Letters
https://doi.org/10.1038/s41587-020-0684-z
1
Department of Chemical and Environmental Engineering, School of Engineering and Applied Science, Yale University, New Haven, CT, USA. 2
Connecticut
Agricultural Experimental Station, State of Connecticut, New Haven, CT, USA. 3
Department of Epidemiology of Microbial Disease, School of Public
Health, Yale University, New Haven, CT, USA. 4
Public Health Modeling, Yale School of Public Health, New Haven, CT, USA. 5
School of Management, Yale
University, New Haven, CT, USA. 6
Yale School of Medicine, New Haven, CT, USA. 7
Yale Institute for Global Health, New Haven, CT, USA. 8
Department of
Biostatistics, Yale School of Public Health, Yale University, New Haven, CT, USA. 9
Yale School of Nursing, Orange, CT, USA. 10
These authors contributed
equally: Jordan Peccia, Alessandro Zulli, Doug E. Brackney. ✉e-mail: jordan.peccia@yale.edu; saad.omer@yale.edu
We measured severe acute respiratory syndrome coronavirus
2 (SARS-CoV-2) RNA concentrations in primary sewage
sludge in the New Haven, Connecticut, USA, metropolitan area
during the Coronavirus Disease 2019 (COVID-19) outbreak in
Spring 2020. SARS-CoV-2 RNA was detected throughout the
more than 10-week study and, when adjusted for time lags,
tracked the rise and fall of cases seen in SARS-CoV-2 clinical
test results and local COVID-19 hospital admissions. Relative
totheseindicators,SARS-CoV-2RNAconcentrationsinsludge
were 0–2 d ahead of SARS-CoV-2 positive test results by date
of specimen collection, 0–2 d ahead of the percentage of positive
tests by date of specimen collection, 1–4 d ahead of local
hospital admissions and 6–8 d ahead of SARS-CoV-2 positive
test results by reporting date. Our data show the utility of viral
RNA monitoring in municipal wastewater for SARS-CoV-2
infection surveillance at a population-wide level. In communities
facing a delay between specimen collection and the
reporting of test results, immediate wastewater results can
provide considerable advance notice of infection dynamics.
The progression of the COVID-19 pandemic has been monitored
primarily by testing symptomatic individuals for the presence
of SARS-CoV-2 RNA and counting the number of positive tests over
time1
. However, in the United States and other countries, the spread
of COVID-19 has commonly exceeded the testing capacity of public
health systems. Moreover, test results are a lagging indicator of
the pandemic’s progression2,3
, because testing is usually prompted
by symptoms, which might take 2 weeks to present after infection4
,
and delays occur between the appearance of symptoms, testing and
the reporting of test results. Monitoring sewage in a community’s
collection or treatment system has been used previously to provide
early surveillance of disease prevalence at a population-wide level,
notably for polio5,6
, and might be similarly beneficial for the current
COVID-19 pandemic. SARS-CoV-2 RNA is present in the stool of
patients with COVID-19 (refs. 7–9
) and in raw wastewater10–12
, and
increased RNA concentrations in raw wastewater have been recently
associated with increases in reported COVID-19 cases11
. However,
the utility of wastewater SARS-CoV-2 concentrations for tracking
the progression of COVID-19 infections is poorly understood.
In this study, we investigated how viral RNA concentrations in
Measurement of SARS-CoV-2 RNA in wastewater
tracks community infection dynamics
Jordan Peccia   1,10 ✉, Alessandro Zulli   1,10
, Doug E. Brackney   2,10
, Nathan D. Grubaugh   3
,
Edward H. Kaplan   1,4,5
, Arnau Casanovas-Massana   3
, Albert I. Ko3
, Amyn A. Malik   6,7
,
Dennis Wang6
, Mike Wang   6
, Joshua L. Warren8
, Daniel M. Weinberger3
, Wyatt Arnold1
and
Saad B. Omer   3,6,7,9 ✉
0 200,000 400,000
0
100,000
200,000
300,000
400,000
500,000
Virus RNA per ml Rep 1 (N1)
VirusRNApermlRep2(N1)
Slope = 0.99
R2
= 0.75
0 100,000 200,000 300,000
0
100,000
200,000
300,000
Virus RNA per ml Rep 1 (N2)
VirusRNApermlRep2(N2)
Slope = 0.97
R2
= 0.62
a
b
Fig. 1 | Replicate RNA extraction and analyses for SARS-CoV-2 RNA. a,
Comparison of SARS-CoV-2 RNA concentration between two replicates
(Rep 1 and Rep 2) using the N1 primer set. b, Comparison of SARS-CoV-2
RNA concentration between two replicates using the N2 primer set.
