The Wigginton-Eisenberg Laboratory works to provide wastewater testing data on multiple pathogens across south-east Michigan. The grid below provides an at-a-glance vision of the current state of wastewater levels. For more detailed information including graphs of this data over time, please visit each organism under the Pathogens tab. For more information about this system, you can click on the information icons ( ) throughout this dashboard or visit the About Page tab.

Testing for Adenovirus




to


Testing for Hepatitis A




to

Testing for Influenza




to



The primer set used for Mpox testing changed on 15 August 2024. For more information, please visit the About Page.

Testing for Mpox




to

Testing for Norovirus




to

Note: The original GII methodology has been retired since August 2024. For more information, visit the About Page.



Testing for Rotavirus




to



Testing for RSV




to




Testing for SARS-CoV-2






to





Catchment Area Maps

Ann Arbor WWTP



Flint WWTP



Jackson WWTP



Tecumseh WWTP



Ypsilanti WWTP





Table of Contents

  1. Introduction
    1. Overview of Process
    2. Sample Collection and Reporting Schedule
  2. Tertile Levels
  3. Clinical Correlation
    1. SARS-CoV-2
    2. Influenza A
  4. Trends
    1. Modification of CDC Trends
    2. SWEEP Trend
  5. Additional Calculations
  6. Case Data & Proxies
    1. COVID-19 Case Data
    2. Influenza Hospitalization Data
    3. Google Trends
    4. RSV Hospitalizations
    5. Rotavirus Positive Tests
  7. Infographic Downloads
    1. For SARS-CoV-2
  8. Laboratory Methods
    1. Influent SARS-CoV-2
    2. Influent Norovirus & Rotavirus
    3. Solids Processing
  9. Wastewater Treatment Plants
  10. About Wastewater Testing - General
    1. History
    2. Considerations in Testing and Evaluation
    3. SARS-CoV-2 Development
  11. Citations
  12. Other Wastewater Detection Dashboards
  13. Dashboard Update Timeline

Contact Us

To contact us, please email help.um.wastewatermonitoring@umich.edu.

The Team

  • Dr. Krista Wigginton
  • Dr. Marisa Eisenberg
  • Dr. Betsy Foxman
  • Dr. Michelle Ammerman
  • Dr. Felipe de Paula Nogueira Cruz
  • Kate Kusiak Galvin
  • Betsy Salzman
  • Stephanie Iorga
  • Vanessa Slack
  • Ahmard Clay

Previously, the team’s data was displayed at the following site: https://umich-wbe.shinyapps.io/sars-cov-2_dash/.

For more information regarding the work done by the team, please visit:

Acknowledgements & Partners

Wastewater Treatment Plant Partners:

  • Ann Arbor Wastewater Treatment Plant
  • Jackson Wastewater Treatment Plant
  • Flint Wastewater Treatment Plant
  • Tecumseh Wastewater Plant
  • Ypsilanti Community Utilities Authority

Health Departments:

  • Genesee County Health Department
  • Jackson County Health Department
  • Lenawee County Health Department
  • Washtenaw County Health Department
  • Michigan Department of Health and Human Services

Funding

This work is funded by the Michigan Department of Health and Human Services (MDHHS) and the University of Michigan.

Team Alumni

  • Dr. Kevin Bakker
  • Kaitlyn Chin
  • Shreya Mullapudi
  • Khaitlyn Figueroa
  • Rachel Learman
  • Jinyi Cai

The Dashboard

This dashboard was created by Julie (Jules) Gilbert.

Introduction


Wastewater-based epidemiology requires cooperation and coordination between many different groups and people, in order to best balance resources and data timeliness.


Overview of Process



Daily solid and influent samples are collected from five different wastewater treatment plants (WWTPs) across southeast Michigan: Ann Arbor, Flint, Jackson, Tecumseh, and Ypsilanti. These samples are delivered to the Wigginton-Eisenberg Laboratory at the University of Michigan twice per week. SARS-CoV-2, Norovirus, and Rotavirus testing is done on the influent samples. These samples undergo a PEG precipitation step to concentrate the targets. RSV, Hepatitis A, Influenza A & B, Mpox, and SARS-CoV-2 testing is done on the solid samples. These samples undergo a total solids determination step. All samples are put through an RNA Extraction and ddPCR step. Following all laboratory work, data undergo quality assurance and control procedures and are uploaded to the dashboard.


