microbiological (faecal indicator bacteria and human and animal genetic faecal source tracking markers), epidemiological, physicochemical and hydromorphological parameters were analysed in parallel. From 44 samples, SARS-CoV-2 RNA was detected in 31, but only at 4 concentrations above the level of quantification (ranging from 8.47 × 10³ to 2.07 × 10⁴ gc/L). The results indicated that surveillance of SARS-CoV-2 RNA in surface waters as ultimate recipients could be used as an epidemiological early-warning tool in countries lacking wastewater treatment and proper sewerage infrastructure. The performance of the applied approach, including advanced sampling site characterization to trace and identify sites with significant raw sewage influence from human populations, could be further improved by adaptation of the methodology for processing higher volumes of samples and enrichment factors, which should provide the quantitative instead of qualitative data needed for WBE.

[The French Armed Forces Biomedical Research Institute (IRBA) and wastewater-based epidemiology: Applicability and relevance in armed forces]

The French Armed Forces Biomedical Research Institute (IRBA) deeply involved in research on SARS-COV-2, participated in the creation of the Obépine sentinel network in charge of detecting, qualifying and quantifying the virus genome in wastewater in France. During this pandemic, wastewater-based epidemiology has proven to be a first class public health tool for assessing viral dynamics in populations and environment. Obépine has also conducted research demonstrating the low infectivity of faeces and wastewater and allowed for early detection of epidemic waves linked to new variants. The IRBA has adapted this powerful tool to the monitoring of viral infections on board the aircraft carrier Charles-de-Gaulle in order to get an operational system for anticipation after the first local outbreak in 2020. The presence of this surveillance and anticipation tool has allowed a better management of SARS-CoV-2 contingent introductions on board during stopovers or crewmembers entries. The combination of a mandatory vaccination protocol and the surveillance of viral circulation in black waters has made it possible to identify and locate cases, and thus to continue the operational mission in the COVID-19 environment while limiting the spread and preserving the health of the crew. This innovative tool can easily be redirected to the search for any other pathogens in blackwater or even, in the long term, to ensure health surveillance of any military establishment, at sea or on land, in France or on overseas bases.

Coordination of SARS-CoV-2 wastewater and clinical testing of university students demonstrates the importance of sampling duration and collection time

Wastewater surveillance has been a useful tool complementing clinical testing during the COVID-19 pandemic. However, transitioning surveillance approaches to small populations, such as dormitories and assisted living facilities poses challenges including difficulties with sample collection and processing. Recently, the need for reliable and timely data has coincided with the need for precise local forecasting of the trajectory of COVID-19. This study compared wastewater and clinical data from the University of Delaware (Fall 2020 and Spring 2021 semesters), and evaluated wastewater collection practices for enhanced virus detection sensitivity. Fecal shedding of SARS-CoV-2 is known to occur in infected individuals. However, shedding concentrations and duration has been shown to vary. Therefore, three shedding periods (14, 21, and 30 days) were presumed and included for analysis of wastewater data. SARS-CoV-2 levels detected in wastewater correlated with clinical virus detection when a positive clinical test result was preceded by fecal shedding of 21 days (p< 0.05) and 30 days (p < 0.05), but not with new cases (p = 0.09) or 14 days of shedding (p = 0.17). Discretely collected wastewater samples were compared with 24-hour composite samples collected at the same site. The discrete samples (n = 99) were composited examining the influence of sampling duration and time of day on SARS-CoV-2 detection. SARS-CoV-2 detection varied among dormitory complexes and sampling durations of 3-hour, 12-hour, and 24-hour (controls). Collection times frequently showing high detection values were between the hours of 03:00 to 05:00 and 23:00 to 08:00. In each of these times of day 33% of samples (3/9) were significantly higher (p < 0.05) than the control sample. The remainder (6/9) of the collection times (3-hour and 12-hour) were not different (p > 0.05) from the control. This study provides additional framework for continued methodology development for microbiological wastewater surveillance as the COVID-19 pandemic progresses and in preparation for future epidemiological efforts.

