Predicting the number of people infected with SARS-COV-2 in a population using statistical models based on wastewater viral load

TLDR: In this paper, statistical regression models from the viral load detected in the wastewater and the epidemiological data from A Coruna health system that allowed us to estimate the number of infected people, including symptomatic and asymptomatic individuals, with reliability close to 90%, were developed.

Abstract: The quantification of the SARS-CoV-2 RNA load in wastewater has emerged as a useful tool to monitor COVID-19 outbreaks in the community. This approach was implemented in the metropolitan area of A Coruna (NW Spain), where wastewater from a treatment plant was analyzed to track the epidemic dynamics in a population of 369,098 inhabitants. Statistical regression models from the viral load detected in the wastewater and the epidemiological data from A Coruna health system that allowed us to estimate the number of infected people, including symptomatic and asymptomatic individuals, with reliability close to 90%, were developed. These models can help to understand the real magnitude of the epidemic in a population at any given time and can be used as an effective early warning tool for predicting outbreaks. The methodology of the present work could be used to develop a similar wastewater-based epidemiological model to track the evolution of the COVID-19 epidemic anywhere in the world.

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Highly predictive regression model of active cases
of COVID-19 in a population by screening
wastewater viral load
Juan Vallejo
Department of Microbiology, University Hospital A Coruña (CHUAC) & Biomedical Research Institute A
Coruña (INIBIC)
Soraya Rumbo-Feal
Biomedical Research Institute of A Coruna https://orcid.org/0000-0002-1796-1815
Kelly Conde
Biomedical Research Institute of A Coruna
Ángel López-Oriona
Research Center for Information and Communication Technologies (CITIC), University of A Coruña
Javier Tarrío (  javier.tarrio@udc.es )
Research Center for Information and Communication Technologies (CITIC), University of A Coruña
Rubén Reif (  ruben.reif@udc.es )
Advanced Scientic Research Center (CICA), University of A Coruña https://orcid.org/0000-0002-1035-
4254
Susana Ladra
Research Center for Information and Communication Technologies (CITIC), University of A Coruña
https://orcid.org/0000-0003-4616-0774
Bruno Rodiño-Janeiro
University of Vienna, Austria
Mohammed Nasser
Biomedical Research Institute of A Coruna
Angeles Cid
University of A Coruña
Maria Veiga
Laboratory of Chemical Engineering, Faculty of Sciences and Advanced Scientic Research Center
(CICA), University of A Coruña, Spain https://orcid.org/0000-0002-5275-5179
Antón Acevedo
University Hospital of A Coruña
Carlos Lamora
EDAR Bens
Germán Bou

Department of Microbiology, University Hospital A Coruña (CHUAC) & Biomedical Research Institute A
Coruña (INIBIC)
https://orcid.org/0000-0001-8837-0062
Ricardo Cao (  ricardo.cao@udc.es )
Research Center for Information and Communication Technologies (CITIC), University of A Coruña
Margarita Poza (  margapoza@gmail.com )
Biomedical Research Institute of A Coruna-University of A Coruna https://orcid.org/0000-0001-9423-
7268
Article
Keywords: SARS-CoV-2, COVID-19, surveillance, wastewater-based epidemiology, wastewater treatment
plant, hospital wastewater, viral load, sewage, outbreaks, imputation, generalized additive models (GAM),
kernel smoothing, LOESS, local polynomial regression
Posted Date: July 15th, 2020
DOI: https://doi.org/10.21203/rs.3.rs-39911/v1
License:   This work is licensed under a Creative Commons Attribution 4.0 International License. 
Read Full License

