Preprint available at Preprints with THE LANCET
Background: Wastewater-based epidemiological surveillance at municipal wastewater treatment plants has proven to play an important role in COVID-19 surveillance. Since, international passenger hubs contribute extensively to global transmission of viruses, wastewater surveillance at this type of location may be of added value as well. The aim of this study is to explore the potential of long-term wastewater surveillance at a large passenger hub as an additional tool for public health surveillance during different stages of a pandemic.
Methods: Here, we present an analysis of SARS-CoV-2 viral loads in airport wastewater by reverse-transcription quantitative polymerase chain reaction (RT-qPCR) from the beginning of the COVID-19 pandemic in Feb 2020, and an analysis of SARS-CoV-2 variants by whole-genome next-generation sequencing from Sep 2020, both until Sep 2022, in the Netherlands. Results are contextualized using (inter)national measures and data sources such as passenger numbers, clinical surveillance data and national wastewater surveillance data.
Findings: Our findings show that wastewater surveillance was possible throughout the study period, irrespective of measures. Viral loads rose simultaneously with the emergence of new variants. Furthermore, trends in viral load and variant detection in airport wastewater closely followed, and in some cases preceded, trends in national daily average viral load in wastewater and variants detected in clinical surveillance.
Interpretation: Wastewater-based epidemiology at a large international airport is a valuable addition to classical COVID-19 surveillance and the developed expertise can be applied in pandemic preparedness plans for other (emerging) pathogens in the future.
This repository contains the code required to reproduce the plots in the preprint: Long-term wastewater monitoring of SARS-CoV-2 viral loads and variants at the major international passenger hub Amsterdam Schiphol Airport: a valuable addition to COVID-19 surveillance.
The code in this repository has been tested with R 4.2.2. with Tidyverse v2.0.0. Packages required are listed in scripts/supplementary_functions.R. The figures can be produced by running the run_analysis.R script, which automatically sources other scripts and gathers data from online databases and the Zenodo data set associated with the preprint, available under the Creative Commons Attribution 4.0 License.
A list of external data used for plotting is given below.
- https://data.rivm.nl/covid-19/COVID-19_varianten.csv: This data is made available by the Kiemsurveillance RIVM under the CC BY 4.0 Licence.
- https://data.rivm.nl/covid-19/COVID-19_rioolwaterdata_landelijk.csv: This data is made available by the Waterschappen, CBS & RIVM under the Public Domain Mark 1.0 Licence.
- https://raw.githubusercontent.com/cov-lineages/pango-designation/master/pango_designation/alias_key.json: This data part of the Pango-Designation GitHub repository made available under the CC BY-NC 4.0 Licence.
To include the hospitalization data in figure 3, please download the weekly hospital admission covid per million data from Our World in Data, and supply the .csv table to the figure_3.R script using the -a option. This data is made available by Our World in Data under the CC BY Licence.
Auke Haver & Anne-Merel van der Drift
- Anne-Merel van der Drift
- Auke Haver
- Astrid Kloosterman
- Ruud van der Beek
- Erwin Nagelkerke
- Jeroen Laros
- Consortium NRS
- Jaap van Dissel
- Ana Maria de Roda Husman
- Willemijn Lodder
This research was funded by the Dutch Ministry of Health, Welfare and Sport as part of the Dutch National Sewage Surveillance program.