Nature Biotechnology | VOL 38 | October 2020 | 1164–1167 | www.nature.com/naturebiotechnology1164
LettersNAtUREBIOtECHnOlOgy
wastewater correlated with compiled testing and hospitalization
data in a U.S. metropolitan area over ~10 weeks, corresponding to a
first wave of SARS-CoV-2 infection.
When municipal raw wastewater discharges into treatment facilities,
solids are settled and collected into a matrix called primary
sewage sludge. We chose to analyze primary sludge rather than raw
1-Mar 31-Mar 30-Apr 30-May
0
10
20
30
40
Hospitaladmissions
1-Mar 31-Mar 30-Apr 30-May
101
Hospitaladmissions
1-Mar 31-Mar 30-Apr 30-May
100
101
102
Positivetestsbydatereported
1-Mar 31-Mar 30-Apr 30-May
0
25
50
75
100
125
150
Positivetestsbydatereported
1-Mar 31-Mar 30-Apr 30-May
103
104
105
106
VirusRNA
1-Mar 31-Mar 30-Apr 30-May
0
100,000
200,000
300,000
400,000
500,000
VirusRNA
N1
N2
1-Mar 31-Mar 30-Apr 30-May
101
102
Positivetestsbydateof
specimencollection
1-Mar 31-Mar 30-Apr 30-May
0
20
40
60
80
Percentagepositivetestsby
dateofspecimencollection
1-Mar 31-Mar 30-Apr 30-May
101
102
Percentagepositivetestsby
dateofspecimencollection
1-Mar 31-Mar 30-Apr 30-May
0
50
100
150
Positivetestsbydateof
specimencollection
a b
c d
e f
g h
i j
Fig. 2 | Sludge SARS-CoV-2 RNA concentration time course and other COVID-19 outbreak indicators on linear (left) and log (right) scales. All data
represent the cities of New Haven, Hamden, East Haven and Woodbridge, Connecticut, which are served by the ESWPAF. The blue vertical dashed
lines indicate the first week of analysis, March 19–25, 2020. a,b, Number of positive SARS-CoV-2 test results, reported by date of specimen collection.
c,d, Percentage of positive SARS-CoV-2 test results, reported by date of specimen collection. e,f, Number of COVID-19 admissions to Yale New Haven
Hospital for residents of the four cities. g,h, Number of positive SARS-CoV-2 test results by public reporting date. i,j, Primary sludge SARS-CoV-2 RNA
concentration (virus RNA gene copies per ml of sludge).
Nature Biotechnology | VOL 38 | October 2020 | 1164–1167 | www.nature.com/naturebiotechnology 1165
Letters NAtUREBIOtECHnOlOgy
wastewater because it provides a high-solids-content, mixed sample
that has been shown to contain a broad diversity of human viruses,
including commonly circulating coronavirus strains13
. During
the COVID-19 outbreak, from March 19, 2020, to June 1, 2020,
in the New Haven, Connecticut, metropolitan area, we collected
daily primary sludge samples from the wastewater treatment facility,
which serves ~200,000 residents. We quantitatively compared
SARS-CoV-2 RNA concentrations in sludge with publicly reported
data on four other measures of the outbreak: SARS-CoV-2 positive
test results by date of specimen collection; the percentage of positive
SARS-CoV-2 test results (test positivity) by date of specimen
collection; the number of local hospital admissions of patients with
COVID-19; and SARS-CoV-2 positive test results by reporting date.
