The city-wide full-scale interactive application of sewage surveillance programme for assisting real-time COVID-19 pandemic control - A case study in Hong Kong

Utilizing wastewater surveillance to model behavioural responses and prevent healthcare overload during "Disease X" outbreaks

During the COVID-19 pandemic, healthcare systems worldwide faced severe strain. This study, utilizing wastewater virus surveillance, identified that periodic spontaneous avoidance behaviours significantly impacted infectious disease transmission during rapid and intense outbreaks. To incorporate these behaviours into disease transmission analysis, we introduced the Su-SEIQR model and validated it using COVID-19 wastewater data from Beijing and Hong Kong. The results demonstrated that the Su-SEIQR model accurately reflected trends in susceptible populations and confirmed cases during the COVID-19 pandemic, highlighting the role of spontaneous collective avoidance behaviours in generating periodic fluctuations. These fluctuations helped reduce infection peaks, thereby alleviating pressure on healthcare systems. However, the effect of these spontaneous behaviours on mitigating healthcare overload was limited. Consequently, we incorporated healthcare capacity constraints into the model, adjusting parameters to further guide population behaviours during the pandemic, aiming to keep the outbreak within manageable limits and reduce strain on healthcare resources. This study provides robust support for the development of environmental and public health policies during pandemics by constructing an innovative transmission model, which effectively prevents healthcare overload. Additionally, this approach can be applied to managing future outbreaks of unknown viruses or "Disease X".

Post-pandemic excess mortality of COVID-19 in Hong Kong: a retrospective study

Background

As the COVID-19 pandemic shifted into the post-pandemic period in early 2023, following the COVID-19 normalization with relaxation of stringent control measures and high vaccination coverage in Hong Kong, its long-term impact on mortality remains challenging with necessary needs of data-driven insights. This study examined the pattern of post-pandemic excess mortality in Hong Kong.

Methods

We analyzed weekly inpatient death data from public hospitals from January 1, 2013, to June 1, 2024, using a mixed model with over-dispersed Poisson regression. Expected mortality was estimated as the difference between observed mortality and baseline derived from pre-pandemic data. Age-stratified analyses of overall and cause-specific mortality were conducted across the pre-Omicron pandemic, Omicron, and post-pandemic periods.

Findings

In the post-pandemic period, the excess mortality declined but remained six-fold higher (37.66 [95% CI: 32.72-42.60] per 100,000) than pre-Omicron level, maintaining significance after adjusting for age (32.79 [95% CI: 28.13-37.46] per 100,000). The older population experienced sustained excess mortality, with crude estimates of 100.51 and 586.74 per 100,000 among those aged 65-79 years and ≥80 years, respectively, primarily due to respiratory diseases. Younger population showed near-zero overall excess mortality, whereas increased excess mortality among them occurred in heart disease, cerebrovascular disease, and injuries.

Interpretation

Our findings highlight the lasting mortality impact of pandemic among vulnerable populations, specifically the older population, possibly due to the post-COVID conditions and circulating COVID-19, suggesting the need for targeted interventions for this group.

Funding

Health and Medical Research Fund.

Modeling the spatiotemporal transmission of COVID-19 epidemic by coupling the heterogeneous impact of detection rates: A case study in Hong Kong

During the COVID-19 epidemic, many infections may have been undiagnosed in communities (hidden cases) due to low detection rates, thus exacerbating the overall prevalence of the epidemic. However, the heterogeneity of detection rates poses a challenge in simulating the proportion and spatial distribution of hidden cases. Coupling the heterogeneous impact of detection rates to extend epidemic modeling is necessary for forecasting the health burden and mitigating the inequity of testing resources. In this study, we developed an agent-based model integrated with the Susceptible-Exposed-Reported-Hidden-Removed (SERHR) model to simulate the spatiotemporal transmission of reported and hidden cases (RH-ABM). The RH-ABM was fitted with data for the fifth wave of infection in Hong Kong induced by the Omicron variant. We conducted multi-scenario simulations based on various testing strategies to assess the local variation in attack rates. The RH-ABM predicted that maintaining a constant high detection rate would reduce the average attack rate from 65.62% to 53.09%. Increasing detection rates in groups with many individuals and daily close contact can also assist in controlling the health burden of outbreaks. The variation in the attack rates is strongly associated with changes in the region-stratified detection rates. In addition, The RH-ABM estimated that allocating limited testing resources based on demographic distribution and human mobility data is effective for controlling the average attack rate.

