Predicting COVID-19 Incidence Using Wastewater Surveillance Data, Denmark, October 2021-June 2022

Lifting of travel restrictions brings additional noise in COVID-19 surveillance through wastewater-based epidemiology in post-pandemic period

The post-pandemic world still faces ongoing COVID-19 infections, although international travel has returned to pre-pandemic conditions. Wastewater-based epidemiology (WBE) is considered an efficient tool for the population-wide surveillance of COVID-19 infections during the pandemic. However, the performance of WBE in post-pandemic era with travel restrictions lifted remains unknown. Utilizing weekly county-level wastewater surveillance data from June 2021-November 2022 for 222 counties in 49 states (covering 104 million people) in the United States of America, we retrospectively evaluated the correlations between SARS-CoV-2 RNA (CRNA) and reported cases, as well as the impacts of international air travel, demographics, socioeconomic aspects, test accessibility, epidemiological, and environmental factors on reported cases under the corresponding CRNA. The lifting of travel restrictions in June 2022, shifted the correlation between CRNA and COVID-19 incidence in the following 7-day and 14-day from 0.70 (IQR: 0.30-0.88) in June 2021-May 2022 (pandemic) to 0.01 (IQR: -0.31-0.36) in June-November 2022 (post-pandemic), and from 0.74 (IQR: 0.31-0.90) to -0.01 (IQR: -0.38-0.45), respectively. Besides, after lifting the travel restrictions, under the same CRNA, the reported case numbers were impacted by many factors, including the variations of international passengers, test accessibility, Omicron prevalence, ratio of population aged between 18 and 65, minority vulnerability, and healthcare system. This highlights the importance of demographics, infection testing, variants and socioeconomic status on the accuracy and implication of WBE to monitor COVID-19 infection status in post-pandemic era. Our findings facilitate the public health authorities to dynamically adjust their WBE-based tools/strategies to the local contexts to achieve optimal community surveillance.

Data for action - description of the automated COVID-19 surveillance system in Denmark and lessons learnt, January 2020 to June 2024

Denmark is one of the leading countries in establishing digital solutions in the health sector. When SARS-CoV-2 arrived in February 2020, a real-time surveillance system could be rapidly built on existing infrastructure, This rapid data integration for COVID-19 surveillance enabled a data-driven response. Here we describe (a) the setup of the automated, real-time surveillance and vaccination monitoring system for COVID-19 in Denmark, including primary stakeholders, data sources, and algorithms, (b) describe outputs for various stakeholders, (c) how outputs were used for action and (d) reflect on challenges and lessons learnt. Outputs were tailored to four main stakeholder groups: four outputs provided direct information to individual citizens, four to complementary systems and researchers, 25 to decision-makers, and 15 informed the public, aiding transparency. Core elements in infrastructure needed for automated surveillance had been in place for more than a decade. The COVID-19 epidemic was a pressure test that allowed us to explore the system's potential and identify challenges for future pandemic preparedness. The system described here constitutes a model for the future infectious disease surveillance in Denmark. With the current pandemic threat posed by avian influenza viruses, lessons learnt from the COVID-19 pandemic remain topical and relevant.

Predicting hospital admissions due to COVID-19 in Denmark using wastewater-based surveillance

Wastewater surveillance has become a fundamental tool to monitor the circulation of SARS-CoV-2 in order to prepare timely public health responses. In this study we integrate available clinical data on hospital admissions with wastewater surveillance data and investigate if predictions of the number of hospital admissions due to COVID-19 in Danish hospitals are improved by including wastewater concentrations of SARS-CoV-2. We implement state space models to describe the relationship between the number of hospital admissions due to COVID-19, available with a three-week classification delay, and more recent numbers of total hospital admissions with COVID-19. Including wastewater concentrations of SARS-CoV-2, we consider five-week predictions of the number of hospital admissions due to COVID-19. As a result of the three-week classification delay, the predictions translate into two hindcasts, one nowcast and two forecasts. The predicted values for all time frames follow the observed numbers well. We find that log likelihood values are higher when including wastewater concentrations (across all horizons) and that lagging the wastewater observations to investigate whether changes in wastewater concentrations occur before changes in hospital admissions does not result in further improvements. Our study shows that including wastewater concentrations improve estimates of the number of hospital admissions due to COVID-19, implying that wastewater concentrations add valuable information about the underlying transmission and that the imminent development of the near-future disease burden from COVID-19 is better informed when carefully including wastewater concentrations.

