© 2021 THE MITRE CORPORATION. ALL RIGHTS RESERVED.

An Alternative Paradigm for 

Digital Exposure Notification: 

Detecting Super-spreading Events

NIST Workshop on Challenges in Digital Proximity Detection in Pandemics

Brian Thompson, Kyle Lemoi, Phil LaGambino, Molly McKnight

January 28, 2021

Approved for Public Release; Distribution Unlimited. Public Release Case Number 21-0175
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Overview

▪ Motivation

– Much of COVID-19 spread is attributable to super-spreading events*, where 

aerosolized virus can accumulate and permeate a room due to poor ventilation

– Current GAEN/TC4TL paradigm does not capture most of these infections, 

e.g., because they are not close enough or index case does not have the app

▪ Idea

– Send an exposure notification if the user spent a significant amount of time in 

the simultaneous presence of multiple others who later report a positive test

– Threshold based on an a posteriori estimate of likelihood the user got infected

▪ Properties

– Works within the GAEN system but fundamentally changes configuration of 

the “attenuation” and “days” parameters and criteria for sending a notification

– Targets high-impact events: the more people, the greater the statistical power

– Linear dependence on adoption rate: works even if index case not using app

– Implicitly captures transmission factors GAEN cannot: biological, situational

*E.g., see Goyal et al. (2020)
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GAEN Configuration — Comparison to Current Paradigm

Parameter Description Configuration under 

current paradigm

Configuration under 

new paradigm Motivation/Rationale

Days

# of days prior to 

positive test or 

symptom onset

High score if at most 

2 days; based on 

infectiousness curve

High score if at least 

3 days; based on 

incubation period

Current paradigm considers whether the person was 

infectious at the time of contact; new paradigm 

considers whether the person got infected then

Attenuation

Bluetooth signal 

attenuation; 

proxy for distance

High score if 

estimated to be 

within 6 feet

High score if 

estimated to be 

within 30 feet

Current paradigm considers whether an infectious person 

was in close contact; new paradigm considers whether 

people who later got infected were in the same room

Duration Duration of 

contact

High score if at 

least 15 minutes

High score if at 

least 30 minutes

Current paradigm based on “plume” model of transmission 

during close contact; new paradigm considers “room” 

model where virus-laden aerosols accumulate in the air

Notification

criteria

When to send a 

notification to 

the app user

“Too close for too 

long” contact with 

an infected person

Nearby multiple others 

who likely got infected 

around that time

Current paradigm considers whether the app user got 

infected by a specific person; new paradigm flags when 

the user was present at a likely super-spreading event
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Example Scenario

▪ 80 people in a bar with a highly infectious person who does not have the app
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Example Scenario

▪ 80 people in a bar with a highly infectious person who does not have the app

▪ 30 of the 80 susceptible people are app users (38% adoption rate)
© 2021 THE MITRE CORPORATION. ALL RIGHTS RESERVED. 6

Example Scenario

▪ 80 people in a bar with a highly infectious person who does not have the app

▪ 30 of the 80 susceptible people are app users (38% adoption rate)

▪ 40 people get infected, including 15 app users
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Example Scenario

▪ 80 people in a bar with a highly infectious person who does not have the app

▪ 30 of the 80 susceptible people are app users (38% adoption rate)

▪ 40 people get infected, including 15 app users

▪ 25 of those infected eventually become symptomatic, including 10 app users

S

S

S

S

S

S

S

S

S

S

S

S

S S

S

S

S

S S

S S

S S S

S
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Example Scenario — Outcome

▪ Current paradigm: Nobody gets notified because the infectious person does 

not have the app and others were not infectious at the time

S

S

S

S

S

S

S

S

S

S

S

S

S S

S

S

S

S S

S S

S S S

S
© 2021 THE MITRE CORPORATION. ALL RIGHTS RESERVED. 9

Example Scenario — Outcome

▪ Current paradigm: Nobody gets notified because the infectious person does 

not have the app and others were not infectious at the time

▪ New paradigm: After the first 3 symptomatic app users report a positive test via 

the app, the other 27 app users get notified, 12 of whom were infected

S

S

S

S

S

S

S

S

S

S

S

S

S S

S

S

S

S S

S S

S S S

S

app users with early symptoms
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Example Scenario — Timeline

Sunday Monday Tuesday Wednesday Thursday Friday Saturday

Day 0

Superspreading

event

Day 1

Day 2 Day 3 Day 4 Day 5

Early 

symptom 

testing

Day 6 Day 7

Test results 

→ exposure 

notifications

Day 8

Day 9

Early symptom onset for 

first 30% of symptomatic 

secondary cases

Estimated 26%* of symptomatic secondary cases 

will still be pre-symptomatic 7 days after getting 

infected — those are the people most likely to 

spread the virus the following weekend

*Based on maximum likelihood estimate of incubation period distribution by Ferretti et al. (2020)
© 2021 THE MITRE CORPORATION. ALL RIGHTS RESERVED. 11

Vision

▪ GAEN could provide users with several different notification types, 

each with appropriate messaging/recommendations:

– Close contact with infected person, low likelihood of transmission

• Inform user of exposure to raise awareness and motivate behavior change

– Close contact with infected person, high likelihood of transmission

• Inform user of exposure, recommend testing and/or self-quarantine

– Attendance at likely super-spreading event

• Inform user of exposure, recommend testing and/or self-quarantine
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Next Steps

▪ Assess the feasibility and efficacy of the new paradigm

– Can it notify enough people early enough to significantly reduce spread?