We measured SARS-CoV-2 virus RNA by quantitative reverse
transcription polymerase chain reaction (qRT–PCR) using the same
N1 and N2 primer sets employed in COVID-19 individual testing.
Virus RNA copies ranged from 1.7 × 103
ml−1
to 4.6 × 105
ml−1
of
primary sludge. All qRT–PCR concentration threshold (Ct) values
–2 0 2 4 6 8
–0.1
0.0
0.1
0.2
0.3
Days
Beta
–2 0 2 4 6 8
–0.5
0.0
0.5
1.0
Days
Cumulativebeta
–2 0 2 4 6 8
0.0
0.5
1.0
Days
Cumulativebeta
–2 0 2 4 6 8
–0.1
0.0
0.1
0.2
0.3
Days
Beta
–2 0 2 4 6 8
0.0
0.2
0.4
0.6
Days
Cumulativebeta
–2 0 2 4 6 8
–0.1
0.0
0.1
0.2
0.3
Days
Beta
–2 0 2 4 6 8
0.0
0.5
1.0
Days
Cumulativebeta
–2 0 2 4 6 8
–0.1
0.0
0.1
0.2
0.3
Days
Beta
a b
c d
e f
g h
Fig. 3 | Estimated daily distributed lag parameters describing the association between viral RNA in sludge and COVID-19 epidemiological parameters.
a, Daily lags of 0–2 d and leads of 1 d are associated with the number of positive tests based on specimen collection date. b, Cumulative relationship
between viral RNA in sludge and the number of positive tests based on specimen collection date. c, Daily lags of 0–2 d and leads of 1 d are associated
with the percentage of positive tests based on specimen collection date. d, Cumulative relationship between viral RNA in sludge and the percentage of
positive tests based on specimen collection date. e, Daily lags of 1–4 d are associated with hospitalization. f, Cumulative relationship between viral RNA in
sludge and hospital admissions. g, Daily lags of sludge virus RNA data at longer time lags (6–8 d in the past) best correlate with the time series of publicly
reported positive tests. h, Cumulative beta relationship between viral RNA in sludge and reported number of positive tests. Posterior means at the center
of each data point and 90% credible intervals for error bars are displayed. For each lag, n = 75 daily values for positive tests by date of specimen collection
(a,b), 75 daily values for percentage of positive tests by date of specimen collection (c,d), 75 daily values for hospital admission (e,f) and 75 daily values
for publicly reported positive tests (g,h).
Nature Biotechnology | VOL 38 | October 2020 | 1164–1167 | www.nature.com/naturebiotechnology1166
LettersNAtUREBIOtECHnOlOgy
were below 40, and 97% of all samples had a Ct value less than 38.
The average Ct was 34.6 for N1 primers and 34.5 for N2 primers.
Values for each replicate were reported as positive only when the
human ribonuclease P (RP) internal control gene was positive. The
average (s.d.) Ct value for the RP gene for all positive samples was
36.2 (1.2) for replicate 1 and 36.2 (1.3) for replicate 2. Replicated
samples demonstrated similar SARS-CoV-2 RNA concentration
values (Fig. 1). Concentration comparisons between replicates produced
slopes of 0.99 (R2
 = 0.75) for N1 primers and 0.97 (R2
 = 0.62)
for N2 primers.
All five measures traced the rise and fall of SARS-CoV-2
infections during the more than 10-week period studied (Fig. 2).