Model-based assessment of COVID-19 epidemic dynamics by wastewater analysis

Continuous surveillance of COVID-19 diffusion remains crucial to control its diffusion and to anticipate infection waves. Detecting viral RNA load in wastewater samples has been suggested as an effective approach for epidemic monitoring and the development of an effective warning system. However, its quantitative link to the epidemic status and the stages of outbreak is still elusive. Modelling is thus crucial to address these challenges. In this study, we present a novel mechanistic model-based approach to reconstruct the complete epidemic dynamics from SARS-CoV-2 viral load in wastewater. Our approach integrates noisy wastewater data and daily case numbers into a dynamical epidemiological model. As demonstrated for various regions and sampling protocols, it quantifies the case numbers, provides epidemic indicators and accurately infers future epidemic trends. Following its quantitative analysis, we also provide recommendations for wastewater data standards and for their use as warning indicators against new infection waves. In situations of reduced testing capacity, our modelling approach can enhance the surveillance of wastewater for early epidemic prediction and robust and cost-effective real-time monitoring of local COVID-19 dynamics.

Monitoring SARS-CoV-2 in the Wastewater and Rivers of Tapachula, a Migratory Hub in Southern Mexico

The COVID-19 pandemic has been monitored by applying different strategies, including SARS-CoV-2 detection with clinical testing or through wastewater-based epidemiology (WBE). We used the latter approach to follow SARS-CoV-2 dispersion in Tapachula city, located in Mexico's tropical southern border region. Tapachula is a dynamic entry point for people seeking asylum in Mexico or traveling to the USA. Clinical testing facilities for SARS-CoV-2 monitoring are limited in the city. A total of eighty water samples were collected from urban and suburban rivers and sewage and a wastewater treatment plant over 4 months in Tapachula. We concentrated viral particles with a PEG-8000-based method, performed RNA extraction, and detected SARS-CoV-2 particles through RT-PCR. We considered the pepper mild mottle virus as a fecal water pollution biomarker and analytical control. SARS-CoV-2 viral loads (N1 and N2 markers) were quantified and correlated with official regional statistics of COVID-19 bed occupancy and confirmed cases (r > 91%). Our results concluded that WBE proved a valuable tool for tracing and tracking the COVID-19 pandemic in tropical countries with similar water temperatures (21-29 °C). Monitoring SARS-CoV-2 through urban and suburban river water sampling would be helpful in places lacking a wastewater treatment plant or water bodies with sewage discharges.

The role of time-varying viral shedding in modelling environmental surveillance for public health: revisiting the 2013 poliovirus outbreak in Israel

Environmental pathogen surveillance is a sensitive tool that can detect early-stage outbreaks, and it is being used to track poliovirus and other pathogens. However, interpretation of longitudinal environmental surveillance signals is difficult because the relationship between infection incidence and viral load in wastewater depends on time-varying shedding intensity. We developed a mathematical model of time-varying poliovirus shedding intensity consistent with expert opinion across a range of immunization states. Incorporating this shedding model into an infectious disease transmission model, we analysed quantitative, polymerase chain reaction data from seven sites during the 2013 Israeli poliovirus outbreak. Compared to a constant shedding model, our time-varying shedding model estimated a slower peak (four weeks later), with more of the population reached by a vaccination campaign before infection and a lower cumulative incidence. We also estimated the population shed virus for an average of 29 days (95% CI 28-31), longer than expert opinion had suggested for a population that was purported to have received three or more inactivated polio vaccine (IPV) doses. One explanation is that IPV may not substantially affect shedding duration. Using realistic models of time-varying shedding coupled with longitudinal environmental surveillance may improve our understanding of outbreak dynamics of poliovirus, SARS-CoV-2, or other pathogens.

Monitoring of SARS-CoV-2 in wastewater: what normalisation for improved understanding of epidemic trends?

