As public health officials and investors search for reliable leading indicators for the 'second wave' of infections that Dr. Fauci and the WHO insist is right around the corner, one team of researchers has determined that the answers might be found under the ground...
To wit, a team of researchers from Yale University in New Haven, Conn. published a paper earlier this month on their studies of SARS-CoV-2 concentrations in the sewage in the Greater New Haven area. In the abstract of their report, the team determined that "when adjusted for the time lag, the virus RNA concentrations were highly correlated with the COVID-19 epidemiological curve (R2 =0.99) and local hospital admissions (R2 =0.99). SARS-CoV-2 RNA concentrations were a seven-day leading indicator ahead of compiled COVID-19 testing data and led local hospital admissions data by three days."
The search for a reliable leading indicator is critical for developing an effective policy response, since the most closely watched data (reports on the number of newly diagnosed cases) is a lagging indicator, since tests typically aren't run on an individual until symptoms of the virus have already started to appear.
Scientists have already proven that SARS-CoV-2 RNA is present in the human waste of COVID-19 patients. That then seeps into the wastewater in a given community's collection system. An analysis of RNA concentrations in waste can, according to the researchers, "provide information on the prevalence and dynamics of infection for entire populations."
By analyzing wastewater from a sewage plant that serves a four-municipality area (pop ~200,000 residents), the researchers applied several data processing techniques to smooth the data and allow for fair comparisons between the sewage data and data collected by the local hospital system in New Haven.
Like many other scientific specialties, the field of wastewater epidemiology existed before the pandemic. But the global outbreak has allowed scientists to expand on these methods in real time in the hope that it can help predict outbreaks before they overwhelm hospital systems.
New Haven COVID-19 epidemic suggest that these data may provide useful epidemiological insights (Figure 1C). SARS-CoV-2 RNA sludge concentrations were quantitatively compared with data that are commonly used to track the community progression of COVID-19 including hospital admissions (Figure 2A,B) and COVID-19 compiled testing data for the four municipalities (New Haven, East Haven, Hamden, and Woodbridge, CT) served by the ESWPAF (Figure 2C). All three measures traced the initial wave of the SARS-CoV-2 outbreak in the New Haven metropolitan area. Applying Locally Weighted Scatterplot Smoothing (LOWESS) to the data and rescaling enables comparison (Figure 2A,B). The virus RNA time course peaked 3 days earlier than hospital admissions (April 9 versus April 12) and a cross correlation analysis revealed a correlation coefficient (R=0.996) between smoothed RNA and hospital data when the latter was shifted 3 days forward.
The team found that the "peak" level of virus RNA arrived 3 days before hospitals reported their 'peak' number of patients.
"Normally when I tell people I work with poo, they're not super-interested," Stephanie Loeb, a postdoctoral researcher at Stanford University, told NPR. But "there's really a lot of information in our waste."
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