However, the sludge results showed an increase during the first week
(March 19–25, 2020) that was not observed in the reported testing
or hospital admissions data. Applying a distributed lag measurement
error time series model allowed an estimation of relationships
between viral time series results and the reported testing and hospital
admissions data. By modeling the epidemiological time series
as a function of the sludge SARS-CoV-2 RNA data across multiple
daily lags (posterior means ± 90% credible intervals), we found that
the sludge results led the number of positive tests by date of specimen
collection by 0–2 d, with a potential lag of 1 d (Fig. 3a,b); the
percentage of positive tests by date of specimen collection by 0–2 d,
with a potential lag of 1 d (Fig. 3c,d); hospital admissions by 1–4 d
(Fig. 3e,f); and the number of positive tests by report date by 6–8
d (Fig. 3g,h). Performing the time series analysis with or without
adjustment for testing volume did not result in differences in estimated
lag times between sludge viral RNA results and number of
positive tests (based on the above date of specimen collection results
and date reported to the Connecticut State Department of Public
Health) (Extended Data Fig. 1).
Overall, our results demonstrate that measurement of SARSCoV-2
RNA concentrations in primary sludge provides an approach
to estimate changes in COVID-19 prevalence on a population level.
Sludge results were not a leading indicator compared to positive
test results or percentage of positive tests by date of specimen collection.
However, they led hospitalizations by 1–4 d and test results
by report date by ~1 week. Thus, in communities where test reporting
is delayed, sludge results, if analyzed and reported on the same
day as sampling, can provide substantial advance notice of infection
dynamics. In locations with rapid reporting of SARS-CoV-2 test
results, the lead time afforded by sewage surveillance might be significantly
reduced. The lags in test reporting have multiple causes
and might vary with societal responses to the pandemic. COVID-19
arrived in the New Haven metropolitan area in early March 2020,
when testing capacity was limited, and there were extended waiting
times from test date to reporting date. Understanding and mitigating
the causes of such lags will require additional research.
Sludge data are also susceptible to variability for multiple reasons.
For example, primary sludge handling approaches are specific
to particular treatment plants and could affect the levels of detectable
virus. Given the uncertainties in sludge data and epidemiological
data, we did not attempt to correlate absolute numbers of sludge
SARS-CoV-2 RNA concentrations and COVID-19 cases.
Monitoring primary sludge is a broadly applicable strategy.
Wastewater treatment plants with primary and secondary treatment
are standard in many regions of the world, and treatment facilities
are rapidly expanding in urban areas of lower- and middle-income
countries14
. In the United States, approximately 16,000 treatment
plants serve more than 250,000,000 people. In regions without primary
wastewater treatment, monitoring of raw wastewater streams
would be necessary. Our results indicate that jurisdictions can use
primary sludge SARS-CoV-2 concentrations as an additional basis
for imposing or easing infection-control restrictions, especially in
locations affected by limits in clinical testing capacity or delays in
test reporting.
Online content
Any methods, additional references, Nature Research reporting
summaries, source data, extended data, supplementary information,
acknowledgements, peer review information; details of
author contributions and competing interests; and statements of
data and code availability are available at https://doi.org/10.1038/
s41587-020-0684-z.
Received: 17 May 2020; Accepted: 28 August 2020;
Published online: 18 September 2020
References
	1.	 Dong, E., Du, H. & Gardner, L. An interactive web-based dashboard to track
COVID-19 in real time. Lancet Infect. Dis. 20, 533–534 (2020).
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asymptomatic proportion of coronavirus disease 2019 (COVID-19)
cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020.
Euro. Surveill. 25, 2000180 (2020).
	3.	 García-Basteiro, A. L. et al. Monitoring the COVID-19 epidemic
in the context of widespread local transmission. Lancet Respir. Med. 8,
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	4.	 Lauer, S. A. et al. The incubation period of coronavirus disease 2019
(COVID-19) from publicly reported confirmed cases: estimation and
application. Ann. Intern. Med. 172, 577–582 (2020).
	5.	 Pöyry, T., Stenvik, M. & Hovi, T. Viruses in sewage waters during and after a
poliomyelitis outbreak and subsequent nationwide oral poliovirus vaccination
campaign in Finland. Appl. Environ. Microbiol. 54, 371–374 (1988).
	6.	 Berchenko, Y. et al. Estimation of polio infection prevalence from
environmental surveillance data. Sci. Transl. Med. 9, eaaf6786 (2017).