Sample Collection & Reporting Schedule



Each day, samples are collected and held at the wastewater treatment plant (WWTP). The courier arrives twice per week, on Mondays and Thursdays (accommodations are made to account for holidays or other interruptions) and takes the samples from the WWTP and brings them to the laboratory at the University of Michigan. In general, wastewater samples from Tuesday and Wednesday arrive in the lab on Thursday, and the results from their testing appears on the dashboard the following evening. Wastewater samples from Friday, Saturday, and Sunday arrive in the lab on Monday, and the results from their testing appears on the dashboard Tuesday evening. Samples that are collected on the day of the courier arrival usually arrive that same day, but occasionally don’t arrive until the next courier day.

Note: This schedule applies primarily to our SARS-CoV-2 Influent testing. Viruses detected in solid samples as well as other organisms detected from influent samples undergo testing in such a way that reporting occurs on Fridays. <Return to Table of Contents>

About the Tertile Level System

Tertile boundaries were calculated on 9/24/2024 using PMMoV-normalized wastewater values. Levels were calculated such that a third of all samples from the selected time period fell into each category. These levels give a general sense of whether the value is high or low relative to values that have been previously detected.


Table 1. Tertile cut-off values for each site, organism, and sample type combination

Site Organism n Start Date End Date Low-to-Medium Medium-to-High
AA SOLID, Influenza A 97 11/18/2022 6/30/2024 3.41970633006921e-05 8.95109374545913e-05
AA SOLID, Influenza B 10 2/26/2024 5/3/2024 2.51993924355136e-05 3.83195992031683e-05
AA SOLID, RSV 93 10/28/2022 7/21/2024 2.7180600542203e-05 5.76904679159707e-05
FL SOLID, Influenza A 70 11/24/2022 ⅚/2024 6.75631891068647e-05 0.000156308
FL SOLID, Influenza B 16 2/13/2024 4/2/2024 0.000104767 0.000147767
FL SOLID, RSV 26 11/1/2022 2/14/2024 4.67589716360255e-05 8.42181368783069e-05
JS SOLID, Influenza A 87 9/28/2022 6/16/2024 8.09408196190823e-05 0.000232853
JS SOLID, nfluenza B 21 1/25/2024 5/5/2024 0.000105141 0.000169936
JS SOLID, RSV 83 11/1/2022 5/24/2024 6.10846248994842e-05 0.000110173
TM SOLID, Influenza A 49 11/18/2022 3/16/2024 0.00015368 0.000450748
TM SOLID, Influenza B 7 3/2/2024 3/30/2024 0.000140812 0.000441055
TM SOLID, RSV 48 10/25/2022 3/16/2024 0.000131591 0.00021786
YC SOLID, Influenza A 65 11/10/2022 3/22/2024 5.05026887095547e-05 0.000106744
YC SOLID, Influenza B 9 2/14/2024 3/26/2024 3.86427287165522e-05 5.46019039935452e-05
YC SOLID, RSV 66 11/2/2022 3/15/2024 5.39534948583985e-05 0.00010649
AA INFLUENT, SARS-CoV-2 N1 1051 7/9/2021 9/18/2024 0.000215362 0.000474341
FL INFLUENT, SARS-CoV-2 N1 868 7/16/2021 9/18/2024 0.000447368 0.000943662
JS INFLUENT, SARS-CoV-2 N1 921 7/12/2021 9/17/2024 0.000396556 0.000975766
TM INFLUENT, SARS-CoV-2 N1 924 1/13/2022 9/19/2024 0.000303463 0.000721156
YC INFLUENT, SARS-CoV-2 N1 1024 ⅞/2021 9/19/2024 0.000345348 0.000677717
AA INFLUENT, Norovirus G2 90 11/30/2023 9/7/2024 0.005203539 0.013955794
FL INFLUENT, Norovirus G2 68 11/27/2023 9/8/2024 0.00926363 0.021897898
JS INFLUENT, Norovirus G2 86 11/30/2023 9/8/2024 0.011280445 0.036472588
TM INFLUENT, Norovirus G2 88 12/1/2023 9/8/2024 0.003060806 0.019375112
YC INFLUENT, Norovirus G2 77 11/26/2023 9/8/2024 0.008446047 0.029814111
AA INFLUENT, Rotavirus 336 4/2/2022 9/18/2024 0.000319777 0.001156051
FL INFLUENT, Rotavirus 242 3/2/2022 9/15/2024 0.000802782 0.003804197
JS INFLUENT, Rotavirus 311 4/1/2022 9/17/2024 0.001066216 0.004597471
TM INFLUENT, Rotavirus 268 ⅜/2022 9/16/2024 0.000330236 0.005173907
YC INFLUENT, Rotavirus 330 4/1/2022 9/18/2024 0.000550329 0.002676543

