Utilizing river and wastewater as a SARS-CoV-2 surveillance tool in settings with limited formal sewage systems

Abstract

The COVID-19 pandemic has profoundly impacted health systems globally and robust surveillance has been critical for pandemic control, however not all countries can currently sustain community pathogen surveillance programs. Wastewater surveillance has proven valuable in high-income settings, but less is known about the utility of water surveillance of pathogens in low-income countries. Here we show how wastewater surveillance of SAR-CoV-2 can be used to identify temporal changes and help determine circulating variants quickly. In Malawi, a country with limited community-based COVID-19 testing capacity, we explore the utility of rivers and wastewater for SARS-CoV-2 surveillance. From May 2020-May 2022, we collect water from up to 112 river or defunct wastewater treatment plant sites, detecting SARS-CoV-2 in 8.3% of samples. Peak SARS-CoV-2 detection in water samples predate peaks in clinical cases. Sequencing of water samples identified the Beta, Delta, and Omicron variants, with Delta and Omicron detected well in advance of detection in patients. Our work highlights how wastewater can be used to detect emerging waves, identify variants of concern, and provide an early warning system in settings with no formal sewage systems.

Fig. 1. Temporal ES sampling in Blantyre Malawi.

A Sampling over time where each individual dot is one sample tested either negative (blue) or positive (red) for SARS-CoV-2. The left y-axis and black line are the Spline curve modeling peaks and valleys in detection based on the frequenting of positivity. The right y-axis is total of number of samples tested. Phase one is denoted with a green top bar and included 7 sites and phase 2 is denoted with a purple bar and includes up to 112 sites. Utilizing the full dataset from May 2020-May 2022 we analyzed collection sites based on their GPS coordinate. B Red dots denote sites with at least one positive sample overlaid on the population density of Blantyre based on HRSL data. C each sampling location is color coded by the overall percent positivity. D Hotspot analysis using Getis-Ord G_i* and E spatial cluster-outlier detection analysis using Anselin Local Moran’s I.

Fig. 2. Estimates of lag time between ES and active case surveillance.

Light blue represents the rolling average of ES positivity over time compared to active case surveillance numbers (bar graph in light orange) where the y-axis is total on number of positive cases per day. Spline comparison of both ES (dark blue) and active case surveillance prevalence (dark orange) show how closely linked peaks in both detection methods are over multiple waves.

Fig. 3. SARS-CoV-2 variant in wastewater identified key VOCs before observed in the patient population.

A Summary of VOC detected by month using Freyja and B the VOC sub-lineage by month, and C Omicron SNPs show putative early detection of the VOC. The heatmap shows all BA.1 Omicron SNPs on the y-axis (blue=BA.1-specific, green=BA.1/BA.2 shared mutation) and individual ES samples overtime on the x-axis where months are by color. In September samples have some key Omicron SNPs but lack the full repertoire of SNPs which become dominant by December.