Computational analysis of SARS-CoV-2/COVID-19 surveillance by wastewater-based epidemiology locally and globally: Feasibility, economy, opportunities and challenges

Abstract

With the economic and practical limits of medical screening for SARS-CoV-2/COVID-19 coming sharply into focus worldwide, scientists are turning now to wastewater-based epidemiology (WBE) as a potential tool for assessing and managing the pandemic. We employed computational analysis and modeling to examine the feasibility, economy, opportunities and challenges of enumerating active coronavirus infections locally and globally using WBE. Depending on local conditions, detection in community wastewater of one symptomatic/asymptomatic infected case per 100 to 2,000,000 non-infected people is theoretically feasible, with some practical successes now being reported from around the world. Computer simulations for past, present and emerging epidemic hotspots (e.g., Wuhan, Milan, Madrid, New York City, Teheran, Seattle, Detroit and New Orleans) identified temperature, average in-sewer travel time and per-capita water use as key variables. WBE surveillance of populations is shown to be orders of magnitude cheaper and faster than clinical screening, yet cannot fully replace it. Cost savings worldwide for one-time national surveillance campaigns are estimated to be in the million to billion US dollar range (US$), depending on a nation's population size and number of testing rounds conducted. For resource poor regions and nations, WBE may represent the only viable means of effective surveillance. Important limitations of WBE rest with its inability to identify individuals and to pinpoint their specific locations. Not compensating for temperature effects renders WBE data vulnerable to severe under-/over-estimation of infected cases. Effective surveillance may be envisioned as a two-step process in which WBE serves to identify and enumerate infected cases, where after clinical testing then serves to identify infected individuals in WBE-revealed hotspots. Data provided here demonstrate this approach to save money, be broadly applicable worldwide, and potentially aid in precision management of the pandemic, thereby helping to accelerate the global economic recovery that billions of people rely upon for their livelihoods.

Keywords: Coronavirus; Global health; Modeling; Wastewater-based epidemiology.

Graphical abstract

Fig. 1

Under a best-case scenario of no in-sewer signal loss, wastewater generation (50–500 L/person/d) and virus shedding (56.6 million–113.2 billion viromes/d) are important variables determining the detectability in community wastewater of a single infected person among one hundred to two million healthy individuals, assuming homogeneous distribution of cases.

Fig. 2

Seasonal weather conditions influence soil and wastewater temperatures (A), which in turn modulate the in-sewer persistence of SARS-CoV-2 ($t_{\frac{1}{2}}$; B). Assuming shedding of a steady virome load into the sewer (100 M genomes/d), the detectable signal at a city's monitoring location (here Tempe, AZ, USA) will change significantly over the course of a year (C). This is due to temperature-induced variability in the time-to-depletion of the virus below the assumed detection limit of 10 genomes/mL (D).

Fig. 3

Annual change of the in-sewer time to depletion of SARS-CoV-2 computed for 8 cities impacted by the COVID-19 pandemic and for the present use case, the city of Tempe, Arizona (USA).

Fig. 4

Model output, illustrating how a constant signal observed at the intake of a given city's wastewater treatment plant (solid black line) may result from a multitude of different combinations of virus loadings, depending on seasonal weather conditions that dictate in-sewer decay of the qRT PCR signal.

Fig. 5

Tempe, AZ undergoes extreme seasonal temperature changes which are predicted to impact SARS-CoV-2 detectability as illustrated by computer simulations. The city has been monitoring wastewater for opioids since May 2018, first in 3 and currently in 5 areas, and recently started monitoring for COVID-19 with the intent of adding this new data source to an existing public health online dashboard to which wastewater analysis data get posted in real-time (https://arcg.is/ey0Ha).