SARS-CoV-2 RNA quantification in wastewater has emerged as a relevant additional means to monitor the COVID-19 pandemic. However, the concentration can be affected by black water dilution factors or movements of the sewer shed population, leading to misinterpretation of measurement results. The aim of this study was to evaluate the performance of different indicators to accurately interpret SARS-CoV-2 in wastewater. Weekly/bi-weekly measurements from three cities in France were analysed from February to September 2021. The concentrations of SARS-CoV-2 gene copies were normalised to the faecal-contributing population using simple sewage component indicators. To reduce the measurement error, a composite index was created to combine simultaneously the information carried by the simple indicators. The results showed that the regularity (mean absolute difference between observation and the smoothed curve) of the simple indicators substantially varied across sampling points. The composite index consistently showed better regularity compared to the other indicators and was associated to the lowest variation in correlation coefficient across sampling points. These findings suggest the recommendation for the use of a composite index in wastewater-based epidemiology to compensate for variability in measurement results.

Improving correlation of wastewater SARS-CoV-2 gene copy numbers with COVID-19 public health cases using readily available biomarkers

The COVID-19 pandemic has highlighted the potential role that wastewater-based epidemiology can play in assessing aggregate community health. However, efforts to translate SARS-CoV-2 gene copy numbers obtained from wastewater samples into meaningful community health indicators are nascent. In this study, SARS-CoV-2 nucleocapsid (N) genes (N1 and N2) were quantified weekly using reverse transcriptase droplet digital PCR from two municipal wastewater treatment plants for seven months. Four biomarkers (ammonium, biological oxygen demand (BOD), creatinine, and human mitochondrial gene NADH dehydrogenase subunit 5) were quantified and used to normalize SARS-CoV-2 gene copy numbers. These were correlated to daily new case data and one-, two-, and three-week cumulative case data. Over the course of the study, the strongest correlations were observed with a one-day case data lag. However, early measurements were strongly correlated with a five-day case data lag. This indicates that in the early stages of the pandemic, the wastewater samples may have indicated active COVID-19 cases before clinical indications. Mitochondrial and creatinine normalization methods showed the strongest correlations throughout the study, indicating that human-specific biomarkers were better at normalizing wastewater data than ammonium or BOD. Granger causality tests supported this observation and showed that gene copies in wastewater could be predictive of new cases in a sewershed.

Tracking the temporal variation of COVID-19 surges through wastewater-based epidemiology during the peak of the pandemic: A six-month long study in Charlotte, North Carolina

The global spread of SARS-CoV-2 has continued to be a serious concern after WHO declared the virus to be the causative agent of the coronavirus disease 2019 (COVID-19) a global pandemic. Monitoring of wastewater is a useful tool for assessing community prevalence given that fecal shedding of SARS-CoV-2 occurs in high concentrations by infected individuals, regardless of whether they are asymptomatic or symptomatic. Using tools that are part of wastewater-based epidemiology (WBE) approach, combined with molecular analyses, wastewater monitoring becomes a key piece of information used to assess trends and quantify the scale and dynamics of COVID-19 infection in a specific community, municipality, or area of service. This study investigates a six-month long SARS-CoV-2 RNA quantification in influent wastewater from four municipal wastewater treatment plants (WWTP) serving the Charlotte region of North Carolina (NC) using both RT-qPCR and RT-ddPCR platforms. Influent wastewater was analyzed for the nucleocapsid (N) genes N1 and N2. Both RT-qPCR and RT-ddPCR performed well for detection and quantification of SARS-CoV-2 using the N1 target, while for the N2 target RT-ddPCR was more sensitive. SARS-CoV-2 concentration ranged from 10³ to 10⁵ copies/L for all four plants. Both RT-qPCR and RT-ddPCR showed a significant positive correlation between SARS-CoV-2 concentrations and the 7-day rolling average of clinically reported COVID-19 cases when lagging 5 to 12 days (ρ = 0.52-0.92, p < 0.001-0.02). A major finding of this study is that RT-qPCR and RT-ddPCR generated SARS-CoV-2 data that was positively correlated (ρ = 0.569, p < 0.0001) and can be successfully used to monitor SARS-CoV-2 signals across the WWTP of different sizes and metropolitan service functions without significant anomalies.