Integrating agent-based disease, mobility and wastewater models for the study of the spread of communicable diseases

Wastewater-based epidemiology was utilized during the COVID-19 outbreak to monitor the circulation of SARS-CoV-2, the virus causing this disease. However, this approach is limited by the need for additional methods to accurately translate virus concentrations in wastewater to disease-positive human counts. Combined modelling of COVID-19 disease cases and the concentration of its causative virus, SARS-CoV-2, in wastewater will necessarily deepen our understanding. However, this requires addressing the technical differences between disease, population mobility and wastewater models. To that end, we developed an integrated Agent-Based Model (ABM) that facilitates analysis in space and time at various temporal resolutions, including disease spread, population mobility and wastewater production, while also being sufficiently generic for different types of infectious diseases or pathogens. The integrated model replicates the epidemic curve for COVID-19 and can estimate the daily infections at the household level, enabling the monitoring of the spatial patterns of infection intensity. Additionally, the model allows monitoring the estimated production of infected wastewater over time and spatially across the sewage and treatment plant. The model addresses differences between resolutions and can potentially support Early Warning Systems (EWS) for future pandemics.

Development of Bayesian segmented Poisson regression model to forecast COVID-19 dynamics based on wastewater data: a case study in Nanning City, China

INTRODUCTION

COVID-19 has caused tremendous hardships and challenges around the globe. Due to the prevalence of asymptomatic and pre-symptomatic carriers, relying solely on disease testing to screen for infections is not entirely reliable, which may affect the accuracy of predictions about the pandemic trends. This study is dedicated to developing a predictive model aimed at estimating of the dynamics of COVID-19 at an early stage based on wastewater data, to assist in establishing an effective early warning system for disease control.

METHOD

Viral load in wastewater and the number of daily reported COVID-19 cases were collected from Nanning CDC and the Chinese Disease Prevention and Control Information System, respectively. We used the viral load to estimate daily reported cases by a Bayesian linear regression model. Subsequently, a Bayesian (segmented) Poisson regression model was developed, using data from the first wave of the epidemic as prior information, to predict the COVID-19 epidemic trend of the second wave. Finally, in order to explore the optimal training data for predicting outbreak dynamics during the pandemic, we fitted the model using various training sets.

RESULTS

The results revealed the estimated cases, using the viral load with a 3-day lag, were consistent with the actual reported cases, with adjusted R² value of 0.935 (p < 0.001). Our model successfully predicted the epidemic peak time and provided early warnings on the third day after the outbreak began. Furthermore, after using data from the first 6 days of the outbreak, the model's MAPE rapidly decreasing to lower levels (MAPE = 29.34%) and eventually stabilized at approximately 20%. Compared to using non-informative priors, this result allows for an advance warning of approximately two weeks. Importantly, as the inclusion of data from early outbreak increased, the predictive results of the model became more stable and accurate.

CONCLUSION

This study demonstrates the potential of wastewater-based epidemiology combined with Bayesian methods as a monitoring and predictive tool during infectious disease outbreaks.

Epidemiological model can forecast COVID-19 outbreaks from wastewater-based surveillance in rural communities

Wastewater has emerged as a crucial tool for infectious disease surveillance, offering a valuable means to bridge the equity gap between underserved communities and larger urban municipalities. However, using wastewater surveillance in a predictive manner remains a challenge. In this study, we tested if detecting SARS-CoV-2 in wastewater can forecast outbreaks in rural communities. Under the CDC National Wastewater Surveillance program, we monitored the SARS-CoV-2 in the wastewater of five rural communities and a small city in Idaho (USA). We then used a particle filter method coupled with a stochastic susceptible-exposed-infectious-recovered (SEIR) model to infer active case numbers using quantities of SARS-CoV-2 in wastewater. Our findings revealed that while high daily variations in wastewater viral load made real-time interpretation difficult, the SEIR model successfully factored out this noise, enabling accurate forecasts of the Omicron outbreak in five of the six towns shortly after initial increases in SARS-CoV-2 concentrations were detected in wastewater. The model predicted outbreaks with a lead time of 0 to 11 days (average of 6 days +/- 4) before the surge in reported clinical cases. This study not only underscores the viability of wastewater-based epidemiology (WBE) in rural communities-a demographic often overlooked in WBE research-but also demonstrates the potential of advanced epidemiological modeling to enhance the predictive power of wastewater data. Our work paves the way for more reliable and timely public health guidance, addressing a critical gap in the surveillance of infectious diseases in rural populations.