	7.	 Wang, W. et al. Detection of SARS-CoV-2 in different types of clinical
specimens. JAMA 323, 1843–1844 (2020).
	8.	 Xu, Y. et al. Characteristics of pediatric SARS-CoV-2 infection and potential
evidence for persistent fecal viral shedding. Nat. Med. 26, 502–505 (2020).
	9.	 Zhang, J., Wang, S. & Xue, Y. Fecal specimen diagnosis 2019 novel
coronavirus-infected pneumonia. J. Med. Virol. 92, 680–682 (2020).
	10.	Ahmed, W. et al. First confirmed detection of SARS-CoV-2 in untreated
wastewater in Australia: a proof of concept for the wastewater surveillance of
COVID-19 in the community. Sci. Total Environ. 728, 138764 (2020).
	11.	Medema, G., Heijnen, L., Elsinga, G., Italiaander, R. & Brouwer, A. Presence
of SARS-coronavirus-2 RNA in sewage and correlation with reported
COVID-19 prevalence in the early stage of the epidemic in The Netherlands.
Environ. Sci. Technol. Lett. 7, 511–516 (2020).
	12.	Wu, F. et al. SARS-CoV-2 titers in wastewater are higher than expected from
clinically confirmed cases. mSystems 5, e00614–e00620 (2020).
	13.	Bibby, K. & Peccia, J. Identification of viral pathogen diversity
in sewage sludge by metagenome analysis. Environ. Sci. Technol. 47,
1945–1951 (2013).
	14.	Zhang, Q. H. et al. Current status of urban wastewater treatment plants in
China. Environ. Int. 92-93, 11–22 (2016).
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
© The Author(s), under exclusive licence to Springer Nature America, Inc. 2020
Nature Biotechnology | VOL 38 | October 2020 | 1164–1167 | www.nature.com/naturebiotechnology 1167
Letters NAtUREBIOtECHnOlOgy
Methods
Sample collection. Primary sewage sludge (40 ml) was collected from the East
Shore Water Pollution Abatement Facility (ESWPAF) in New Haven, Connecticut,
USA. A total of 73 samples were taken daily from March 19, 2020, to June 1, 2020,
between 8:00 and 10:00 EDT, and stored at −80 °C before analysis (samples were
not available on May 3 and 6). The first sampling dates were before widespread
testing in the region and before the March 23, 2020, stay-at-home restrictions
implemented throughout the State of Connecticut. From the sampling start and
end dates, cities served by the ESWPAF experienced an increase in confirmed
COVID-19 cases (by testing) from seven cases to 3,978 (ref. 15
). The plant serves an
estimated population of 200,000 people with average treated flows of 1.75 m3
s−1
.
Sludge collected from ESWPAF is primary sludge, sampled at the outlet of a gravity
thickener, ranging in solids content from 2.6% to 5%. The solids residence time in
the gravity thickener is 4 h.
Viral RNA quantitative testing. To quantify SARS-CoV-2 RNA concentrations
in primary sludge, 2.5 ml of well-mixed sludge samples were added directly
to a commercial kit optimized for isolation of total RNA from soil (RNeasey
PowerSoil Total RNA Kit, Qiagen). Two replicate RNA extractions and analyses
were performed for one daily primary sludge sample. Isolated RNA pellets were
dissolved in 50 μl of ribonuclease-free water, and total RNA was measured by
spectrophotometry (NanoDrop, Thermo Fisher Scientific). SARS-CoV-2
RNA was quantified by one-step qRT–PCR using the U.S. Centers for Disease
Control and Prevention (CDC) N1 and N2 primers sets16,17
. For control and in
accordance with the CDC protocol, analysis was also conducted for the human
RP gene17
, and SARS-CoV-2 results were reported only if RP detection was
positive. Samples were analyzed using the Bio-Rad iTaq Universal Probes One-Step
Kit in 20-µl reactions run at 50 °C for 10 min and 95 °C for 1 min, followed
by 40 cycles of 95 °C for 10 s and 60 °C for 30 s per the manufacturer’s
recommendations. SARS-CoV-2 RNA concentrations were determined using
a standard curve as previously described17
and presented as virus RNA copies.