Review and Meta‐Analysis: SARS‐CoV‐2 and Enveloped Virus Detection in Feces and Wastewater

Detection and quantification of viruses supplies key information on their spread and allows risk assessment for public health. In wastewater, existing detection methods have been focusing on non‐enveloped enteric viruses due to enveloped virus transmission, such as coronaviruses, by the fecal‐oral route being less likely. Since the beginning of the SARS‐CoV‐2 pandemic, interest and importance of enveloped virus detection in wastewater has increased. Here, quantitative studies on SARS‐CoV‐2 occurrence in feces and raw wastewater and other enveloped viruses via quantitative real‐time reverse transcription polymerase chain reaction (RT‐qPCR) during the early stage of the pandemic until April 2021 are reviewed, including statistical evaluation of the positive detection rate and efficiency throughout the detection process involving concentration, extraction, and amplification stages. Optimized and aligned sampling protocols and concentration methods for enveloped viruses, along with SARS‐CoV‐2 surrogates, in wastewater environments may improve low and variable recovery rates providing increased detection efficiency and comparable data on viral load measured across different studies.

SARS-CoV-2 RNA surveillance in large to small centralized wastewater treatment plants preceding the third COVID-19 resurgence in Bangkok, Thailand

Wastewater surveillance for SARS-CoV-2 RNA has been a successful indicator of COVID-19 outbreaks in populations prior to clinical testing. However, this has been mostly conducted in high-income countries, which means there is a dearth of performance investigations in low- and middle-income countries with different socio-economic settings. This study evaluated the applicability of SARS-CoV-2 RNA monitoring in wastewater (n = 132) to inform COVID-19 infection in the city of Bangkok, Thailand using CDC N1 and N2 RT-qPCR assays. Wastewater influents (n = 112) and effluents (n = 20) were collected from 19 centralized wastewater treatment plants (WWTPs) comprising four large, four medium, and 11 small WWTPs during seven sampling events from January to April 2021 prior to the third COVID-19 resurgence that was officially declared in April 2021. The CDC N1 assay showed higher detection rates and mostly lower Ct values than the CDC N2. SARS-CoV-2 RNA was first detected at the first event when new reported cases were low. Increased positive detection rates preceded an increase in the number of newly reported cases and increased over time with the reported infection incidence. Wastewater surveillance (both positive rates and viral loads) showed strongest correlation with daily new COVID-19 cases at 22-24 days lag (Spearman's Rho = 0.85-1.00). Large WWTPs (serving 432,000-580,000 of the population) exhibited similar trends of viral loads and new cases to those from all 19 WWTPs, emphasizing that routine monitoring of the four large WWTPs could provide sufficient information for the city-scale dynamics. Higher sampling frequency at fewer sites, i.e., at the four representative WWTPs, is therefore suggested especially during the subsiding period of the outbreak to indicate the prevalence of COVID-19 infection, acting as an early warning of COVID-19 resurgence.

Wastewater surveillance of SARS-CoV-2 in Austria: development, implementation, and operation of the Tyrolean wastewater monitoring program

Wastewater-based epidemiology (WBE) is an effective approach for tracking information on spatial distribution and temporal trends of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at the community level. Herein, the development, implementation, and operation of the wastewater monitoring program serving Tyrol - a federal province of Austria - are described. The development of this program was initiated by Tyrolean health authorities at the end of the first phase of the Coronavirus disease 2019 (COVID-19) pandemic (May 2020). In close co-operation with the water sector and academic institutions, efficient and effective workflows and processes for wastewater surveillance were established. The monitoring program went into operation in November 2020. By the end of July 2021, a total of 5,270 wastewater influent samples collected at 43 sites were analyzed. The monitoring program provided valuable insights into the development of the pandemic situation in Tyrol and fulfilled several tasks that are of importance in different phases of the pandemic. It represented an early-warning system, provided independent confirmation of temporal trends in COVID-19 prevalence, enabled the assessment of the effectiveness of measures, alerted about bursts of disease activity, and provided evidence for the absence of COVID-19. These findings underline the importance of establishing national wastewater monitoring programs as a complementary source of information for efficient and effective pandemic management.