Tertile levels set as:

  • 1 = Low = Seven-sample rolling average < 33.33th percentile
  • 2 = Medium = Seven-sample rolling average < 66.66th percentile
  • 3 = High = Seven-sample rolling average >= 66.66th percentile

About the Clinical Correlation Level System


Clinical Correlation Levels were defined based on the measured PMMoV-normalized wastewater values and the reported cases or hospitalization data available for the diseases associated with the detected viruses.

As of 11/06/2023, for Influenza A (Solids) and RSV (Solids), if the day’s sample is calculated to be below the limit of quantification, the level is re-set to 1. Values are calculated to be below the limit of quantification if the concentration of either the organism or the PMMoV measurement is below the three-plate rolling average of the Negative controls concentration or the three-plate rolling average of the high error bar for the target concentration of the Negative control wells at a 95% confidence interval, whichever is higher.


SARS-CoV-2:



Level thresholds for SARS-CoV-2 were calculated based on correlating case rates and normalized wastewater concentration levels in Ann Arbor over the 2021-2022 year. The wastewater concentration levels correspond approximately to case rates as follows:

  • 1 = 50 or less weekly cases/100K
  • 2 = 51 to 200 weekly cases/100K
  • 3 = 201 to 400 weekly cases/100K
  • 4 = 401 weekly cases/100K or more

Influenza A:



Level thresholds for Influenza A were calculated based on correlating hospitalization rates and normalized wastewater concentration levels over the 2022-2023 Flu Season. The wastewater concentration levels correspond approximately to hospitalization levels as follows:

  • 1 = 0-0.25% of inpatient beds filled with Influenza A patients (county hospitals)
  • 2 = 0.25%-1% of inpatient beds filled with Influenza A patients (county hospitals)
  • 3 = 1-4% of inpatient beds filled with Influenza A patients (county hospitals)
  • 4 = 4-6% of inpatient beds filled with Influenza A patients (county hospitals)
  • 5 = 6%+ of inpatient beds filled with Influenza A patients (county hospitals)

About Trends


Trend variables assist in defining the direction wastewater levels are moving in, whether they are increasing, decreasing, or staying steady. They are best used in consideration alongside the Quartile or Clinical Correlation levels. <Return to Table of Contents>


Modification of the CDC's Incidence Trend


For SARS-CoV-2, “Trend” is a modification of the CDC’s Incidence Trend calculation, which was presented by Matt Lozier (document originally here - retrieved via the Wayback Machine from a web scrape done on 19 November 2022 available here).

If the “Trend” metric falls into “decline”, then a counter begins to count the number of days the metric is in decline. This “Days in Decline” metric uses the derivative of a spline fit to the three sample average of the PMMoV-normalized wastewater data in combination with the Trend category determination in order to decide whether the system is still “in decline”.

This decision to exit a decline is conservative, in order to account for “wobble” (natural variation) within the system and along the decline. It is slow to turn off (it needs 5 sample days of an overall increase, “elevated plateau”, or “elevated growth” in a row to turn off the downward counter) and it doesn’t let the decline stop if the system is still in “low plateau” or the category can’t be determined due to low sampling over the previous 2 weeks. This allows the scenario of a decrease, plateau, and continuation of decrease, which is a scenario which would want to be counted as one long decline.


SWEEP Trend


The trend variable calculation that is presented on the Trends & Comparison page is calculated following the methodology presented by the Sentinel Wastewater Epidemiology Evaluation Project run by the Michigan Department of Health and Human Services.

7-Day Trend = First, the seven day rolling average for the given gene is calculated. Then the seven day average value from 7 days ago is subtracted from the current seven day average. This value is divided by the seven day average from 7 days ago and multiplied by 100.

