The Doctors for Coronavirus Prevention Project Thanksgiving / Christmas Messaging Campaign
Phase: NA
Conditions: Covid19
Sponsor: National Bureau of Economic Research, Inc.

Study Protocol and
Statistical Analysis Plan
The Doctors for Coronavirus Prevention
Project Thanksgiving / Christmas
Messaging Campaign
NCT Number: 04644328
Unique Protocol ID: 2003000118A010
Note: This document contains the pre-speciﬁed written
description of the clinical study and statistical
considerations for analyzing the data in the study.
Please note that the study was titled di↵erently in this
document (“COVID-19 Thanksgiving Messaging at
Scale”) as it was unclear for a period whether resources
for the Christmas experiment would be available.

Pre-Analysis Plan
COVID-19 Thanksgiving Messaging at Scale
This Draft: November 25, 2020
1.
Introduction
In prior work, we have found that video messages delivered by doctors increase knowledge of COVID-
19, and in some cases, lead to viewers taking actions consistent with the messaging. In a scale-up
phase, we plan to use Facebook ads to show a 15 second video clip recorded by 6 MGH, Harvard
and Lynn Community health center doctors to approximately 40,000,000 Facebook users. The ads will
be shown between November 14-29 and focus on staying safe – limiting travel, social distancing and
mask-wearing. We are randomizing exposure to the ad campaign at the ZIP code and county level
to study whether the videos change mobility and Thanksgiving holiday travel and whether there are
any detectable impacts on the spread of COVID-19. Using information about Facebook social network
connections and mobility patterns between counties, we also plan to measure whether exposure to the
ad campaign spilled over along the network.
2.
Sample Frame and Treatment
2.1
Treatment
Facebook users will be exposed to an ad campaign featuring a set of videos about Thanksgiving travel.
6 doctors from MGH, Harvard and Lynn Community health center have each recorded one short mes-
sage (15 seconds) using an identical script: “This Thanksgiving, the best way to show your love is to
stay home. If you do visit, wear a mask at all times. I’m Dr. XX from XX, and I’m urging you: don’t
risk spreading COVID. Stay safe, stay home.”
The video messages will be posted to a project Facebook page titled "The Doctors for Coronavirus
Prevention Project". Users will see the ads and the name of the Facebook group associated with the ads
on their feeds. Watching the videos in the ad posts is completely optional for Facebook users.
Within treated geographies, Facebook’s algorithm will be used to place the ads to users 18 and older. We
expect each treated individual to see the ads approximately 3 times over the course of the ad campaign.
1

2.2
Sample Frame
We rolled out the campaign in 2 phases:
• Phase 1:
– 13 states were chosen based on the availability of zip-code level COVID-19 data. We used a
2-stage randomization with both county and zip-code level variation.
– 820 counties
– 6998 zip codes
– Campaign duration: November 14-29
• Phase 2:
– Facebook was interested in scaling up the campaign to 15 additional states with high COVID-
19 case rates and relatively low levels of compliance with preventative behaviors.
– 1069 counties
– Campaign duration: November 23-29
We expect that approximately 40,000,000 Facebook users will be exposed to the videos across the two
phases.
2.3
Treatment Assignment
The experimental design differed across phases.
• Phase 1: 2-stage randomization with both county and zip-code level variation
– 50% of counties in the 13 states assigned to Low Intensity treatment
* 25% of zip codes in the low intensity counties treated (exposed to the campaign)
– 50% of counties in the 13 states assigned to High Intensity treatment
* 75% of zip codes in the high intensity treated (exposed to the campaign)
• Phase 2: 1-stage randomization at the county-level.
– 80% of counties the 15 target states treated (exposured to the campaign)
Treatment intensities were decided based on Facebook’s ad budget constraints.
In the Zip-code level treatments, ad targeting may not be perfect. If individuals do not share their
location data with Facebook, then IP address is used to proxy for geographic location.
2

