Using wastewater-based epidemiology as a potential instrument for the prediction and control of COVID-19 disease outbreaks.
Cent Eur J Public Health
; 30(1): 3-6, 2022 Mar.
Article
in English
| MEDLINE | ID: covidwho-1789806
ABSTRACT
The COVID-19 pandemic has revealed a significant number of cracks in the current vigilance techniques that stand to minimise outbreaks of SARS-CoV-2. There is a serious inadequacy of the testing capacity of healthcare systems worldwide, which can be attributed to the lack of appropriate testing and monitoring methods for a disease such as COVID-19. The current tools in use for COVID-19 surveillance are either expensive, not applicable to large populations or yield results after the outbreak has already occurred. The immense contagiousness in combination with a wealth of asymptomatic carriers means that RT-PCR testing is not feasible on a mass scale. It is evident that new methods are required for the monitoring of COVID-19 and a range of new epidemiological tools must be implemented if public health systems worldwide want to make relevant predictions on the patterns of disease spread and increase the efficacy of their decisions. In addition to this, the pandemic has highlighted the necessity for redirecting biomedical research towards early diagnosis and rational therapy of respiratory viruses in particular, as well as prevention of their spread by conventional means. An efficient early detection system would save lives and allow countries to return to pre-pandemic standards of living. At the forefront of this lies wastewater-based epidemiology, which carries immense potential as a means of pre-symptomatic diagnosis and population-based surveillance.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
COVID-19
Type of study:
Observational study
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
Cent Eur J Public Health
Journal subject:
Epidemiology
/
Public Health
Year:
2022
Document Type:
Article
Affiliation country:
Cejph.a7161
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