[{"model": "dashboard.pathogendescription", "pk": 4, "fields": {"pathogen": "RSV", "lead_text": "Samples are collected at wastewater treatment plants from across the state and analyzed at our lab in Chicago. Results are posted and updated weekly. Numbers on the y-axis represent respiratory syncytial virus Illness (RSV) viral remnants in gene copies/liter. Dates on the x-axis are dates the samples were collected.", "details": "

Interpreting results

Wastewater surveillance data is "noisy," meaning it is highly variable. Viruses are shed from infected individuals at different levels of intensity and for different lengths of time. Samples are also taken from sources (i.e. wastewater treatment plants) that are subject to a variety of environmental impacts, including weather events and industrial activity. For this reason, we focus on trends in the data rather than specific measurements, specifically increases and decreases. A significant increase over time may indicate cases are increasing in a community.

Wastewater surveillance data should always be interpreted alongside other public health metrics.

Why are there two different colored data points?

The different colored data points represent two different types of RSV, types A and B. RSV A and B are the most common types of RSV viruses that infect humans and cause seasonal epidemics, and thus are of importance for public health.

What does the line mean?

Since wastewater data are highly variable, we include a curve fitted to the data to better visualize increases and decreases. We do this by "smoothing" the data using the Locally Weighted Scatterplot Smoothing (LOWESS) method, a local regression analysis. This method is useful because it reduces the influence of data outliers (i.e. sample readings that are much higher or much lower than the other samples taken at the same site during the same general time frame). We also include 95% confidence intervals around the trend line. This means we are 95% certain the actual trend line is within the intervals.

What is the difference between log and linear?

The linear scale uses an equal distance between steps on the vertical axis, adding a constant number for each new step (e.g., 1, 2, 3, 4 or 10, 20, 30, 40). A log scale uses an unequal distance between steps, multiplying by a constant number for each new step. Typically, as we do here, this multiple is 10, so the axis might show steps at 1, 100, 1000, 10,000, etc.

Log scales are used to visualize data when the range of values shown is large and when viewing data over a longer time period. Public Health officials and scientists often work with data on these scales, while linear scales are more familiar to the public.

Limitations of the data

Researchers are still working on understanding many open scientific questions, including how to use the data to tell how many people are sick. Variability between wastewater sources, treatment facilities, and communities makes it difficult to translate the RSV A/B gene copy (gc) concentration into a measure of how many people are infected in the community. However, an upward/downward trend in RSV A/B gc/liter generally suggests a similar trend in the number of people infected within a given community.

For similar reasons related to the variability in each sample, comparing data from site-to-site is also discouraged.

Methodology

Wastewater is analyzed using the GT-Digital RSV Wastewater Surveillance Kit for the QIAcuity® Digital PCR System, which comprises of a molecular reagent kit containing an assay solution and control required for the quantification of respiratory synctial virus (RSV). The nucleocapsid gene is targeted. Results are reported in gene copies per liter (gc/L) of wastewater.

References

Armbruster, D.A. et al., Limit of blank, limit of detection and limit of quantitation, Clin. Biochem. Rev. 29, Suppl 1, 2008

"}}, {"model": "dashboard.pathogendescription", "pk": 2, "fields": {"pathogen": "SARS-CoV-2", "lead_text": "Samples are collected at wastewater treatment plants from across the state and analyzed at our lab in Chicago. Results are posted and updated weekly. Numbers on the y-axis represent SARS-CoV-2 viral remnants in gene copies/liter. Dates on the x-axis are dates the samples were collected.", "details": "

Interpreting results

Wastewater surveillance data is "noisy," meaning it is highly variable. People infected with SARS-CoV-2 shed the virus at different levels of intensity and for different lengths of time. Samples are also taken from sources (i.e. wastewater treatment plants) that are subject to a variety of environmental impacts, including weather events and industrial activity. For this reason we focus on trends in the data rather than specific measurements, specifically increases and decreases. A significant increase over time may indicate cases are increasing in a community.

Wastewater surveillance data should always be interpreted alongside other public health metrics.

Why are there two different colored data points?

We made a change to our lab methods in February 2022 to improve our ability to detect very small quantities of SARS-CoV-2 virus in wastewater. Our new method increases the amount of virus we are able to extract from each sample. Because wastewater contains more than just viruses (chemicals, food scraps, soaps, human waste, etc.), it can be tricky to isolate the viruses we are looking for. The new method helps us do just that.