3.
Hypotheses
Main hypotheses:
1. Do the ads change the behaviors of those in the treated zip areas?
• Do they travel less?
– Within zip
– To places outside of zip? Do they travel shorter distances?
• Do they host fewer visitors?
• Do they wear masks more?
2. Do the ads change the type of interactions that happen?
• Super-spreading interaction (high degree)
• Sneaky interaction (low-degree)
3. Do the ads affect COVID rates and syptoms in treated areas?
4. Do the ads spill over onto other areas that are connected to the treatment ZIPs?
• Behavioral spillovers: travel/mobility
• COVID impacts
• Are spillovers more pronounced on the mobility network or on the Facebook friendship
network?
4.
Empirical Analysis
4.1
Outcomes
1. Mobility and Travel: different outcomes are observable from Facebook at the county and zip code
level due to data privacy concerns:
• Zip code-level:
(a) Fraction of individuals staying in the same geographical location (1mi x 1mi area)
(measured daily). An individual is recorded as staying home if they are in the same
(1mi x 1mi) area at the beginning and the end of an 8 hour interval. The measure
misses people who leave their neighborhood and return within the interval.
(b) Fraction of individuals leaving the geographical location (1mi x 1mi area) each day
(measured daily)
• County-level:
(a) “Stay put” metric: how much of the time are people really staying in their homes?
(measured daily) This measure is much more precise than what is available at the zip
code level. It reﬂects the fraction of the day that is spent in a single 600m x 600m
geographical area. Source.
3

(b) “Change in movement” metric: “We quantify how much people move around by
counting the number of level-16 Bing tiles (600m x 600m) they are seen in within a
day. People seen in more tiles are probably moving around more, while people seen
in fewer are probably moving around less. Each day we take all the eligible people in
a given region and compute the number of distinct tiles they were seen in.” (measured
daily). Source.
(c) Colocation data: Data marks counts of people from county pairs (A,B) who are observed
5 minutes in the same place. It counts this as a share relative to all possible meetings.
(Measured weekly). This gives a county-by-county transition matrix that is symmetric.
(We do not observe where the meeting takes place).
• Timing: We are interested in mobility outcomes for Thanksgiving week. Baseline obser-
vations are based on Facebook data availability. When possible, we will include mobility
measures from other holidays in 2020.
2. Covid 19:
(a) Case rates/hospitalization/deaths:
• Zip: available from local public health departments for the 13 states in the Phase I
experiment.
• County: available from local public health departments, nation-wide.
Available for
download from the New York Times.
(b) Symptoms:
• Facebook COVID-19 symptoms survey. Source.
• Timing: Daily data during the 1 month following Thanksgiving. We began collecting base-
line COVID data at the zip code level on November 11.
3. Intended behaviors and beliefs:
• The data all come from Facebook’s “Brand Lift” tool. In each zip code of the Phase 1 study,
a hold-out sample is randomly selected in target zips to not receive the ad. We can compare
treated and control responses within zip.
• Questions:
(a) Ad recall
(b) Mask intentions
(c) Thanksgiving travel intentions
(d) How much should people try to stay home?
Our primary outcomes are in bold. We are most likely to be able to detect changes on mobility given
the study design. Therefore, mobility is our primary outcome. We are also extremely interested in
impacts on Covid-19 rates and symptoms. However, detecting impacts on Covid-19 is signiﬁcantly
more demanding from a statistical power perspective given that it is relatively rare at the weekly time
horizon. We view the intended behaviors and beliefs outcomes as secondary.
Additional outcomes, data pending:
4