1
Original article for NATURE COMMUNICATIONS
Highly predictive regression model of active cases of COVID-19 in a population by
screening wastewater viral load
Juan A. Vallejo
1
*, Soraya Rumbo-Feal
1
*, Kelly Conde-Pérez
1
*, Ángel López-Oriona
2
*,
Javier Tarrío
2#
, Rubén Reif
3#
, Susana Ladra
4
, Bruno K. Rodiño-Janeiro
5
, Mohammed
Nasser
1
, Ángeles Cid
6,3
, María C Veiga
3
, Antón Acevedo
7
, Carlos Lamora
8
, Germán
Bou
1
, Ricardo Cao
2,9#
and Margarita Poza
1,6#
*Authors contributed equally
#
Authors for corresponding
1
Microbiology Research Group, University Hospital Complex (CHUAC) - Institute of
Biomedical Research (INIBIC), University of A Coruña (UDC), A Coruña, Spain.
2
Research Group MODES, Research Center for Information and Communication
Technologies (CITIC), University of A Coruña, Spain.
3
Advanced Scientific Research Center (CICA), University of A Coruña, Spain.
4
Database Laboratory, Research Center for Information and Communication
Technologies (CITIC), University of A Coruña, Spain.
5
Division of Microbial Ecology, Department for Microbiology and Ecosystem Science,
University of Vienna, Austria.
6
Department of Biology, University of A Coruña, Spain.
7
Medical Management Department, University Hospital Complex of A Coruña
(CHUAC), Spain.
8
Public wastewater treatment plant company EDAR Bens, S.A., A Coruña, Spain.
9
Technological Institute for Industrial Mathematics (ITMATI), Universities of A
Coruña, Santiago de Compostela and Vigo, Spain

2
ABSTRACT (150 words)
The quantification of the SARS-CoV-2 load in wastewater has emerged as a useful
method to monitor COVID-19 outbreaks in the community. This approach was
implemented in the metropolitan area of A Coruña (NW Spain), where wastewater from
the treatment plant of Bens was analyzed to track the epidemic’s dynamic in a population
of 369,098 inhabitants. We developed statistical regression models that allowed us to
estimate the number of infected people from the viral load detected in the wastewater
with a reliability close to 90%. This is the first wastewater-based epidemiological model
that could potentially be adapted to track the evolution of the COVID-19 epidemic
anywhere in the world, monitoring both symptomatic and asymptomatic individuals. It
can help to understand with a high degree of reliability the true magnitude of the epidemic
in a place at any given time and can be used as an effective early warning tool for
predicting outbreaks.
RUNNING TITLE: SARS-CoV-2 reliable surveillance on sewage
KEYWORDS:
SARS-CoV-2, COVID-19, surveillance, wastewater-based epidemiology, wastewater
treatment plant, hospital wastewater, viral load, sewage, outbreaks, imputation,
generalized additive models (GAM), kernel smoothing, LOESS, local polynomial
regression.
INTRODUCTION
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel member of
the Coronaviridae family and is the pathogen responsible for coronavirus disease 2019
(COVID-19), which has led to a worldwide pandemic. Patients may present with a wide
variety of symptoms and the prognosis ranges from mild or moderate disease, to severe
disease and death
1
. Importantly, a significant percentage of those infected are
asymptomatic, with studies finding that 20% to over 40% of cases show no symptoms
2-4
,
a condition that helps the silent spread of the disease. SARS-CoV-2 is an enveloped virus
with a nucleocapsid made up of single-stranded RNA bound to protein N (Nucleocapsid),
surrounded by a lipid membrane that contains structural proteins M (Membrane), E

3
(Envelop) and S (Spike)
5-8
. The structure of protein S gives the virus its distinctive crown
of spikes and is responsible for binding to the angiotensin-converting enzyme 2 (ACE2)
receptor, which allows the virus to enter the host cell
9,10
. ACE2 receptors are present in a
range of human cell types, with particular abundance in respiratory and gastrointestinal
epithelial cells. In fact, an analysis of ACE2 receptor distribution in human tissues found
the highest levels of expression in the small intestine
11
. Although respiratory symptoms
are the most frequently described in patients with COVID-19, several studies have shown
that the gastrointestinal tract can also be affected by SARS-CoV-2. A meta-analysis found
that 15% of patients had gastrointestinal symptoms and that around 10% of patients
presented gastrointestinal symptoms but not respiratory symptoms
12
. Conversely, SARS-
CoV-2 RNA has been found in the faeces of people without gastrointestinal symptoms
13-
18
. A systematic literature review found that more than half (53.9%) of those tested for
faecal viral RNA were positive, and noted that the virus is excreted in the stool for long
periods, in some cases a month or more after the individual has tested negative for their
respiratory samples
15,19-24
. The fact that the virus can grow in enterocytes of human small
intestine organelles
25,26
and the discovery of infectious virus in faeces highlights the
potential for replication in the gastrointestinal epithelium of patients. However, despite
several studies suggesting that transmission of the virus could take place through the
faecal-oral axis
27,28
, there is so far insufficient evidence to confirm this method of
contagion
19,29
. Similarly, there has been no evidence of contagion through wastewater,
which might reflect SARS-CoV-2’s instability in water and its sensitivity to
disinfectants
9,29-34
. Viral RNA can, nonetheless, be found in wastewater
33
, which has
made monitoring of viral RNA load in sewage a promising tool for the epidemiological
tracking of the pandemic
35-40
.
Wastewater is a dynamic system that can reflect the circulation of microorganisms in the
population. Previous studies have evaluated the presence in wastewater of several viruses
41-45
. Processes to monitor SARS-CoV-2 in wastewater were first developed in the
Netherlands
35
, followed by the USA
46
, France
39
, Australia
36
, Italy
47
and Spain
37,48
. In the
Netherlands, no viral RNA was detected 3 weeks before the first case was reported, but
genetic material started to appear over time, as the number of cases of COVID-19
increased
35
. A wastewater plant in Massachusetts detected a higher viral RNA load than
expected based on the number of confirmed cases at that point, possibly reflecting viral
shedding of asymptomatic cases in the community
38
. In Paris, wastewater measurements