Data generated with the previous method is not directly comparable with the new method.

What does the line mean?

Since wastewater data is highly variable, we include a trend line that fits a curve to the data to better visualize increases and decreases. We do this by "smoothing" the data using the Locally Weighted Scatterplot Smoothing (LOWESS) method, a local regression analysis. This method is useful because it reduces the influence of data outliers (i.e. sample readings that are much higher or much lower than the other samples taken at the same site during the same general time frame). We also include 95% confidence intervals around the trend line. This means we are 95% certain the actual trend line is within the intervals.

What is the difference between log and linear?

The linear scale uses an equal distance between steps on the vertical axis, adding a certain number for each new step (e.g., 1, 2, 3, 4 or 10, 20, 30, 40). A log scale uses an unequal distance between steps, multiplying a certain number for each new step. Typically, as we do here, this multiple is 10, so the axis might show steps at 1, 100, 1000, 10,000, etc.

Limitations of the data

Researchers are still working on understanding many open scientific questions, including being able to tell how many people are sick. Variability between wastewater sources, treatment facilities, and communities makes it difficult to translate the SARS-CoV-2 gene copy (gc) concentration into a measure of how many people are infected in the community. However, an upward/downward trend in SARS-CoV-2 gc/liter generally suggests a similar trend in the number of people infected within a given community.

For similar reasons related to the variability in each sample, comparing data from site-to-site is also discouraged.

Methodology

Wastewater is analyzed using digital polymerase chain reaction (dPCR) to determine the concentration of the SARS-CoV-2 virus in a sample. The nucleocapsid protein (N) gene of the virus is targeted in the assay, and results are reported in gene copies per liter (gc/L) of starting wastewater.

References

"}}, {"model": "dashboard.pathogendescription", "pk": 6, "fields": {"pathogen": "Influenza-A", "lead_text": "Samples are collected at wastewater treatment plants from across the state and analyzed at our lab in Chicago. Results are posted and updated weekly. Numbers on the y-axis represent influenza A viral remnants in gene copies/liter. Dates on the x-axis are dates the samples were collected.", "details": "

Interpreting results

Wastewater surveillance data is "noisy," meaning it is highly variable. People infected with influenza shed the virus at different levels of intensity and for different lengths of time. Samples are also taken from sources (i.e. wastewater treatment plants) that are subject to a variety of environmental impacts, including weather events and industrial activity. For this reason we focus on trends in the data rather than specific measurements, specifically increases and decreases. A significant increase over time may indicate cases are increasing in a community.

Wastewater surveillance data should always be interpreted alongside other public health metrics.

What does the line mean?

What is the difference between log and linear?

Limitations of the data

Researchers are still working on understanding many open scientific questions, including being able to tell how many people are sick. Variability between wastewater sources, treatment facilities, and communities makes it difficult to translate the influenza A gene copy (gc) concentration into a measure of how many people are infected in the community. However, an upward/downward trend in influenza A gc/liter generally suggests a similar trend in the number of people infected within a given community.

For similar reasons related to the variability in each sample, comparing data from site-to-site is also discouraged.

Methodology

Wastewater is analyzed using the GT-Digital Influenza and SARS-CoV-2 Wastewater Surveillance Multiplex Assay Kit for the QIAcuity® Digital PCR System, which comprises of a molecular reagent kit containing all primers, probes, and controls for wastewater surveillance of influenza and SARS-CoV-2. The kit complies with the CDC Wastewater Surveillance Testing Method guidance for reporting to the National Wastewater Surveillance System (NWSS). Results are reported in gene copies per liter (gc/L) of starting wastewater.

References

Armbruster, D.A. et al., Limit of blank, limit of detection and limit of quantitation, Clin. Biochem. Rev. 29, Suppl 1, 2008

"}}, {"model": "dashboard.pathogendescription", "pk": 7, "fields": {"pathogen": "Influenza-B", "lead_text": "Samples are collected at wastewater treatment plants from across the state and analyzed at our lab in Chicago. Results are posted and updated weekly. Numbers on the y-axis represent influenza B viral remnants in gene copies/liter. Dates on the x-axis are dates the samples were collected.", "details": "

Interpreting results

Wastewater surveillance data is \"noisy,\" meaning it is highly variable. People infected with influenza shed the virus at different levels of intensity and for different lengths of time. Samples are also taken from sources (i.e. wastewater treatment plants) that are subject to a variety of environmental impacts, including weather events and industrial activity. For this reason we focus on trends in the data rather than specific measurements, specifically increases and decreases. A significant increase over time may indicate cases are increasing in a community.