JN.1 and the ongoing battle: unpacking the characteristics of a new dominant COVID-19 variant

In the fourth year of the COVID-19 occurrence, a new COVID-19 variant, JN.1, has emerged and spread globally and become the dominant strain in several regions. It has some specific mutations in its spike proteins, empowering it with higher transmissibility. Regarding the significance of the issue, understanding the clinical and immunological traits of JN.1 is critical for enhancing health strategies and vaccination efforts globally, with the ultimate goal of bolstering our collective response to the pandemic. In this study, we take a look at the latest findings of JN.1 characteristics and mutations as well as its consequences on bypassing immune system. We demonstrate the importance of continual surveillance and strategic adaptation within healthcare frameworks along with the significance of wastewater sampling for the rapid identification of emerging SARS-CoV-2 variants.

Jointly estimating epidemiological dynamics of Covid-19 from case and wastewater data in Aotearoa New Zealand

BACKGROUND

Timely and informed public health responses to infectious diseases such as COVID-19 necessitate reliable information about infection dynamics. The case ascertainment rate (CAR), the proportion of infections that are reported as cases, is typically much less than one and varies with testing practices and behaviours, making reported cases unreliable as the sole source of data. The concentration of viral RNA in wastewater samples provides an alternate measure of infection prevalence that is not affected by clinical testing, healthcare-seeking behaviour or access to care.

METHODS

We construct a state-space model with observed data of levels of SARS-CoV-2 in wastewater and reported case incidence and estimate the hidden states of the effective reproduction number, R, and CAR using sequential Monte Carlo methods.

RESULTS

We analyse data from 1 January 2022 to 31 March 2023 from Aotearoa New Zealand. Our model estimates that R peaks at 2.76 (95% CrI 2.20, 3.83) around 18 February 2022 and the CAR peaks around 12 March 2022. We calculate that New Zealand's second Omicron wave in July 2022 is similar in size to the first, despite fewer reported cases. We estimate that the CAR in the BA.5 Omicron wave in July 2022 is approximately 50% lower than in the BA.1/BA.2 Omicron wave in March 2022.

CONCLUSIONS

Estimating R, CAR, and cumulative number of infections provides useful information for planning public health responses and understanding the state of immunity in the population. This model is a useful disease surveillance tool, improving situational awareness of infectious disease dynamics in real-time.

Online dashboards for SARS-CoV-2 wastewater-based epidemiology

Aim: Wastewater-based epidemiology (WBE) is increasingly used to monitor pandemics. In this manuscript, we review methods and limitations of WBE, as well as their online dashboards. Materials & methods: Online dashboards were retrieved using PubMed and search engines, and annotated for timeliness, availability of English version, details on SARS-CoV-2 sublineages, normalization by population and PPMoV load, availability of case/hospitalization count charts and of raw data for export. Results: We retrieved 51 web portals, half of them from Europe. Africa is represented from South Africa only, and only seven portals are available from Asia. Conclusion: WBS provides near-real-time cost-effective monitoring of analytes across space and time in populations. However, tremendous heterogeneity still persists in the SARS-CoV-2 WBE literature.

Results from the SARS-CoV-2 wastewater-based surveillance system in Denmark, July 2021 to June 2022

The microbiological analysis of wastewater samples is increasingly used for the surveillance of SARS-CoV-2 globally. We described the setup process of the national SARS-CoV-2 wastewater-based surveillance system in Denmark, presented its main results during the first year of activities, from July 2021 to June 2022, and discussed their operational significance. The Danish SARS-CoV-2 wastewater-based surveillance system was designed to cover 85 % of the population in Denmark and it entailed taking three weekly samples from 230 sites. Samples were RT-qPCR tested for SARS-CoV-2 RNA, targeting the genetic markers N1, N2 and RdRp, and for two faecal indicators, Pepper Mild Mottle Virus and crAssphage. We calculated the weekly SARS-CoV-2 RNA concentration in the wastewater from each sampling site and monitored it in view of the results from individual testing, at the national and regional levels. We attempted to use wastewater results to identify potential local outbreaks, and we sequenced positive wastewater samples using Nanopore sequencing to monitor the circulation of viral variants in Denmark. The system reached its full implementation by October 2021 and covered up to 86.4 % of the Danish population. The system allowed for monitoring of the national and regional trends of SARS-CoV-2 infections in Denmark. However, the system contribution to the identification of potential local outbreaks was limited by the extensive information available from clinical testing. The sequencing of wastewater samples identified relevant variants of concern, in line with results from sequencing of human samples. Amidst the COVID-19 pandemic, Denmark implemented a nationwide SARS-CoV-2 wastewater-based surveillance system that integrated routine surveillance from individual testing. Today, while testing for COVID-19 at the community level has been discontinued, the system is on the frontline to monitor the occurrence and spread of SARS-CoV-2 in Denmark.