14-Day Trend = First, the fourteen day rolling average for the given gene is calculated. Then the fourteen day average value from 14 days ago is subtracted from the current fourteen day average. This value is divided by the fourteen day average from 14 days ago and multiplied by 100. <Return to Table of Contents>

Both trend values are categorized as:



Additional Dashboard Calculations


  • 7- or 3- Sample Average: Rolling average for the last 7 or 3 samples. Includes all values (samples below limit of quantification, and outliers)
  • 7- or 3- Sample Average w/o Outliers: Rolling average for the last 7 or 3 samples. Includes samples below limit of quantification, does NOT include outliers.
  • Average Weekly Samples: {city} {sample type} average weekly samples since YYYY-MM-DD; calculated as the average number of samples tested per week over the most recent two months prior to the end date selected in the “Date Range” sidebar selector
Organism Limit of Detection
Norovirus < 4 Positive droplets detected in either PMMoV or Sample merged wells
Rotavirus < 4 Positive droplets detected in either PMMoV or Sample merged wells

For Norovirus** & Rotavirus, outliers (high) are calculated by testing site. Using pmmov-normalized sample values, the 7-sample rolling average is calculated, along with the 7-sample standard deviation. The lower limit and upper limit of accepted “in-range” values are calculated as the 7-sample rolling average +/- (7 * the rolling standard deviation). In data prior to implementing this change, high outliers were removed manually from the dataset based on laboratory decision-making.

** for the old Norovirus GII system

Case Data & Proxies


Reported case and hospitalization data for the diseases caused by the viruses we detect in the wastewater are made available as we are able. Displaying this data side by side with our wastewater detection is an important step in understanding the relationship between the two. It is important to consider the spatial aspect of this data, and how it can be different between the wastewater catchment area and the various geography levels that case or hospitalization data is available at.


COVID-19 Case Data


  • Case data are from the Michigan Disease Surveillance System (MDSS), the Michigan Department of Health and Human Services’ web based communicable disease reporting system. Cases are attributed to date of illness onset. If illness onset date is unavailable, date of testing is used. If date of testing is unavailable, date of referral to MDSS is used. Date attributions are subject to change over time as better data become available. Both probable and confirmed status cases are available for display. Confirmed cases only include individuals with a positive diagnostic laboratory test for COVID-19. Probable cases include individuals with COVID-19 symptoms and an epidemiological link to a confirmed case or a positive serology test, but do not have a positive diagnostic laboratory test. <Return to Table of Contents>

  • Cases were assigned to a given wastewater catchment area only if they had associated location data included in their case report. This information for cases is associated with the individual’s residential address, so this does not account for any travel into or out of the catchment area.

  • COVID-19 case data is presented as a 7-Day Average Number of Cases per Day, per 100,000 Population. If this value for a day is < 10, then the value is not displayed (censored).


Table 3. Estimated Populations of Wastewater Catchment Areas

Wastewater Catchment Area Estimated Population Source
Ann Arbor 121093 ACS 5Y Estimate 2020 - City
Flint 95999 ACS 5Y Estimate 2020 - City
Jackson 90000 State of Michigan SWEEP
Tecumseh 8680 State of Michigan SWEEP
Ypsilanti 330000 State of Michigan SWEEP


Influenza Hospitalization Data


Influenza hospitalization data is provided by the Michigan Department of Health and Human Services (MDHHS) once per week. Hospitals are assigned to a county based upon their physical location/address. “Percent of Inpatient Beds filled with Influenza Patients” is calculated as the total number of inpatient beds filled with influenza patients divided by the total number of inpatient beds for all hospitals in the reported county each day.

This data does not differentiate between Influenza A and Influenza B infections. To begin to contextualize the current circulating percentages of Influenza A and Influenza B, visit https://www.cdc.gov/flu/weekly/index.htm.


Google Trend Analytics


Google Trend information is pulled periodically as a potential correlate of disease occurrence.