Wastewater surveillance data should always be interpreted alongside other public health metrics.

What is the difference between log and linear?

Limitations of the data

Researchers are still working on understanding many open scientific questions, including being able to tell how many people are sick. Variability between wastewater sources, treatment facilities, and communities makes it difficult to translate the influenza B gene copy (gc) concentration into a measure of how many people are infected in the community. However, an upward/downward trend in influenza B gc/liter generally suggests a similar trend in the number of people infected within a given community.

For similar reasons related to the variability in each sample, comparing data from site-to-site is also discouraged.

Methodology

References

Armbruster, D.A. et al., Limit of blank, limit of detection and limit of quantitation, Clin. Biochem. Rev. 29, Suppl 1, 2008

"}}, {"model": "dashboard.pathogendescription", "pk": 5, "fields": {"pathogen": "SARS-CoV-2-Genomic", "lead_text": "Samples are collected at wastewater treatment plants throughout the state, processed at a University of Illinois Chicago laboratory, then sequenced at Argonne National Laboratory to determine the variants of SARS-CoV-2 virus present. Numbers on the y-axis represent the relative abundance of SARS-CoV-2 lineages as a percentage. Dates on the x-axis represent sample collection dates. Date display options include data from the prior two years and data from the entire study time. A variant color legend is found below.", "details": "

Interpreting results

What are virus variant lineages? Do different variants cause different disease?

A viral variant is a version of a virus that has genetic differences (mutations) from the reference strain of that virus. Because viruses mutate over time, new variants are always emerging. New viral variants can lead to different lineages or subtypes of viruses.

Mutation: A change in the genetic information introduced during viral replication.

Variant: A genome that contains a particular set of mutations.

Lineage: A collection of variants related to each other based on genomic analysis.

Variant analysis is of interest to doctors, public health officials, and community members alike because different variants of a virus may produce more or less severe disease over time.

How are viral lineages determined?

Wastewater undergoes genomic sequencing at Argonne National Laboratory where bioinformaticians (data scientists) use the sequence data to determine the variants of SARS-CoV-2 virus present in the samples. Individuals are typically only infected with one variant of SARS-CoV-2 at a time and shed only that variant. The view seen here represents all variants present in the community at the given point in time.

Our sequencing strategy utilizes the entire genome of SARS-CoV-2 to identify mutations that are found in distinct variants of the virus. Full genome coverage provides better resolution for distinguishing variants, especially those that are very similar to each other. Variant names and lineage relationships are determined by the World Health Organization (WHO).

What is the sequencing plot showing?

This plot displays the relative abundance, or proportion, of lineages found in a wastewater sample collected during a particular collection week. This plot was generated after comparing sample sequences to a SARS-CoV-2 reference genome and identifying characteristic mutations associated with different variants. The percentage of each variant present in the sample is then calculated and displayed.

What do the results mean?

The SARS-CoV-2 variants identified in a particular plant’s wastewater can provide insight into the variants circulating in the population that the plant serves. Wastewater disease surveillance is uniquely advantageous because it passively captures all of the virus shed in wastewater from the community where the wastewater was collected, not just the virus that is found in symptomatic individuals who seek health care and are tested in laboratories. Wastewater data is not interpreted in isolation but rather is considered alongside other public health metrics to fully understand the ecology and effects of a virus in a particular community.

Limitations of the Data

The ability to sequence SARS-CoV-2 depends significantly on the amount of the virus present within the sample. The higher the concentration, the more accurate and complete the sequencing is. If there is not much virus present in the wastewater at the time of sampling, sequencing results become less reliable. Further, the detection of variants is limited by the reference database. If a specific variant has not yet been identified by the wider health community, we will not be able to display it until it has been formally named and defined.

Methodology

Wastewater samples are collected from plants across the state of Illinois once or twice a week and are shipped overnight to a laboratory at the University of Illinois Chicago. At the lab, wastewater samples are concentrated and undergo nucleic acid (genomic material) extraction and further preparation for sequencing. Around 60 samples are sequenced each week, and over 4,200 having been sequenced since December 2021. After sequencing, a bioinformatic workflow is employed that characterizes the relative abundance of SARS-CoV-2 variants in each sample.

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Northeast Sewage Treatment Plant