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

Galani, Aikaterini; Aalizadeh, Reza; Kostakis, Marios; Markou, Athina; Alygizakis, Nikiforos; Lytras, Theodore; Adamopoulos, Panagiotis G.; Peccia, Jordan; Thompson, David C.; Kontou, Aikaterini; Karagiannidis, Apostolos; Lianidou, Evi; Avgeris, Margaritis; Paraskevis, Dimitrios; Tsiodras, Sotirios; Scorilas, Andreas; Vasiliou, Vasilis; Dimopoulos, Meletios-Athanasios; Thomaidis, Nikolaos S.

doi:10.1016/j.scitotenv.2021.150151

Cited by 148 publications

(104 citation statements)

References 49 publications

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“…The presence of SARS-CoV-2 in wastewater treatment plant (WWTP) influent, and within the sewer network, can help determine the presence of infection in a community. This is because the detection of SARS-CoV-2 genetic material in wastewater is indicative of the number of COVID-19 cases (dependent on the testing levels of that community) and thus can present a valuable early indicator for local hospitalisations, viral outbreaks or COVID-19 cases (Larsen & Wigginton 2020;Galani et al 2022). This information can be used as an epidemiological indicator even in areas where community testing is not possible, for instance in low-income environments where resources are limited.…”

Section: Introductionmentioning

confidence: 99%

Establishment of local wastewater-based surveillance programmes in response to the spread and infection of COVID-19 – case studies from South Africa, the Netherlands, Turkey and England

Tlhagale

Liphadzi

Bhagwan

et al. 2022

Journal of Water and Health

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The COVID-19 pandemic has resulted in over 340 million infection cases (as of 21 January 2022) and more than 5.57 million deaths globally. In reaction, science, technology and innovation communities across the globe have organised themselves to contribute to national responses to COVID-19 disease. A significant contribution has been from the establishment of wastewater-based epidemiological (WBE) surveillance interventions and programmes for monitoring the spread of COVID-19 in at least 55 countries. Here, we examine and share experiences and lessons learnt in establishing such surveillance programmes. We use case studies to highlight testing methods and logistics considerations associated in scaling the implementing of such programmes in South Africa, the Netherlands, Turkey and England. The four countries were selected to represent different regions of the world and the perspective based on the considerable progress made in establishing and implementing their national WBE programmes. The selected countries also represent different climatic zones, economies, and development stages, which influence the implementation of national programmes of this nature and magnitude. In addition, the four countries' programmes offer good experiences and lessons learnt since they are systematic, and cover extensive areas, disseminate knowledge locally and internationally and partnered with authorities (government). The programmes also strengthened working relations and partnerships between and among local and global organisations. This paper shares these experiences and lessons to encourage others in the water and public health sectors on the benefits and value of WBE in tackling SARS-CoV-2 and related future circumstances.

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Section: Introductionmentioning

confidence: 99%

Establishment of local wastewater-based surveillance programmes in response to the spread and infection of COVID-19 – case studies from South Africa, the Netherlands, Turkey and England

Tlhagale

Liphadzi

Bhagwan

et al. 2022

Journal of Water and Health

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“…Furthermore, some settings may have no appetite for COVID-19 risk mitigation regardless of air surveillance results. Environmental surveillance in these settings may nonetheless be valuable to public health alone, allowing them to anticipate and respond quickly to surges in respiratory disease 60 . In fact, in settings where diagnostic testing for SARS-CoV-2 is limited by pandemic fatigue and apathy towards risk minimization measures, air sampling could be exceptionally useful in providing baseline data on respiratory virus levels that would otherwise be impossible to obtain.…”

Section: Discussionmentioning

confidence: 99%

SARS-CoV-2 and other respiratory pathogens are detected in continuous air samples from congregate settings