1. Mobility data from Safegraph (zip code level).
2. MGH COVID-19 symptom tracking app data.
3. Stay put, Change in movement, co-location at the zip code level.
4. Dynata survey data on plans to travel for Thanksgiving, sampled from Nov. 13-24
4.2
Regression Analysis
Direct Impacts (Phase 1):
1. Analysis using Zip-level randomization (Covid cases and mobility outcomes):
(a) Direct impacts (pooled): Yzt = at + gc + bTreatzt + dYz0 + #zt
• Yz0 is a baseline measure of the outcome.
• gc denotes either:
– Indicator for High Intensity county
– County ﬁxed effects. For counties with homogenous treatment assignment across
zips, pool them into a larger “county unit” to be able to add FEs without losing
observations.
(b) Direct impacts (disaggregated): Yzt = at + b1Treatzt + b21(HighIntensity)ct + b3Treatzt1(HighIntens
dYz0 + #zt
• b2 is a measure of spillovers, related to the geographic network.
(c) Distributional direct impacts: Quantile regression.
• Repeat the exercises, above, with quantile regressions. Motivation, right tail (super-
spreader) and left tail (sneaky) interactions.
2. Analysis using County-level randomization (Covid symptoms and mobility outcomes):
(a) Direct impacts: Yct = at + b1(HighIntensity)ct + dYc0 + #zt
(b) Distributional direct impacts: Quantile regression.
3. Analysis using Individual-level randomization (“Brand Lift” outcomes)
• We will obtain from FB: Control mean, Treatment mean, number of observations. With this
we will need to construct standard errors.
• 2 methods for inference, using binary outcomes:
– Construct t-tests on equality of treatment and control
– Conduct permutation tests
• We will also be able to test if the individual response is different in Urban versus Rural. FB
will give us means in T and C and number of observations separately for Urban vs. rural.
Direct Impacts (Phase 2):
5

4. Analysis using County-level randomization (Covid symptoms and cases, mobility outcomes):
(a) Direct impacts: Yct = at + as + b1(Treated)ct + dYc0 + #zt
• as are state ﬁxed effects due to stratiﬁcation.
(b) Distributional direct impacts: Quantile regression.
Indirect impacts (Phase 1 and Phase 2 variation):
5. Indirect zip code-level impacts
• Sample-frame = Phase 1 states, but spillovers can come from both phase 1 and phase 2 treat-
ments.
• We only have detailed facebook network information (SCI) at the zip code level (no coloca-
tion data).
(a) Yzt = a + bSCI,TExpPhase1TreatSCI
z
+ bSCI,HIExpPhase1HighIntSCI
z
+ dSCIExpPhase2TreatSCI
z
+
gSCI,1ExpPhase1SampleSCI
z
+ gSCI,2ExpPhase2SampleSCI
z
+ lY0#zt
• Notation:
– SCI is the Facebook friend network, Mobility is the Facebook colocation mobility net-
work.
– Phase1 denotes the ﬁrst treatment phase. Phase2 denotes the second treatment phase.
– Treat denotes whether a connected zip is treated (for Phase 1 and Phase 2).
– HighInt denotes whether a connected zip is in a High Intenstiy county.
– All non-phase 1 and phase 2 zips are marked non-treated
– We will measure exposure (Exp) in 2 different ways:
(a) Share of network connections exposed
(b) Number of network connections exposed
6. Indirect county-level impacts:
• Sample-frame = All states (Phase 1 and Phase 2 states, and non-intervened states), but need
to control for relevant exposure
(a) Yct = a + bSCIExpPhase1TreatSCI
c
+ bMobExpPhase1TreatMobility
c
+ dSCIExpPhase2TreatSCI +
dMobExpPhase2TreatMobility
c
+ gSCI,1ExpPhase1SampleSCI + gMob,1ExpPhase1SampleMobility
c
+
gSCI,2ExpPhase2SampleSCI
c
+ gMob,2ExpPhase2SampleMobility + fSCIDegreeSCI
c
+ fMobDegreeMob
c
+
lY0 + #t
(b) Yct = a + bSCIExpPhase1TreatSCI
c
+ dSCIExpPhase2TreatSCI + gSCI,1ExpPhase1SampleSCI +
gSCI,2ExpPhase2SampleSCI
c
+ fSCIDegreeSCI
c
+ lY0 + #t
(c) Yct = a + bMobExpPhase1TreatMobility
c
+ dMobExpPhase2TreatMobility
c
+ gMob,1ExpPhase1SampleMobility
c
gMob,2ExpPhase2SampleMobility + fMobDegreeMob
c
+ lY0 + #t
6