Lessons Learned from COVID-19 Pandemic in Combating Antimicrobial Resistance-Experience of Hong Kong, China

The world has gone through the COVID-19 pandemic and has now returned to normalcy. We reviewed the strategies and public health actions conducted in Hong Kong during the COVID-19 pandemic, and reflected on the lessons learned, which are potentially useful in the fight against antimicrobial resistance (AMR). We recommended extending wastewater surveillance for AMR, apart from SARS-CoV2. We suggested exploring the use of rapid tests in outpatients to aid clinical diagnosis and reduce antibiotic use for viral infections. Stringent infection control measures are crucial to prevent nosocomial transmission of resistant microorganisms, such as vancomycin-resistant enterococci and carbapenemase-producing Enterobacterales in hospitals and in elderly homes. Taking COVID-19 experiences as a reference, transparent data, the prompt dissemination of information, and strategic risk communication should be adopted to maintain sustained behavioral changes in AMR. We also encouraged the adoption of information technology, artificial intelligence, and machine learning in antimicrobial stewardship programs. We also discussed the potential merits and limitations of these strategies. The lessons learned from the COVID-19 pandemic may provide insights into the long battle against AMR.

Wastewater-based epidemiology: deriving a SARS-CoV-2 data validation method to assess data quality and to improve trend recognition

Introduction

Accurate and consistent data play a critical role in enabling health officials to make informed decisions regarding emerging trends in SARS-CoV-2 infections. Alongside traditional indicators such as the 7-day-incidence rate, wastewater-based epidemiology can provide valuable insights into SARS-CoV-2 concentration changes. However, the wastewater compositions and wastewater systems are rather complex. Multiple effects such as precipitation events or industrial discharges might affect the quantification of SARS-CoV-2 concentrations. Hence, analysing data from more than 150 wastewater treatment plants (WWTP) in Germany necessitates an automated and reliable method to evaluate data validity, identify potential extreme events, and, if possible, improve overall data quality.

Methods

We developed a method that first categorises the data quality of WWTPs and corresponding laboratories based on the number of outliers in the reproduction rate as well as the number of implausible inflection points within the SARS-CoV-2 time series. Subsequently, we scrutinised statistical outliers in several standard quality control parameters (QCP) that are routinely collected during the analysis process such as the flow rate, the electrical conductivity, or surrogate viruses like the pepper mild mottle virus. Furthermore, we investigated outliers in the ratio of the analysed gene segments that might indicate laboratory errors. To evaluate the success of our method, we measure the degree of accordance between identified QCP outliers and outliers in the SARS-CoV-2 concentration curves.

Results and discussion

Our analysis reveals that the flow and gene segment ratios are typically best at identifying outliers in the SARS-CoV-2 concentration curve albeit variations across WWTPs and laboratories. The exclusion of datapoints based on QCP plausibility checks predominantly improves data quality. Our derived data quality categories are in good accordance with visual assessments.

Conclusion

Good data quality is crucial for trend recognition, both on the WWTP level and when aggregating data from several WWTPs to regional or national trends. Our model can help to improve data quality in the context of health-related monitoring and can be optimised for each individual WWTP to account for the large diversity among WWTPs.

Rapid and extensive SARS-CoV-2 Omicron variant infection wave revealed by wastewater surveillance in Shenzhen following the lifting of a strict COVID-19 strategy

Wastewater-based epidemiology (WBE) has emerged as a promising tool for monitoring the spread of COVID-19, as SARS-CoV-2 can be shed in the faeces of infected individuals, even in the absence of symptoms. This study aimed to optimize a prediction model for estimating COVID-19 infection rates based on SARS-CoV-2 RNA concentrations in wastewater, and reveal the infection trends and variant diversification in Shenzhen, China following the lifting of a strict COVID-19 strategy. Faecal samples (n = 4337) from 1204 SARS-CoV-2 infected individuals hospitalized in a designated hospital were analysed to obtain Omicron variant-specific faecal shedding dynamics. Wastewater samples from 6 wastewater treatment plants (WWTPs) and 9 pump stations, covering 3.55 million people, were monitored for SARS-CoV-2 RNA concentrations and variant abundance. We found that the viral load in wastewater increased rapidly in December 2022 in the two districts, demonstrating a sharp peak in COVID-19 infections in late-December 2022, mainly caused by Omicron subvariants BA.5.2.48 and BF.7.14. The prediction model, based on the mass balance between total viral load in wastewater and individual faecal viral shedding, revealed a surge in the cumulative infection rate from <0.1 % to over 70 % within three weeks after the strict COVID-19 strategy was lifted. Additionally, 39 cryptic SARS-CoV-2 variants were identified in wastewater, in addition to those detected through clinical surveillance. These findings demonstrate the effectiveness of WBE in providing comprehensive and efficient assessments of COVID-19 infection rates and identifying cryptic variants, highlighting its potential for monitoring emerging pathogens with faecal shedding.