For the standard curve, complementary DNA synthesized from full-length
SARS-CoV-2 RNA (WA1-USA strain) was used as a template to generate SARS
CoV-2 N gene transcripts as previously described17
. The N gene was amplified,
and the PCR amplicon was purified and used for template in the MEGAscript T7
Kit (Thermo Fisher Scientific) to generate single-stranded RNA transcripts.
RNA was quantified on a Qubit fluorimeter (Thermo Fisher Scientific) and
integrity verified on a Bioanalyzer 2100 (Agilent). Viral RNA copies were
calculated, and serial ten-fold dilutions were made. To validate our N1 and N2
primers sets, standard curves using the ten-fold series dilution (5 × 101
to 5 × 108
copies per reaction) of the N gene transcripts were analyzed. The N1 primer set
generated a standard curve with an R2
value of 0.98 with an efficiency of 94.1%
(slope = −3.473; y intercept = 42.266). The N2 primer set generated a standard
curve with an R2
value of 0.99 with an efficiency of 88.5% (slope = −3.632;
y intercept = 42.528).
The SARS-CoV-2 concentration results were adjusted to the total RNA
extracted by multiplying sample concentrations by the ratio of the maximum
RNA concentration to the sample RNA concentration. This accounts for
day-to-day variations in sludge solids content and RNA extraction efficiency.
To determine whether sludge RNA extracts contained PCR inhibition, target
RNA was spiked into three separate sterile, ribonuclease-free water samples
(no inhibition) and five different sludge RNA extracts from samples collected
at a time in the outbreak when cases were low and viral RNA was not detected
with N1 primers. Spiked samples were then diluted 5× and 25×, and sludge
RNA Ct values were compared to water RNA Ct values using N1 primers.
No differences were observed for average water Ct values and sludge extract
Ct vales for the no dilution (P = 0.14), 5× dilution (P = 0.51) and 25× dilution
(P = 0.23), two-tailed t-test, suggesting no PCR inhibition in the RNA extracts.
All samples were diluted 5× for use as a template to ensure that qRT–PCR
inhibition occurred. Sewage sludge from March 2018 was used as a control,
and no SARS-CoV-2 detection was observed from either N1 or N2 primers.
These control sludges were stored at −80 °C and were consistently positive
for the human RP gene. Positive RNA controls and no-template controls
were included in all qRT–PCR runs. Appropriate Ct values were observed
for all positive controls, and no amplification was observed in negative
template controls.
Epidemiological data. Daily COVID-19 admissions to the Yale New Haven
Hospital were compiled from hospital records—adjusted to include only New
Haven, East Haven, Hamden and Woodbridge, Connecticut residents—and
confirmed by laboratory testing. Hospital data were obtained from the Joint
Data Analytics Team for the New Haven Health System. The total number and
percentage of tests of residents from the four cities that were positive for
COVID-19 and reported by date of specimen collection and date of reporting to
the State of Connecticut were supplied through a data request to the Connecticut
Department of Public Health. Numbers of laboratory-confirmed positive
COVID-19 tests by report date in the towns served by the ESWPAF (New Haven,
East Haven, Hamden and Woodbridge, Connecticut) were compiled from daily
reports published by the Connecticut Department of Public Health15
.
Statistics. Linear regressions were used to estimate the relationship between
SARS-CoV-2 RNA copies per ml results for replicated RNA extractions of each
daily sample (n = 73 for each PCR primer). Two-tailed t-tests (α = 0.05) were used
in PCR inhibition experiments to determine if spiked sludge RNA extracts resulted
in the same Ct values as spiked water samples at no dilution, 5× dilution and
25× dilution, n = 6 for water spiked samples and n = 15 for sludge spiked samples
for each dilution.