This information is presented at a weekly, state-wide level only. The organism specific search terms used are:

Adenovirus “adenovirus”, “adenovirus symptoms”

Hepatitis A “hepatitis a”, “hepatitis a symptoms”

Influenza “flu symptoms”, “influenza”, “flu”, “influenza symptoms”

Mpox “mpox symptoms”, “mpox”, “monkeypox symptoms”, “monkeypox”

Norovirus “norovirus symptoms”, “norovirus”, “stomach flu”, “stomach flu symptoms”

RSV “rsv symptoms”, “rsv”

SARS-CoV-2 “covid”, “covid symptoms”

Rotavirus “rotavirus”, “rotavirus symptoms”


RSV Hospitalizations


Data regarding RSV hospitalizations is pulled from the CDC’s Data API at https://data.cdc.gov/Case-Surveillance/Weekly-Rates-of-Laboratory-Confirmed-RSV-Hospitali/29hc-w46k. For additional information regarding this data, visit https://www.cdc.gov/rsv/research/rsv-net/index.html

The reported data is collected as part of the Respiratory Syncytial Virus Hospitalization Surveillance Network (RSV-NET) and includes data on both children under the age of 18 and adults. The data presented on our dashboard is for Michigan state-wide, and includes the rate of hospitalization per 100,000 population for the week ending on the date indicated. Data is subject to reporting lags. As data are received each week, prior case counts and rates are updated accordingly. <Return to Table of Contents>


Rotavirus Test Positivity


Data regarding test positivity is web-scraped from the table at https://www.cdc.gov/surveillance/nrevss/images/rota/ROT1PP_Reg3.htm. For additional information regarding this data, visit https://www.cdc.gov/surveillance/nrevss/rotavirus/region.html

Participating laboratories report weekly to CDC the total number of rotavirus tests performed that week, and the number of those tests that were positive. The data presented on our dashboard is for the midwest region, which includes Michigan, Ohio, Indiana, Illinois, Wisconsin, Minnesota, Iowa, Missouri, Kansas, Nebraska, South Dakota, and North Dakota. The data presented is the average percent of those tests that were positive from three adjacent weeks: the specified week, and the weeks preceding and following it (centered 3-week moving average). Data is subject to reporting lags. As data are received each week, prior data is updated accordingly.

Infographic Downloads


For SARS-CoV-2


Flyer-type downloads are available for each of the five wastewater treatment plant catchment areas for SARS-CoV-2 levels. These infographics display a line chart that plots the PMMoV-normalized SARS-CoV-2 Influent N1 measurements and seven-sample rolling average for a five month period. The levels displayed on the infographic are the Clinical Correlations levels for SARS-CoV-2 detection. The date displayed in the upper right-hand corner is the most recent sample date avaiable at the time the infographic was made. The current Clinical Correlation level and Trend is displayed, and the 10 Day projection for what the Clinical Correlation level and Trend will be is also included. The 10 Day projection is determined based on an unweighted linear regression model. The map of the wastewater treatment plant catchment area is also included on the graphic. <Return to Table of Contents>

Laboratory Methods


Influent SARS-CoV-2

We use the following methods to extract, concentrate, and quantify SARS-CoV-2 in wastewater.

  • Extraction and Concentration

  • Quantification

  • Influent Methodology from Flood, M. T., D’Souza, N., Rose, J. B., & Aw, T. G. (2021). Methods evaluation for rapid concentration and quantification of SARS-CoV-2 in raw wastewater using droplet digital and quantitative RT-PCR. Food and environmental virology, 13(3), 303-315. (with modifications)


Norovirus and Rotavirus


NNorovirus GII and Rotavirus species A are detected using influent samples prepared using the method described above, but with the primers from the following papers.

  • For Norovirus GII: Kageyama, Tsutomu, et al. “Broadly reactive and highly sensitive assay for Norwalk-like viruses based on real-time quantitative reverse transcription-PCR.” Journal of Clinical Microbiology 41.4 (2003): 1548-1557.
  • For Rotavirus: Zeng, S-Q., et al. “One-step quantitative RT-PCR for the detection of rotavirus in acute gastroenteritis.” Journal of Virological Methods 153.2 (2008): 238-240.
  • For Norovirus GI & GII (New): dPCR Norovirus GI;GII Assay Kit for the Bio-Rad QX200® v1.1 – BETA; SKU: 100468-1.1; Cat# 100468-1.1

Further information regarding norovirus GII monitoring is available online: https://doi.org/10.1101/2023.04.10.23288357


Solids Methods


Extraction from settled solids is used to detect Adenovirus, Hepatitis A, RSV, Influenza A, Influenza B, and Mpox. The method for sample processing is described in the paper below. Kim, Sooyeol, et al. “SARS-CoV-2 RNA is enriched by orders of magnitude in primary settled solids relative to liquid wastewater at publicly owned treatment works.” Environmental science: water research & technology 8.4 (2022): 757-770.