Wastewater multiplex PCR amplicon sequencing revealed community transmission of SARS-CoV-2 lineages during the outbreak of infection in Chinese Mainland

During the COVID-19, wastewater-based epidemiology (WBE) has become a powerful epidemic surveillance tool widely used worldwide. However, the development and application of this technology in Chinese Mainland are relatively lagging. Herein, we for the first time monitored the community circulation of SARS-CoV-2 lineages using WBE methods in Chinese Mainland. During the peak period of infection outbreak at the end of 2022, six precious sewage samples were collected from the manhole in the student dormitory area on Wangjiang Campus of Sichuan University. RT-qPCR revealed that the six sewage samples were all positive for SARS-CoV-2 RNA. Multiplex PCR amplicon sequencing of the sewage samples reflected the local transmission of SARS-CoV-2 variants. The results of two deconvolution methods indicate that the main virus lineages have clear evolutionary genetic correlations. Furthermore, the sampling time is consistent with the timeline of concern for these virus lineages, as well as the timeline of uploading the nucleic acid sequences from the corresponding lineages in Sichuan to the database. These results demonstrate the reliability of the sewage sequencing results. Multiplex PCR amplicon sequencing is by far the most powerful analytical tool of WBE, enabling quantitative detection of virus lineages transmission and evolution at the community level.

Advancing Public Health Surveillance: Integrating Modeling and GIS in the Wastewater-Based Epidemiology of Viruses, a Narrative Review

This review article will present a comprehensive examination of the use of modeling, spatial analysis, and geographic information systems (GIS) in the surveillance of viruses in wastewater. With the advent of global health challenges like the COVID-19 pandemic, wastewater surveillance has emerged as a crucial tool for the early detection and management of viral outbreaks. This review will explore the application of various modeling techniques that enable the prediction and understanding of virus concentrations and spread patterns in wastewater systems. It highlights the role of spatial analysis in mapping the geographic distribution of viral loads, providing insights into the dynamics of virus transmission within communities. The integration of GIS in wastewater surveillance will be explored, emphasizing the utility of such systems in visualizing data, enhancing sampling site selection, and ensuring equitable monitoring across diverse populations. The review will also discuss the innovative combination of GIS with remote sensing data and predictive modeling, offering a multi-faceted approach to understand virus spread. Challenges such as data quality, privacy concerns, and the necessity for interdisciplinary collaboration will be addressed. This review concludes by underscoring the transformative potential of these analytical tools in public health, advocating for continued research and innovation to strengthen preparedness and response strategies for future viral threats. This article aims to provide a foundational understanding for researchers and public health officials, fostering advancements in the field of wastewater-based epidemiology.

Small-scale wastewater-based epidemiology (WBE) for infectious diseases and antibiotic resistance: A scoping review

Wastewater analysis can serve as a source of public health information. In recent years, wastewater-based epidemiology (WBE) has emerged and proven useful for the detection of infectious diseases. However, insights from the wastewater treatment plant do not allow for the small-scale differentiation within the sewer system that is needed to analyze the target population under study in more detail. Small-scale WBE offers several advantages, but there has been no systematic overview of its application. The aim of this scoping review is to provide a comprehensive overview of the current state of knowledge on small-scale WBE for infectious diseases, including methodological considerations for its application. A systematic database search was conducted, considering only peer-reviewed articles. Data analyses included quantitative summary and qualitative narrative synthesis. Of 2130 articles, we included 278, most of which were published since 2020. The studies analyzed wastewater at the building level (n = 203), especially healthcare (n = 110) and educational facilities (n = 80), and at the neighborhood scale (n = 86). The main analytical parameters were viruses (n = 178), notably SARS-CoV-2 (n = 161), and antibiotic resistance (ABR) biomarkers (n = 99), often analyzed by polymerase chain reaction (PCR), with DNA sequencing techniques being less common. In terms of sampling techniques, active sampling dominated. The frequent lack of detailed information on the specification of selection criteria and the characterization of the small-scale sampling sites was identified as a concern. In conclusion, based on the large number of studies, we identified several methodological considerations and overarching strategic aspects for small-scale WBE. An enabling environment for small-scale WBE requires inter- and transdisciplinary knowledge sharing across countries. Promoting the adoption of small-scale WBE will benefit from a common international conceptualization of the approach, including standardized and internationally accepted terminology. In particular, the development of good WBE practices for different aspects of small-scale WBE is warranted. This includes the establishment of guidelines for a comprehensive characterization of the local sewer system and its sub-sewersheds, and transparent reporting to ensure comparability of small-scale WBE results.