Bibliographic Details
| Title: |
Wastewater-based epidemiology for COVID-19 surveillance and beyond: A survey |
| Authors: |
Chen Chen, Yunfan Wang, Gursharn Kaur, Aniruddha Adiga, Baltazar Espinoza, Srinivasan Venkatramanan, Andrew Warren, Bryan Lewis, Justin Crow, Rekha Singh, Alexandra Lorentz, Denise Toney, Madhav Marathe |
| Source: |
Epidemics, Vol 49, Iss , Pp 100793- (2024) |
| Publisher Information: |
Elsevier, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Infectious and parasitic diseases |
| Subject Terms: |
Wastewater, Epidemiology, Infectious disease, COVID-19, Survey, Infectious and parasitic diseases, RC109-216 |
| More Details: |
The pandemic of COVID-19 has imposed tremendous pressure on public health systems and social economic ecosystems over the past years. To alleviate its social impact, it is important to proactively track the prevalence of COVID-19 within communities. The traditional way to estimate the disease prevalence is to estimate from reported clinical test data or surveys. However, the coverage of clinical tests is often limited and the tests can be labor-intensive, requires reliable and timely results, and consistent diagnostic and reporting criteria. Recent studies revealed that patients who are diagnosed with COVID-19 often undergo fecal shedding of SARS-CoV-2 virus into wastewater, which makes wastewater-based epidemiology for COVID-19 surveillance a promising approach to complement traditional clinical testing. In this paper, we survey the existing literature regarding wastewater-based epidemiology for COVID-19 surveillance and summarize the current advances in the area. Specifically, we have covered the key aspects of wastewater sampling, sample testing, and presented a comprehensive and organized summary of wastewater data analytical methods. Finally, we provide the open challenges on current wastewater-based COVID-19 surveillance studies, aiming to encourage new ideas to advance the development of effective wastewater-based surveillance systems for general infectious diseases. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1755-4365 |
| Relation: |
http://www.sciencedirect.com/science/article/pii/S1755436524000549; https://doaj.org/toc/1755-4365 |
| DOI: |
10.1016/j.epidem.2024.100793 |
| Access URL: |
https://doaj.org/article/6da60cdbea9d41a2ae42341405230ded |
| Accession Number: |
edsdoj.6da60cdbea9d41a2ae42341405230ded |
| Database: |
Directory of Open Access Journals |