The primers used for Adenovirus, Influenza A, Influenza B, RSV, Mpox, and Hepatitis A are from the following papers.

  • Heim, Albert, et al. “Rapid and quantitative detection of human adenovirus DNA by real‐time PCR.” Journal of medical virology 70.2 (2003): 228-239 and mentioned in Lu X, Trujillo-Lopez E, Lott L, Erdman DD.2013.Quantitative Real-Time PCR Assay Panel for Detection and Type-Specific Identification of Epidemic Respiratory Human Adenoviruses. J Clin Microbiol51:.https://doi.org/10.1128/jcm.03297-12

  • Xagoraraki IKuo DH, Wong K, Wong M, Rose JB.2007.Occurrence of Human Adenoviruses at Two Recreational Beaches of the Great Lakes. Appl Environ Microbiol73:.https://doi.org/10.1128/AEM.01239-0

  • Influenza A & B: https://www.cdc.gov/coronavirus/2019-ncov/lab/multiplex.html

  • Wolfe, Marlene K., et al. “Wastewater-based detection of two influenza outbreaks.” Environmental Science & Technology Letters 9.8 (2022): 687-692.

  • Hughes, Bridgette, et al. “Respiratory syncytial virus (RSV) RNA in wastewater settled solids reflects RSV clinical positivity rates.” Environmental Science & Technology Letters 9.2 (2022): 173-178.

  • Previous Primers: Wolfe, Marlene K., et al. “Use of Wastewater for Mpox Outbreak Surveillance in California.” New England Journal of Medicine (2023).

  • Primers as of 15 August 2024 (samples after 19 August 2024): https://www.cdc.gov/poxvirus/mpox/pdf/non-variola-orthopoxvirus-generic-real-time-pcr-test.pdf

  • Costafreda, M. Isabel, Albert Bosch, and Rosa M. Pintó. “Development, evaluation, and standardization of a real-time TaqMan reverse transcription-PCR assay for quantification of hepatitis A virus in clinical and shellfish samples.” Applied and environmental microbiology 72.6 (2006): 3846-3855.

Wastewater Treatment Plants


Solid and influent samples are collected from all five wastewater treatment plant locations. However, each plant has different features in both mechanics and populations served. Influent samples are 24-hour composite samples, and samples are taken from untreated wastewater.

Plant Area Type Avg. Flow Rate (in Million Gallons per Day)
Ann Arbor Urban 18.5
Flint Urban 17.4
Jackson Urban 12.79
Tecumseh Rural 0.7
Ypsilanti Rural 27

<Return to Table of Contents>

About Wastewater Testing - General


Pathogenic microorganisms that infect humans are commonly shed in feces, urine, saliva, and skin. The shed organisms can eventually end up in municipal wastewater, where they can be detected and quantified. Such measurements can provide helpful information for public health officials regarding community health.


History


In the 1960s, epidemiologists began using wastewater to track and contain polio outbreaks. These early efforts relied on culture-based methods to detect polioviruses in the wastewater. In the 1980s, Hepatitis A was monitored in wastewater using hybridization techniques with radioactive cDNA probes. Broader wastewater pathogen detection techniques began in the 1990s with the advent of polymerase chain reaction (PCR) technology. PCR-based methods have been widely used for SARS-CoV-2 surveillance.


Considerations in Testing and Evaluation


  • Normalizing the sample - Human inputs within a single community’s wastewater can be diluted during storm-water events or other changes in wastewater inputs. Likewise, comparing pathogen concentrations in the wastewaters between different community sewersheds can be complicated by different wastewater compositions, such as the amount and types of industries in the sewershed. Normalizing ultimately helps account for the number of individuals that contribute to the wastewater sample and thus facilitates comparing organism concentrations measured over time in the same sewershed and between sewersheds. Potential normalization factors include concentrations of of Pepper Mild Mottle virus, crAssphage, Bacteroides HF183, or Lachnospiraceae Lachno3 in the wastewater. Normalizing by wastewater flow rate and sewershed population, as well as chemical concentrations, have also been proposed. <Return to Table of Contents>

  • Sample collection method and frequency - Wastewater collected as a grab sample represents a single point in time, whereas composite samples represent wastewater levels over a specified period of time (often 24 hours). Sample collection frequency can affect the ability to observe trends in wastewater–samples collected and analyzed at a higher frequency (e.g., daily samples) provide more information on disease dynamics in a community than samples collected and analyzed at a lower frequency collection (e.g, weekly samples).