– How does its precision-recall tradeoff compare to the current paradigm?

▪ Estimate impact on population-level health outcomes and social burden

– Building on Oxford’s open-source agent-based simulation model to include 

super-spreading events, both observed and unobserved factors in viral 

transmission, and explicit representation of contact tracing mechanisms

– Consider new paradigm as alternative and complement to current paradigm

▪ Collaborate with others to reduce the spread of COVID-19

– Explore the technical and operational challenges involved in adapting GAEN 

for super-spreading event detection

– Integrate our changes into Oxford’s open-source repository for public use

– Help inform PHAs in determining messaging around exposure notifications
© 2021 THE MITRE CORPORATION. ALL RIGHTS RESERVED.

Brian Thompson

bthompson@mitre.org
© 2021 THE MITRE CORPORATION. ALL RIGHTS RESERVED.

BACKUP
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Efficacy of GAEN — Study Design

▪ Simulate interactions between infected and susceptible users of GAEN-based 

apps, then use the GAEN risk formula to infer whether transmission occurred

– Transmission model integrates components from established and recent 

literature: infectiousness, emission, transport, and dose-response models

– Consider both observed and unobserved biological and situational factors

▪ Measure efficacy as GAEN’s ability to achieve both high recall (detection rate 

of true transmission events) and high precision (low false alarm/notification rate)

– Evaluate under both ideal and noisy conditions (e.g., attenuation -> distance)

Source: Chen et al. (2020). “Short-range airborne route dominates exposure 

of respiratory infection during close contact”. Building and Environment.
© 2021 THE MITRE CORPORATION. ALL RIGHTS RESERVED. 16

Efficacy of GAEN — Study Conclusions

▪ Fundamental limit on GAEN’s ability to accurately predict transmission

Detecting 50% of transmissions means that more than 90% of exposure 

notifications will be false alarms — even if GAEN perfectly infers distance, 

duration, and days — which may drive down app usage and compliance

▪ New solutions must break out of the current paradigm if a more favorable 

precision-recall tradeoff is to be achieved

Precise formula using 

observable factors 

captured by GAEN

GAEN under ideal 

conditions, using 

recommended 

configuration

GAEN under noisy 

conditions, using 

recommended 

configuration

Manual contact 

tracing, assumes 

perfect memory 

and doesn’t work 

for strangers
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Efficacy of GAEN — Transmission Factors Considered

distance

duration

days after 

symptom 

onset

GAEN MITRE model

incubation 

period

peak viral load

emission rate of 

aerosols during 

normal speech

loudness 

of speech

exercise level
© 2021 THE MITRE CORPORATION. ALL RIGHTS RESERVED. 18

Efficacy of GAEN — Impact of Transmission Factors

Transmission Factor Used by GAEN? How to infer Accuracy Impact on effectiveness

Who 

(person) No

Only evident in 

retrospect if many 

infections result

Moderate with data on 

forward-traced infections, 

low otherwise

High — maximum amount of virus transmitted 

can vary by multiple orders of magnitude across 

individuals [Jacot et al. (2020), Asadi et al. (2020)]

What 

(activity) No Assumption based on environment

Moderate with location 

data (bar vs. restaurant 

vs. train), low otherwise

High — amount of virus transmitted can vary by over 

two orders of magnitude from breathing to singing or 

shouting [Morawska et al. (2009), Asadi et al. (2020)]

Where

(environment) No

Location data, 

RSSI/sensors for 

indoor/outdoor

High with location data, 

moderate with sensor 

data, low otherwise

Moderate — size of space is helpful for identifying 

super-spreading events, less important for tracing 

individual contact events except for indoor/outdoor

When 

(time) Yes

date of self-report 

or + test, proxy for 

symptom onset

Moderate if infected 

person is symptomatic, 

low otherwise

High — amount of viable virus transmitted decreases 

by multiple orders of magnitude if more than 1-3 

days before or after the time of peak infectiousness

How close/crowded 

(proximity) Yes

Bluetooth RSSI 

and/or other 

sensor data

High with sensor data, 

moderate with RSSI only

Moderate — high accuracy is helpful for tracing 

individual contacts; moderate accuracy is probably 

sufficient for identifying super-spreading events

How long 

(duration) Yes

Timestamps of 

scans when ID 

appeared

High (within 5 minutes)

Moderate — amount of virus transmitted grows 

between linearly and quadratically with duration, 

depending on ventilation rate and other factors
© 2021 THE MITRE CORPORATION. ALL RIGHTS RESERVED. 19

Measuring Impact — Integration with Oxford model *

▪ What is the Oxford model?

– Population-level agent-based simulation model capturing demographics, 

person-to-person interactions, virus spread, NPIs, and health outcomes

– Developed at Oxford Big Data Institute, published in Science, open-source

▪ Proposed augmentations to the Oxford model:

– Group interactions — many people in an enclosed space for a prolonged 

period of time (bars, public transit, etc.); “room” model vs. “plume” model; 

enables modeling of super-spreader events

– Contact tracing apps — higher-fidelity model enables comparison of 

effectiveness of different exposure risk inference algorithms

* Hinch et al. (2020). “COVID-19 Agent-based Model With Instantaneous Contact Tracing”.

Open-source code repository: https://github.com/BDI-pathogens/OpenABM-Covid19
© 2021 THE MITRE CORPORATION. ALL RIGHTS RESERVED.

Measuring Impact — Integration with Oxford model