• Notation:
– SCI is the Facebook friend network, Mobility is the Facebook colocation mobility net-
work.
– Phase1 denotes the ﬁrst treatment phase. Phase2 denotes the second treatment phase.
– Treat denotes whether a connected county is either High Intensity (for Phase 1) or treated
(for phase 2)
– We will measure exposure in 2 different ways:
* Share of network connections exposed
* Number of network connections exposed
Supplemental Analysis:
7. Indirect impacts: (“Link-level” Analysis), using nation-wide county co-location data: Construct
county-pair level analog to the Indirect county-level regressions, above.
8. Test for presence of any spillovers on the network: reference.
9. Analysis using County-level non-random variation
• Goal, use untreated states to generate a “pure control”
– Can be used to measure impacts of the whole campaign.
• Differences-in-differences regression
– Not in sample frame
– Low intensity treatment
– High intensity treatment
• Use standard diff in diff setup (log(cases) on county FE + time FE + treatment * FE.
– Additionally use Synthetic Diff-in-Diff approach: reference.
– Also Consider non-linear Diff-in-Diff to capture extreme covid growth rates: reference.
4.3
Controls
For all regressions, we will use Machine Learning techniques to select the controls. Potential controls
include: Baseline covid rates, Baseline mobility rates, State ﬁxed effects, zip and county-level demo-
graphics from the census, distance to major city, distance to state capitol,...
4.4
Inference
For our core analyses at both the county and zip level, we will use heteroskedasticity-robust standard
errors, as our randomization is at those levels. We will check for additional statistical robustness fol-
lowing the approaches outlined in Section 5 of the Supplementary Appendix of this paper.
7

4.5
Heterogeneous Effects
We will explore heterogeneity on the following dimensions:
• Urban vs. rural ZIPs: (this is what we stratiﬁed on for the brand lift study.) Caveat: ad targeting
is running differently in urban vs. rural, so the “ﬁrst stage” is likely also different.
• Travel restrictions and local government messaging: are PSAs and policy substitutes or comple-
ments? (If substitutes, we get power on the other stuff by dummying them out.)
• COVID rates at origin and destination. Does the campaign discourage travel to/from high covid
places?
• Prior infection rates (e.g. infections in April?)
• Red/Blue counties and zips
• Weather: rain or cold weather on Thanksgiving (or weekend more generally)
5.
Funding and Human Subjects Review
Facebook is supplying sufﬁcient ad credits to fund the campaign. The IRB at MIT is serving as the
primary institution of record and has entered into a reliance agreement with Harvard, Massachusetts
General Hospital, and Yale.
8

Massachusetts Institute of Technolog\
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APPLICATION FOR COMPREHENSIVE REVIEW
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I. BASIC INFORMATION
1. Title of Stud\
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3. Funding
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your application. A draft of the research proposal is acceptable.
Do not leave this section blank. If your project is not funded, check No Funding in section C.
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3. E[perimental Procedures
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by using e.g., appropriate monitoring, safety devices and withdrawal of a subject if there is evidence of
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Serious or unexpected adverse reactions or injuries, and/or unanticipated problems involving risks to
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reported within 10 working days.
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If your study involves individually identifiable health information and is sponsored by MIT Medical, an
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HIPAA likely applies to your study. For more information regarding the applicability of HIPAA to
human subjects research, please click here.
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when use of individually identifiable health information would pose only minimal risk to study
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participants (among other requirements). For additional information regarding whether your study
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the research could not be conducted without the information.
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information that you plan to convert into de-identified information for use by other researchers, then
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limited data set form for use by other researchers, then you must obtain a signed Authorization for
Release of Protected Health Information Form from each participant before receiving their
identifiable health information for use in your study. You must complete a formal data use
agreement in order for your application to be approved.
V.  INVESTIGATOR¶S ASSURANCE
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I certif\ the information provided in this application is complete and correct.
I understand that I have ultimate responsibilit\ for the conduct of the stud\, the ethical
performance of the project, the protection of the rights and Zelfare of human subjects, and strict
adherence to an\ stipulations imposed b\ COUHES
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Massachusetts Institute of Technolog\
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consultants, laboratory technicians and assistants.
All study personnel must be listed below. This listing must include contact information, a brief statement
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Important note: all study personnel are required to complete Human Subject Training before work
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I. MIT AFFILIATES
Personnel name and e-mail address
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