  • The laboratory detection method used - Methods used to quantify organisms in wastewater can be culture-based or molecular-based. Culture-based methods require a live, or infectious organism to be detected and quantified. PCR-based methods require the targeted regions of the genome to be intact so that they can be detected and quantified. PCR methods do not relay information about the infectiou state of the organism, but they are relatively quick and specific. With PCR-based methods, pathogen wastewater concentrations can be measured and reported within 1-2 days of when the organisms were shed. Different methods have different limits of detection.

  • The persistence of the organism in the environment - The temperature of the environment, along with the presence of other chemicals and organisms in the environment, may impact the amount of the infectious/live organism or its genome that can be detected.

  • Wastewater-based epidemiology methods have detection limits that depend on the amount of organisms people shed during an infection and the methods used to recover and detect the organism.


SARS-CoV-2 Development


SARS-CoV-2, or the virus that causes COVID-19, is shed in stool during infections. We can therefore quantify SARS-CoV-2 RNA in wastewater to get a sense of a community’s COVID-19 burden. Most efforts to quantify SARS-CoV-2 concentrations in wastewater focus on the liquid fraction of wastewater. Methods have also been developed to measure SARS-CoV-2 RNA in the solid fractions of wastewater and there is evidence that solids fractions contain higher concentrations of SARS-CoV-2 than the liquid fraction (see links below). Focusing on solids can therefore improve detection limits.

Citations


Icons Used in Descriptions of Lab Processes & Timelines:

Other Wastewater Detection Dashboards


<Return to Table of Contents>

History of Dashboard Updates

Record of the changes made to this dashboard (history starting March 2023).

17 March 2023

  • In reference to wastewater sample measurements, instances of “Seven/Fourteen Day” corrected to “Seven/Fourteen Sample” to better reflect the reality of sampling and calculation.
  • Updated About Page formatting to include multiple tabs for improved organization of information.

21 March 2023

  • Influenza A, RSV, and Mpox solid sample PMMoV-normalized data views added to the “Pathogens” tab
  • Google Trend search views for RSV, Influenza A, Norovirus, Mpox, and SARS-CoV-2 search terms were added to the “Pathogens” tab
  • Influenza A, RSV, and Mpox options added to landing page Trend tables

27 March 2023

  • Website title header changed from “Wastewater Sampling by the Wigginton-Eisenberg Research Group at the University of Michigan” to “Community Level Wastewater Monitoring by UM”

28 March 2023

  • Additional team member lab and research group websites added to the Dashboard About Page sidebar.

31 May 2023

  • Original landing page was moved to “Additional Views” > “Trends & Comparison” and replaced with a heat map grid view of all organisms and testing locations as a “Home” page.

19 June 2023

  • Buttons added to the bottom of the heat map grid view on the “Home” page. These buttons allow for quick navigation to the stated organism’s full “Pathogen” page.

27 June 2023

  • Rotavirus influent sample PMMoV-normalized data view added to the “Pathogens” tab
  • Norovirus & Rotavirus laboratory technique section of About Page updated

17 July 2023

  • “Days in Decline” definition and description expanded in About Page

9 August 2023

  • Aesthetic design and color changes made to entire site

11 August 2023

  • Landing page grid had rotavirus added, and new capability to switch between levels determined by quantile or clinical correlations.
  • Landing page panel with level information moved to About page.

15 August 2023

  • Feedback request banner added to landing page.

16 August 2023

  • Option to switch between viewing the 3-sample average line and the 7-sample average line on Influenza A, Norovirus, RSV, and SARS-CoV-2 added to each organism’s scatterplot page.
  • Level view lines (Quartile and/or Clinical Correlation) on Influenza A, Norovirus, Rotavirus, RSV, and SARS-CoV-2 added to each organism’s scatterplot page.
  • Downloadable SARS-CoV-2 Risk Cards made available on landing page.