Estimation of primary sludge as a potential leading indicator was performed
using a distributed lag measurement error time series model. This analysis was
carried out in the Bayesian framework, allowing us to correctly characterize
multiple sources of uncertainty when estimating the lagged associations of interest.
In the analyses, we assume that the observed sludge testing data represent unbiased
estimates of an underlying, unobserved trajectory of viral concentration in the
sludge. We then evaluated the association between the underlying trajectory of
viral concentration in the sludge at multiple lagged periods and the number of
positive tests (based on date of specimen collection)/percentage of positive tests
based on date of specimen collection)/hospitalizations/number of positive tests
(based on date of report) using distributed lag Poisson regression models that
included a random effect to account for overdispersion and autocorrelation in
the outcome, n = 73 for sludge virus RNA samples, n = 75 daily positive tests by
date of specimen collection, n = 75 daily percentage of positive tests reported by
date of specimen collection, n = 75 daily reported hospitalizations and n = 75 daily
reported positive tests. The distributed lag regression parameters were modeled
using a random walk process. The models are fit using the rjags package in R18
.
Mathematical models include the following:
Model for case counts:
Ytjλt  Poisson λtð Þ; t ¼ d þ 1; ¼ ; n � 1
ln λtð Þ ¼ Ot þ β0 þ
Xd
j¼�1
βjxt�j þ ϕt
ϕtjα; ϕt�1; σ2
ϕ  Normal αϕt�1; σ2
ϕ
 
; ϕ0  0
Model for primary sludge:
Wtj  Normal xt; σ2
ϵ
À Á
; j ¼ 1; ¼ ; m
xt  Normal 0; 1002
À Á
Prior distributions:
β0; μ  Normalð0; 1002
Þ
βjjβj�1; σ2
β  Normal βj�1; σ2
β
 
; j ¼ 2; ¼ ; d
β1  Normalð0; 1002
Þ
σϕ; σϵ; σβ  Uniformð0; 100Þ
α  Uniformð0; 1Þ
where d is the number of past lags included in the model, n is the total number of
days of available data, m is the number of primary sludge replicates on a given day,
Yt is the case count on day t, Ot is the offset on day t (number of tests performed on
each day for the analysis of cases by test date or 0 otherwise), Wtj is the measured
concentration of virus in sludge from sample replicate j on day t, and xt is the
unobserved true concentration of virus in sludge on day t.
Reporting Summary. Further information on research design is available in the
Nature Research Reporting Summary linked to this article.
Data availability
The epidemiological data used in this study are provided as Source Data to Fig. 2.
All sludge data are available upon request.
References
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Department of Health https://data.ct.gov/Health-and-Human-Services/
COVID-19-confirmed-cases-by-town-/28fr-iqnx (2020).
	16.	U.S. Centers for Disease Control and Prevention. CDC 2019-Novel
Coronavirus (2019-nCoV) Real-Time RT–PCR Diagnostic Panel https://www.
fda.gov/media/134922/download?fbclid=IwAR1DdEweazD3ixmrpZMc07V
XM0_n1qx455rGV7E0fAEcA1QZf3Peh0Qxypo (2020).
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	17.	Vogels, C. B. F. et al. Analytical sensitivity and efficiency comparisons of
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Statistical Comptuting (DSC, 2003).
Acknowledgements
We wish to thank the Greater New Haven Water Pollution Control Authority,
New Haven, Connecticut, USA, for primary sludge sampling assistance. A.Z. is
supported by a CoReCT seed grant from the Yale School of Medicine and the Yale
Institute of Global Health. N.D.G. is supported by a gift from the Huffman Family
Donor Advised Fund. A.K. and A.C.-M. are supported by the Beatrice Kleinberg
Neuwirth Fund, the Yale School of Public Health and the National Institutes of
Health (R01 AI121207). The authors thank K. Yousey-Hindes and P. Clogher for
providing the data on percentage of positive tests.