Ramuta

Newman

Brakefield

et al. 2022

Preprint

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Two years after the emergence of SARS-CoV-2, there is still a need for better ways to assess the risk of transmission in congregate spaces. We deployed active air samplers to monitor the presence of SARS-CoV-2 in real-world settings across communities in the Upper Midwestern states of Wisconsin and Minnesota. Over 29 weeks, we collected 527 air samples from 15 congregate settings and detected 106 SARS-CoV-2 positive samples, demonstrating SARS-CoV-2 can be detected in air collected from daily and weekly sampling intervals. We expanded the utility of air surveillance to test for 40 other respiratory pathogens. Surveillance data revealed differences in timing and location of SARS-CoV-2 and influenza A virus detection in the community. In addition, we obtained SARS-CoV-2 genome sequences from air samples to identify variant lineages. Collectively, this shows air surveillance is a scalable, cost-effective, and high throughput alternative to individual testing for detecting respiratory pathogens in congregate settings.

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“…Together, these factors may have limited the ability of the CSSS to detect local outbreaks in vulnerable neighbourhoods, areas where lower community testing rates were also observed during the pandemic 12 . In future epidemic surveillance efforts, combining syndromic surveillance with non-participatory data sources, such as number of calls regarding specific complaints to nurse telephone consultation services 19 , measurements of virus occurrence in wastewater 20, 21 , monitoring of mobility patters in the population 22 , and aggregate data on vaccination rates across neighbourhoods, may constitute a cost-efficient way to characterize community infection trends and predict increased demands on healthcare resources.…”

Section: Discussionmentioning

confidence: 99%

App-based COVID-19 syndromic surveillance and prediction of hospital admissions: The COVID Symptom Study Sweden

Kennedy

Fitipaldi

Hammar

et al. 2021

Preprint

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Background The response of the Swedish authorities to the COVID-19 pandemic was less restrictive than in most countries during the first year, with infection and death rates substantially higher than in neighbouring Nordic countries. Because access to PCR testing was limited during the first wave (February to June 2020) and regional data were reported with delay, adequate monitoring of community disease spread was hampered. The app-based COVID Symptom Study was launched in Sweden to disseminate real-time estimates of disease spread and to collect prospective data for research. The aim of this study was to describe the research project, develop models for estimation of COVID-19 prevalence and to evaluate it for prediction of hospital admissions for COVID-19. Methods We enrolled 143 531 study participants (≥18 years) throughout Sweden, who contributed 10.6 million daily symptom reports between April 29, 2020 and February 10, 2021. Data from 19 161 self-reported PCR tests were used to create a symptom-based algorithm to estimate daily prevalence of symptomatic COVID-19. The prediction model was validated using external datasets. We further utilized the model estimates to forecast subsequent new hospital admissions. Findings A prediction model for symptomatic COVID-19 based on 17 symptoms, age, and sex yielded an area under the ROC curve of 0.78 (95% CI 0.74-0.83) in an external validation dataset of 943 PCR-tested symptomatic individuals. App-based surveillance proved particularly useful for predicting hospital trends in times of insufficient testing capacity and registration delays. During the first wave, our prediction model estimates demonstrated a lower mean error (0.38 average new daily hospitalizations per 100 000 inhabitants per week (95% CI 0.32, 0.45)) for subsequent hospitalizations in the ten most populated counties, than a model based on confirmed case data (0.72 (0.64, 0.81)). The model further correctly identified on average three out of five counties (95% CI 2.3, 3.7) with the highest rates of hospitalizations the following week during the first wave and four out of five (3.0, 4.6) during the second wave. Interpretation The experience of the COVID Symptom Study highlights the important role citizens can play in real-time monitoring of infectious diseases, and how app-based data collection may be used for data-driven rapid responses to public health challenges.

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

Cited by 148 publications

References 49 publications

Establishment of local wastewater-based surveillance programmes in response to the spread and infection of COVID-19 – case studies from South Africa, the Netherlands, Turkey and England

Establishment of local wastewater-based surveillance programmes in response to the spread and infection of COVID-19 – case studies from South Africa, the Netherlands, Turkey and England

SARS-CoV-2 and other respiratory pathogens are detected in continuous air samples from congregate settings

App-based COVID-19 syndromic surveillance and prediction of hospital admissions: The COVID Symptom Study Sweden

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