Analysis of wastewater may provide advance notice of SARS-CoV-2 infection dynamics in communities before positive tests are reported, suggests a paper in Nature Biotechnology. The findings — based on a ten-week analysis of primary sludge in the New Haven, Connecticut, USA metropolitan area — may help provide up-to-date information on infections in areas with testing capacity limitations or reporting delays.
The progression of COVID-19 is tracked largely by diagnostic testing results and hospital data. However, COVID-19 symptoms may not present for up to two weeks after infection, and there may be delays in reporting positive tests if testing capacity is overwhelmed. Monitoring sewage has been used in previous disease epidemics, and increasing amounts of SARS-CoV-2 RNA in wastewater have been associated with rising numbers of COVID-19 cases.
Jordan Peccia and colleagues conducted a ten-week analysis of primary sewage sludge from a waste treatment plant in New Haven. They collected daily samples from 19 March to 1 June and compared SARS-CoV-2 RNA concentrations in the sludge to publicly available infection data. They analysed the numbers and percentages of positive SARS-CoV-2 tests by specimen collection date, the numbers of positive SARS-CoV-2 tests by reporting date, and COVID-19 hospital admissions. The authors found that viral RNA concentrations in sewage sludge were 0–2 days ahead of the numbers and percentage of positive tests based on specimen collection date. They also found that the sludge data were 1–4 days ahead of hospital admissions and 6–8 days ahead of reported positive tests. The lead time of 6–8 days was largely explained by delays in reporting of test results.
Because sludge and epidemiological data are both susceptible to variability, the authors did not correlate the level of SARS-CoV-2 RNA in sludge and the number of COVID-19 cases. However, they argue that in areas where reporting or testing are delayed, analysis of rising and falling trends in sludge data could provide advance notice of infection dynamics.
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