Translating Wastewater Data 

for Policymaking

Aparna Keshaviah, Sc.M.

Senior Statistician, Mathematica

akeshaviah@mathematica-mpr.com
Overview of presentation

2

Impact of standardization on wastewater trends and alerts

Contextualizing wastewater data for policymakers

Triangulating information through data synthesis

Data A

Data B

Data D Data C

Data E
Impact of standardization on wastewater trends and alerts

3

North Carolina

Haywood county 

Pop. 61,971

27% connected to WWTP

Jackson county 

Pop. 43,327

31% connected to WWTP

Research was conducted in partnership with the Tuckaseigee Water & Sewer Authority, Jackson County Department of Public Health, 

and University of Wisconsin-Milwaukee’s School of Freshwater Sciences and supported by a grant from Dogwood Health Trust. 
Influence of different parameters on trends

4

Haywood county

(B) Standardization by flow rate, service population, & viral recovery (BCov) 

(A) Unstandardized viral concentration

(C) Standardization by a human biomarker (PMMoV)

Spearman’s rho: 0.69 – 0.84

(max @2-3d lag)

Spearman’s rho: 0.72 – 0.87

(max @7-8d lag)

Spearman’s rho: 0.68 – 0.86

(max @1-2d lag)
Influence of different parameters on trends

5

Jackson county

(A) Unstandardized viral concentration

(B) Standardization by flow rate, service population, & viral recovery (BCov) 

(C) Standardization by a human biomarker (PMMoV)

Spearman’s rho: 0.76 – 0.84

(max @4d lag)

Spearman’s rho: 0.75 – 0.83

(max @0-4d lag)

Spearman’s rho: 0.73 – 0.84

(max @1-4d lag)
What criteria should trigger an alert?

6

Haywood county Jackson county

Standardized by

flow, pop., BCov

Standardized by

PMMoV

Should alerts be based on: - Levels crossing a threshold? Based on absolute levels or percent change?

- Sustained increase for X days? How many days? Ignore large spikes?

- Statistically significant increase? What type I error rate is appropriate?

- Regression modeling of trends? Ability to handle non-linear effects?
Contextualizing wastewater data for policymakers

7

Wastewater

Case proxies

Pandemic vulnerability Health care capacity

Confirmed cases
Utah DEQ wastewater dashboard

8

WW viral load 

vs. 

confirmed cases
WW viral load

vs. 

proxy measure

Regional 

context

Population 

risk factors

Testing & risk

Community 

vulnerability

+ 

policy/community Ds

Mathematica wastewater dashboard

9

WW viral load

vs. 

confirmed cases
Curated COVID-19 repository

10

https://www.mathematica.org/features/covid-19-curated-data-modeling-and-policy-resources
Triangulating information through data synthesis

11

Montana

Research was partly supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number 

U54GM115371. 

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. 
Site Drug Use Profiles

12

Average Estimated Dose 

(mg/person)

Rural

(0.73)

#1 Methamphetamines

#3 Codeine

#2 Tramadol

#4 Amphetamines

Urban 

(0.36)

#3 Methamphetamines

#2 Tramadol

#1

Codeine

#4 Amphetamines
Estimating Black Market Activity

13

Methamphetamines

Wastewater dose

Prescription dose
Predicting Need for Overdose Response

14

Heroin

¬ EMS overdose call

involving heroin
Measuring Law Enforcement Impact

15

Methamphetamines

¬ Large seizure of 

meth / amphetamines

Amphetamines
Some publications on our wastewater work

Call to action around 

the opioid epidemic 

16

Taking lessons from the 

opioid epidemic for COVID-19

Developing a flexible 

national surveillance system
Acknowledgements

17

Marisa Henry

Data Analytics Developer

Xindi C. Hu

Senior Data Scientist
Questions?