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. 2022 Mar 1:810:152213.
doi: 10.1016/j.scitotenv.2021.152213. Epub 2021 Dec 9.

SARS-CoV-2 genome quantification in wastewaters at regional and city scale allows precise monitoring of the whole outbreaks dynamics and variants spreading in the population

S Wurtzer  1 , P Waldman  2 , M Levert  3 , N Cluzel  3 , J L Almayrac  4 , C Charpentier  5 , S Masnada  6 , M Gillon-Ritz  7 , J M Mouchel  2 , Y Maday  3 , M Boni  8 ; OBEPINE Consortium; AP-HP Virologist Group; V Marechal  9 , L Moulin  10
Affiliations

SARS-CoV-2 genome quantification in wastewaters at regional and city scale allows precise monitoring of the whole outbreaks dynamics and variants spreading in the population

S Wurtzer et al. Sci Total Environ. .

Abstract

SARS-CoV-2 is a coronavirus causing a globalized outbreak called COVID-19. SARS-CoV-2 transmission is associated with inhalation of contaminated respiratory droplets and could causes severe complications. Until today several "waves" of infections have been observed despite implementation of strict health policies. Decisions for such sanitary measures are based on population health monitoring. Unfortunately, for COVID-19, a significant proportion of individuals are asymptomatic but play a role in the virus transmission. To overcome these limitations, several strategies were developed including genome quantification in wastewater that could allow monitoring of the health status of population, since shedding of SARS-CoV-2 in patient stool is frequent. Wastewater-based epidemiology (WBE) was established and several countries implemented this approach to allow COVID-19 outbreak monitoring. In France, the OBEPINE project performed a quantitative analysis of SARS-CoV-2 in raw wastewater samples collected from major wastewater treatment plants (WWTP) since March 2020. In the greater Paris area 1101 samples (507 for five WWTP and 594 for sewer) were collected. This 16 months monitoring allows us to observe the outbreak dynamics. Comparison of WBE indicators with health data lead to several important observation; the good level of correlation with incidence rates, the average 3 days lead time, and the sensitivity (WBE change when incidence is > to 7/100000 inhabitants). We also compared the local monitoring (city level) with the regional monitoring, to help cluster identification. Moreover, variants of concern (VOC) emerged due to the selection pressure. We developed a specific RT-qPCR method targeting the deletion H69-V70 in the spike protein, using this deletion as a proxy of the B.1.1.7 presence in the wastewater. With this data we demonstrate the predominant role played by this strain in the third wave. All these results allow a better description and understanding of the pandemic and highlight the role of such WBE indicators.

Keywords: COVID-19; Epidemiology; Quantification; RT-qPCR; SARS-CoV-2; Variant monitoring; Wastewater.

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Conflict of interest statement

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Unlabelled Image
Graphical abstract
Fig. 1
Fig. 1
Average viral load in several WWTP from Ile de France global. Open red circles represent average concentration (1 to 5 WWTP) per day. Red line is the lowess curve resulting from the smoothing of the concentration values. Incidence data for the whole region (black curve) 7 day cumulated. Estimated concentration of genomes containing the del-69-70 mutation (green). Blue area represents the lockdown periods. Concentration ordinate are split in two to allow first wave representation.
Fig. 2
Fig. 2
SARS-CoV-2 viral concentration in 5 WWTP from Ile-de-France. Average daily (red open circles) and smoothed (Lowess) (red line) viral RNA concentrations are depicted as well as incidence at the level of the department (black line) The calculated WWI is represented in blue. Gray surface indicates lockdown periods. Each graph is a different WWTP.
Fig. 3
Fig. 3
Upper panel median value of the sewer measurement (open red circles), red line show the dynamics of the concentration (Lowess). Black line represents the incidence of the 7-days cases in the area. In gray lockdown period. Lower panel: heat map of each sample point SARS CoV 2 concentration, white line represent beginning and ending of the lockdown.
Fig. 4
Fig. 4
Relationship between log of the concentration of SARS-CoV-2 genome and the log number of incidence number. Panel A regional scale, in blue average of the 5 WWTP, in red Lowess of the same data. Panel B, in blue SEM WWTP, in red MAV. panel C relationship between SEV and departmental incidence number. Panel D relation between STV and the departmental incidence number panel E, relationship for SEC and Paris incidence panel F, linear regression of all WWTP and the regional average.
Fig. 5
Fig. 5
Panel A (upper), correlation between the WWI for each WWTP and 2nd and 3th wave. Panel B (lower) positive or negative lag (in day) between the WWI. Lag was calculated by maximizing the correlation between the two datasets. For both panel, correlation was calculated 1000 time using a subsample of 50% of the values. LSM mean STV.
Fig. 6
Fig. 6
Evolution of the average concentration of del 69-70 in the Greater Paris area (open blue circle) and representation of the dynamics by Lowess smoothing (blue line). In black 7 days rolling average sequence of variants in the Paris city (sub population of the Greater Paris, data SPF). In purple data from the Parisian hospital (data AP-HP).
See this image and copyright information in PMC

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