Frequently asked questions

Q1. What are the contributions mentioned in the paper "Highly predictive regression model of active cases of covid-19 in a population by screening wastewater viral load" ?

In this paper, the authors found that more than half ( 53.9 % ) of those tested for faecal viral RNA were positive, and noted that the virus is excreted in the stool for long periods, in some cases a month or more after the individual has tested negative for their respiratory samples. 

Q2. What was the way to estimate the viral load effect?

Since the nonparametric estimation of the viral load effect had a logarithmic shape, a multiple linear model was fitted using the logarithmic transformation of the viral load, daily flow, rainfall, temperature, and humidity. 

Q3. What is the importance of a linear regression model for estimating the viral load in the metropolitan?

The evolution of the viral load along the day is an important feature for selecting narrower sampling intervals when the viral load was low and difficult to detect. 

Q4. How was the prediction ability of the GAM and LOESS models evaluated?

The prediction ability of this fitted linear model, the GAM, and the linear and quadratic LOESS models has been evaluated using a 6-fold cross validation procedure, to prevent overfitting. 

Q5. How did the study measure the viral RNA load in sewage sludge?

A study from Yale University measured the concentration of SARS-CoV-2 RNA in sewage sludge and found that viral RNA concentrations were highest 3 days before peak hospital admissions of COVID-19 cases, and 7 days before peak community COVID-19 cases40. 

Q6. How many cases of COVID-19 were reported in the Netherlands?

In the Netherlands, no viral RNA was detected 3 weeks before the first case was reported, but genetic material started to appear over time, as the number of cases of COVID-19 increased 35. 

Q7. Why does the only precedent52 combine computational analysis and modelling with a theoretical approach?

The only precedent52 combines computational analysis and modelling with a theoretical approach in order to identify useful variables and confirm the feasibility and cost-effectiveness of WBE as a prediction tool. 

Q8. How many people in the WWTP Bens have symptoms?

This means that, for a population of about 369,098 inhabitants, the number of people infected with SARS-CoV-2 contributing their sewage into the WWTP Bens would be around 6,644, which includes people with symptoms and those who are asymptomatic. 

Q9. When can the series of estimated active cases be backcasted?

Since the number of active cases in the health area has been reported until June 5th, the series of estimated official active cases could be backcasted from May 8th until June 5th. 

Q10. What was the only assay with all (six) unmeasured replications?

In the only assay with all (six) unmeasured replications, the number of RNA copies was imputed using the minimum of measured viral load along the whole set of assays. 

Q11. How long does it take for the wastewater to reach the WWTP Bens?

The estimated time for the wastewater to reach the WWTP Bens along the network is between 1.5 h and 3 h, depending on the source in the metropolitan area. 

Q12. What is the way to predict outbreaks?

It can help to understand with a high degree of reliability the true magnitude of the epidemic in a place at any given time and can be used as an effective early warning tool for predicting outbreaks. 

Q13. What is the effect of the viral load on the real number of COVID-19 active cases?

The effect of the viral load in the real number of COVID-19 active cases showed a logarithmic shape (Figure 8A), which suggests that the number of COVID-19 active cases can be modelled linearly as a function of the logarithm of the viral load.