Epidemiological model can forecast COVID-19 outbreaks from wastewater-based surveillance in rural communities

Wastewater can play a vital role in infectious disease surveillance, especially in underserved communities where it can reduce the equity gap to larger municipalities. However, using wastewater surveillance in a predictive manner remains a challenge. We tested if detecting SARS-CoV-2 in wastewater can predict outbreaks in rural communities. Under the CDC National Wastewater Surveillance program, we monitored several rural communities in Idaho (USA). While high daily variations in wastewater viral load made real-time interpretation difficult, a SEIR model could factor out the data noise and forecast the start of the Omicron outbreak in five of the six cities that were sampled soon after SARS-CoV-2 quantities increased in wastewater. For one city, the model could predict an outbreak 11 days before reported clinical cases began to increase. An epidemiological modeling approach can transform how epidemiologists use wastewater data to provide public health guidance on infectious diseases in rural communities.

Timely Monitoring of SARS-CoV-2 RNA Fragments in Wastewater Shows the Emergence of JN.1 (BA.2.86.1.1, Clade 23I) in Berlin, Germany

The importance of COVID-19 surveillance from wastewater continues to grow since case-based surveillance in the general population has been scaled back world-wide. In Berlin, Germany, quantitative and genomic wastewater monitoring for SARS-CoV-2 is performed in three wastewater treatment plants (WWTP) covering 84% of the population since December 2021. The SARS-CoV-2 Omicron sublineage JN.1 (B.2.86.1.1), was first identified from wastewater on 22 October 2023 and rapidly became the dominant sublineage. This change was accompanied by a parallel and still ongoing increase in the notification-based 7-day-hospitalization incidence of COVID-19 and COVID-19 ICU utilization, indicating increasing COVID-19 activity in the (hospital-prone) population and a higher strain on the healthcare system. In retrospect, unique mutations of JN.1 could be identified in wastewater as early as September 2023 but were of unknown relevance at the time. The timely detection of new sublineages in wastewater therefore depends on the availability of new sequences from GISAID and updates to Pango lineage definitions and Nextclade. We show that genomic wastewater surveillance provides timely public health evidence on a regional level, complementing the existing indicators.

Evaluation of the pilot wastewater surveillance for SARS-CoV-2 in Norway, June 2022 - March 2023

BACKGROUND

During the COVID-19 pandemic, wastewater-based surveillance gained great international interest as an additional tool to monitor SARS-CoV-2. In autumn 2021, the Norwegian Institute of Public Health decided to pilot a national wastewater surveillance (WWS) system for SARS-CoV-2 and its variants between June 2022 and March 2023. We evaluated the system to assess if it met its objectives and its attribute-based performance.

METHODS

We adapted the available guidelines for evaluation of surveillance systems. The evaluation was carried out as a descriptive analysis and consisted of the following three steps: (i) description of the WWS system, (ii) identification of users and stakeholders, and (iii) analysis of the system's attributes and performance including sensitivity, specificity, timeliness, usefulness, representativeness, simplicity, flexibility, stability, and communication. Cross-correlation analysis was performed to assess the system's ability to provide early warning signal of new wave of infections.

RESULTS

The pilot WWS system was a national surveillance system using existing wastewater infrastructures from the largest Norwegian municipalities. We found that the system was sensitive, timely, useful, representative, simple, flexible, acceptable, and stable to follow the general trend of infection. Preliminary results indicate that the system could provide an early signal of changes in variant distribution. However, challenges may arise with: (i) specificity due to temporary fluctuations of RNA levels in wastewater, (ii) representativeness when downscaling, and (iii) flexibility and acceptability when upscaling the system due to limited resources and/or capacity.

CONCLUSIONS

Our results showed that the pilot WWS system met most of its surveillance objectives. The system was able to provide an early warning signal of 1-2 weeks, and the system was useful to monitor infections at population level and complement routine surveillance when individual testing activity was low. However, temporary fluctuations of WWS values need to be carefully interpreted. To improve quality and efficiency, we recommend to standardise and validate methods for assessing trends of new waves of infection and variants, evaluate the WWS system using a longer operational period particularly for new variants, and conduct prevalence studies in the population to calibrate the system and improve data interpretation.

First cases of SARS-CoV-2 BA.2.86 in Denmark, 2023

We describe 10 cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant BA.2.86 detected in Denmark, including molecular characteristics and results from wastewater surveillance that indicate that the variant is circulating in the country at a low level. This new variant with many spike gene mutations was classified as a variant under monitoring by the World Health Organization on 17 August 2023. Further global monitoring of COVID-19, BA.2.86 and other SARS-CoV-2 variants is highly warranted.