SARS-CoV-2 wastewater surveillance data can predict hospitalizations and ICU admissions

We measured SARS-CoV-2 RNA load in raw wastewater in Attica, Greece, by RT-qPCR for the environmental surveillance of COVID-19 for 6 months. The lag between RNA load and pandemic indicators (COVID-19 hospital and intensive care unit (ICU) admissions) was calculated using a grid search. Our results showed that RNA load in raw wastewater is a leading indicator of positive COVID-19 cases, new hospitalization and admission into ICUs by 5, 8 and 9 days, respectively. Modelling techniques based on distributed/fixed lag modelling, linear regression and artificial neural networks were utilized to build relationships between SARS-CoV-2 RNA load in wastewater and pandemic health indicators. SARS-CoV-2 mutation analysis in wastewater during the third pandemic wave revealed that the alpha-variant was dominant. Our results demonstrate that clinical and environmental surveillance data can be combined to create robust models to study the on-going COVID-19 infection dynamics and provide an early warning for increased hospital admissions.

Inferring SARS-CoV-2 RNA shedding into wastewater relative to the time of infection

Since the start of the coronavirus disease-2019 (COVID-19) pandemic, there has been interest in using wastewater monitoring as an approach for disease surveillance. A significant uncertainty that would improve the interpretation of wastewater monitoring data is the intensity and timing with which individuals shed RNA from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into wastewater. By combining wastewater and case surveillance data sets from a university campus during a period of heightened surveillance, we inferred that individual shedding of RNA into wastewater peaks on average 6 days (50% uncertainty interval (UI): 6-7; 95% UI: 4-8) following infection, and that wastewater measurements are highly overdispersed [negative binomial dispersion parameter, k = 0.39 (95% credible interval: 0.32-0.48)]. This limits the utility of wastewater surveillance as a leading indicator of secular trends in SARS-CoV-2 transmission during an epidemic, and implies that it could be most useful as an early warning of rising transmission in areas where transmission is low or clinical testing is delayed or of limited capacity.

Inferring SARS-CoV-2 RNA shedding into wastewater relative to the time of infection

Since the start of the coronavirus disease-2019 (COVID-19) pandemic, there has been interest in using wastewater monitoring as an approach for disease surveillance. A significant uncertainty that would improve the interpretation of wastewater monitoring data is the intensity and timing with which individuals shed RNA from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into wastewater. By combining wastewater and case surveillance data sets from a university campus during a period of heightened surveillance, we inferred that individual shedding of RNA into wastewater peaks on average 6 days (50% uncertainty interval (UI): 6-7; 95% UI: 4-8) following infection, and that wastewater measurements are highly overdispersed [negative binomial dispersion parameter, k = 0.39 (95% credible interval: 0.32-0.48)]. This limits the utility of wastewater surveillance as a leading indicator of secular trends in SARS-CoV-2 transmission during an epidemic, and implies that it could be most useful as an early warning of rising transmission in areas where transmission is low or clinical testing is delayed or of limited capacity.

Inferring SARS-CoV-2 RNA shedding into wastewater relative to the time of infection

Since the start of the coronavirus disease-2019 (COVID-19) pandemic, there has been interest in using wastewater monitoring as an approach for disease surveillance. A significant uncertainty that would improve the interpretation of wastewater monitoring data is the intensity and timing with which individuals shed RNA from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into wastewater. By combining wastewater and case surveillance data sets from a university campus during a period of heightened surveillance, we inferred that individual shedding of RNA into wastewater peaks on average 6 days (50% uncertainty interval (UI): 6-7; 95% UI: 4-8) following infection, and that wastewater measurements are highly overdispersed [negative binomial dispersion parameter, k = 0.39 (95% credible interval: 0.32-0.48)]. This limits the utility of wastewater surveillance as a leading indicator of secular trends in SARS-CoV-2 transmission during an epidemic, and implies that it could be most useful as an early warning of rising transmission in areas where transmission is low or clinical testing is delayed or of limited capacity.