Author contributions
J.P., A.Z., D.E.B., N.D.G., D.W., M.W. and W.A. contributed laboratory analysis
and sample collection. J.P., E.H.K., A.C.-M., A.I.K., A.A.M., J.L.W., D.M.W. and
S.B.O. contributed epidemiological analysis. D.M.W. and J.L.W. produced the
distributed lag analysis.
Competing interests
The authors declare no competing interests.
Additional information
Extended data is available for this paper at https://doi.org/10.1038/s41587-020-0684-z.
Supplementary information is available for this paper at https://doi.org/10.1038/
s41587-020-0684-z.
Correspondence and requests for materials should be addressed to J.P. or S.B.O.
Reprints and permissions information is available at www.nature.com/reprints.
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Letters NAtUREBIOtECHnOlOgy
Extended Data Fig. 1 | Estimated daily distributed lag parameters describing the association between viral RNA in sludge and positive COVID-19 tests,
based on date of report to CT Department of Health. The top row figures are adjusted for testing volume, bottom row figures are unadjusted for testing
volume. a, daily lags of sludge data at longer time lags (0 to 4 days in the past) best correlate with the time series of reported positive tests (adjusted).
b, cumulative relationship between viral RNA in sludge and number of positive tests (adjusted). c, daily lags of sludge data at longer time lags (0 to 4 days
in the past) best correlate with the time series of reported cases (not adjusted for number of tests). d, cumulative relationship between viral RNA in sludge
and number of positive tests (not adjusted). The posterior means at the center of each data point and 90% credible intervals for error bars are displayed.
For each lag, n=73 daily virus RNA samples, n=75 daily reported adjusted positive test values, n=75 daily reported unadjusted positive test values.
Nature Biotechnology | www.nature.com/naturebiotechnology
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natureresearch|reportingsummaryApril2020
Corresponding author(s): Jordan Peccia
Last updated by author(s): 2020-7-16
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COVID-19 new case data were obtained from publicly available data at the Connecticut Department of Public Health <https://data.ct.gov/stories/s/COVID-19-data/
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Study description This study determined SARS-CoV-2 RNA concentrations in primary sewage sludge. These concentrations were taken each day for 73
days, during a COVID-19 outbreak. The time course of virus RNA in primary sewage was quantitatively compared to community
epidemiological curves (based on testing) and hospital admission data and we showed that virus RNA concentrations were a leading
indicator of outbreak dynamics.
Research sample The research sample was primary sludge from a municipal wastewater treatment facility in New Haven, CT. Primary sludge was
produced through primary sedimentation and gravity thickening.
Sampling strategy Samples were collected leading up to the COVID-19 outbreak in New Haven, CT and ended as the outbreak had ended long after the
outbreak ended (continued reduction in cases for `several weeks). Samples were collected daily.
Data collection AZ, WA and JP extracted RNA and processed sludge, DB and NG conducted qRT-PCR. JP built data spreadsheets. EK, JW, DW led
epidemiology analysis.
Timing and spatial scale March 19 (before COVID-19 outbreak) to June 1 (near end of COVID-19 outbreak), samples daily for 75 days (samples were not
available for 2 days in May).
Data exclusions Data (less than 5% with negative RP control genes were declared invalid in accordance with CDC prototocol
Reproducibility Replicate RNA extractions were conducted on daily sludge samples, in addition two independent PCR primer sets were used for all
analyses. Replicate RNA extractions were plotted to observe and quantify replicate relationships (Figure 1) .
Randomization sample RNA extraction and analysis were performed in batches of 10 (5 samples, 2 replicates each). Dates were randomly chosen for
each batch.
Blinding Sampling at the gravity thickener was performed at the treatment facility employee, most RNA extraction performed by AZ, qRT-PCR
performed by DB without knowledge of the sample dates or other information on expected content of each sample.
Did the study involve field work? Yes No
Field work, collection and transport
Field conditions Wastewater treatment facility during the Spring of 2020, New Haven, CT.
Location New Haven, CT, USA East Shore Water Pollution Abatement Facility
Access & import/export Permission obtained from facility manager and operator
Disturbance none
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natureresearch|reportingsummaryApril2020
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