5 September 2023

  • Header case/hospitalization labels added to Landing Page grid blocks.
  • Pathogen > Influenza A tab: Clinical Correlation and Quartile Level Legends were added to the sidebar, the default landing view of the chart displays the Clinical Correlation levels, and there is now the ability to control the y-axis maximum of the chart.
  • Pathogen > SARS-CoV-2 tab: Clinical Correlation and Quartile Level Legends were added to the sidebar and the default landing view of the chart displays the Clinical Correlation levels.
  • Pathogen > RSV tab: Midwest Region RSV hospitalization data from the CDC was added to the case comparison chart.
  • Pathogen > Rotavirus tab: Midwest Region Rotavirus test positivity data from the CDC was added to the case comparison chart.

5 September 2023

  • X-Axis date labels edited to the same format (ex. Apr ’23) across all charts under the Pathogen table.

1 October 2023

  • Data processing and QAQC systems have been standardized and implemented as a pipeline. The system is still under heavy monitoring to ensure behavior is as expected.

2 October 2023

  • About Page formatting and contents updated.

13 October 2023

  • Navigation improvement between About Page and About Page Table of Contents

26 October 2023

  • Average number of samples per week tested over the previous two months from each site added to each organism under the Pathogen tab.
  • Influenza hospitalization data in Michigan counties added as a “case” option under Pathogens > Influenza A
  • Expanded general site use and information on landing page.
  • Introduced the use of information icon buttons to be able to include additional information about terminology and organisms

6 November 2023

  • Mpox removed from landing page grid. Regular Mpox testing in wastewater was paused on 10 September 2023. All historical data is available under Pathogens > Mpox. The laboratory maintains the ability to restart regular Mpox testing at any time, in order to support public health needs.
  • Landing page levels and rolling-average calculations for Influenza A (Solids) and RSV (Solids) now take into account whether the daily values are below the limit of quantification.

28 November 2023

  • Outlier determination for Influent SARS-CoV-2 changed from a “batch” system to an “inclusive one-at-a-time” system, to better align with the real-world aspect of a sample per day.

12 December 2023

  • Under the Pathogen tab, the Norovirus time-series chart title was edited to increase specificity, as all Norovirus testing is detecting Norovirus GII.

18 December 2023

  • Influenza B testing data added under Pathogens > Influenza.
  • Influenza A and B combined into one dropdown tab (Influenza) and can both be viewed on the same chart.
  • Influenza B sampling frequency data added. Sampling frequency list does not appear if both Influenza A and B are selected.

2 February 2024

  • Norovirus outliers removed from consideration for landing page level calls.
  • Norovirus tab under “Pathogens”:
    • Ability to view last year’s data in comparison on time series chart added.
    • Ability to adjust y-axis limit for better viewing added.
    • Samples below LOD and samples that are high outliers now marked with open circle(s) on the time series chart.

5 February 2024

  • Rotavirus outliers removed from consideration for landing page level calls.
  • Rotavirus tab under “Pathogens”:
    • Ability to view last year’s data in comparison on time series chart added.
    • Samples below LOD and samples that are high outliers now marked with open circle(s) on the time series chart.

9 February 2024

  • Fix made to “Trends & Comparison” view, as it was not appearing

14 February 2024

  • Fix made to Norovirus “Compare to Last Year” view, as it was not appearing

15 February 2024

  • 3- and 7- sample rolling average with or without outliers inclusion/exclusion standardized across organism charts.

19 February 2024

  • Mpox testing resumed due to stakeholder request. Data made available under Pathogens > Mpox.

28 March 2024

  • Updated graphic on About Page describing sample collection, testing, and reporting order of events.

30 May 2024

  • Addition made to Norovirus & Rotavirus outlier logic, whereas if there are three or more samples marked as an outlier in a row, and the three samples occur on two or more testing plates, then the sample values are no longer marked as outliers, even if they meet the initial outlier definition of having a value outside the 7-day rolling average +/- 7*the 7-day rolling standard deviation.

10 June 2024

  • Adenovirus 40/41 added to Pathogens view

5 August 2024

  • Regular testing for the N2 gene target of SARS-CoV-2 in influent samples ended.

27 September 2024

  • Hepatitis A data added under Pathogens > Hepatitis A.
  • Additional Norovirus data, detections of GI and GII using a new methodology, added under Pathogens > Norovirus
  • Landing page placed under construction
  • Ability to add level lines to plots removed
  • Marker for Mpox primer set change added to view in Pathogens > Mpox

07 October 2024

  • New landing page layout with tertile level system implemented

05 December 2024

  • Removed extranneous selectors and re-set defaults in preparation for tertile level system view options

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