The devil is in the details: emerging insights on the relevance of wastewater surveillance for SARS-CoV-2 to public health

The severe health consequences and global spread of the COVID-19 pandemic have necessitated the rapid development of surveillance programs to inform public health responses. Efforts to support surveillance capacity have included an unprecedented global research response into the use of genetic signals of SARS-CoV-2 in wastewater following the initial demonstration of the virus' detectability in wastewater in early 2020. The confirmation of fecal shedding of SARS-CoV-2 from asymptomatic, infected and recovering individuals further supports the potential for wastewater analysis to augment public health conventional surveillance techniques based on clinical testing of symptomatic individuals. We have reviewed possible capabilities projected for wastewater surveillance to support pandemic management, including independent, objective and cost-effective data generation that complements and addresses attendant limitations of clinical surveillance, early detection (i.e., prior to clinical reporting) of infection, estimation of disease prevalence, tracking of trends as possible indicators of success or failure of public health measures (mask mandates, lockdowns, vaccination, etc.), informing and engaging the public about pandemic trends, an application within sewer networks to identify infection hotspots, monitoring for presence or changes in infections from institutions (e.g., long-term care facilities, prisons, educational institutions and vulnerable industrial plants) and tracking of appearance/progression of viral variants of concern.

Early warning signal reliability varies with COVID-19 waves

Early warning signals (EWSs) aim to predict changes in complex systems from phenomenological signals in time series data. These signals have recently been shown to precede the emergence of disease outbreaks, offering hope that policymakers can make predictive rather than reactive management decisions. Here, using a novel, sequential analysis in combination with daily COVID-19 case data across 24 countries, we suggest that composite EWSs consisting of variance, autocorrelation and skewness can predict nonlinear case increases, but that the predictive ability of these tools varies between waves based upon the degree of critical slowing down present. Our work suggests that in highly monitored disease time series such as COVID-19, EWSs offer the opportunity for policymakers to improve the accuracy of urgent intervention decisions but best characterize hypothesized critical transitions.

Wastewater Surveillance during Mass COVID-19 Vaccination on a College Campus

The suitability of wastewater monitoring following widespread vaccination against COVID-19 remains uncertain. SARS-CoV-2 RNA levels were monitored in wastewater solids during a university mass vaccination campaign in which >90% of the 12280 students were fully vaccinated (Pfizer-BioNTech, BNT162b2). SARS-CoV-2 RNA levels in wastewater solids correlated with the 7-day average of COVID-19 cases when lagged by 1-3 days (ρ = 0.51-0.55; p = 0.023-0.039). During and after the second vaccine dose, SARS-CoV-2 RNA was not detected in wastewater solids on 19 of 21 days (12 consecutive days of nondetection at the end of the semester), a significant decrease (p = 0.027) in positivity rate. A large influx of outside visitors (move out and commencement) led to an immediate increase in wastewater SARS-CoV-2 RNA positivity (seven detections over seven days). Wastewater solids offer a sensitive matrix for environmental surveillance of COVID-19 at the subsewershed level (50% probability of RNA detection with two cases) both during and after mass vaccination. Mass vaccination was coincident with decreased shedding of SARS-CoV-2 RNA into wastewater. This suggests the absence of a large population of shedding infections, symptomatic or not, following mass vaccination among a university campus population.

Wastewater network infrastructure in public health: Applications and learnings from the COVID-19 pandemic

Accurate estimates of COVID-19 burden of infections in communities can inform public health strategy for the current pandemic. Wastewater based epidemiology (WBE) leverages sewer infrastructure to provide insights on rates of infection by measuring viral concentrations in wastewater. By accessing the sewer network at various junctures, important insights regarding COVID-19 disease activity can be gained. The analysis of sewage at the wastewater treatment plant level enables population-level surveillance of disease trends and virus mutations. At the neighborhood level, WBE can be used to describe trends in infection rates in the community thereby facilitating local efforts at targeted disease mitigation. Finally, at the building level, WBE can suggest the presence of infections and prompt individual testing. In this critical review, we describe the types of data that can be obtained through varying levels of WBE analysis, concrete plans for implementation, and public health actions that can be taken based on WBE surveillance data of infectious diseases, using recent and successful applications of WBE during the COVID-19 pandemic for illustration.