Consistently estimating network statistics using aggregated relational data.

Collecting complete network data is expensive, time-consuming, and often infeasible. Aggregated Relational Data (ARD), which ask respondents questions of the form "How many people with trait X do you know?" provide a low-cost option when collecting complete network data is not possible. Rather than asking about connections between each pair of individuals directly, ARD collect the number of contacts the respondent knows with a given trait. Despite widespread use and a growing literature on ARD methodology, there is still no systematic understanding of when and why ARD should accurately recover features of the unobserved network. This paper provides such a characterization by deriving conditions under which statistics about the unobserved network (or functions of these statistics like regression coefficients) can be consistently estimated using ARD. We first provide consistent estimates of network model parameters for three commonly used probabilistic models: the beta-model with node-specific unobserved effects, the stochastic block model with unobserved community structure, and latent geometric space models with unobserved latent locations. A key observation is that cross-group link probabilities for a collection of (possibly unobserved) groups identify the model parameters, meaning ARD are sufficient for parameter estimation. With these estimated parameters, it is possible to simulate graphs from the fitted distribution and analyze the distribution of network statistics. We can then characterize conditions under which the simulated networks based on ARD will allow for consistent estimation of the unobserved network statistics, such as eigenvector centrality, or response functions by or of the unobserved network, such as regression coefficients.

Effects of a large-scale social media advertising campaign on holiday travel and COVID-19 infections: a cluster randomized controlled trial.

During the Coronavirus Disease 2019 (COVID-19) epidemic, many health professionals used social media to promote preventative health behaviors. We conducted a randomized controlled trial of the effect of a Facebook advertising campaign consisting of short videos recorded by doctors and nurses to encourage users to stay at home for the Thanksgiving and Christmas holidays ( NCT04644328 and AEARCTR-0006821 ). We randomly assigned counties to high intensity (n = 410 (386) at Thanksgiving (Christmas)) or low intensity (n = 410 (381)). The intervention was delivered to a large fraction of Facebook subscribers in 75% and 25% of randomly assigned zip codes in high- and low-intensity counties, respectively. In total, 6,998 (6,716) zip codes were included, and 11,954,109 (23,302,290) users were reached at Thanksgiving (Christmas). The first two primary outcomes were holiday travel and fraction leaving home, both measured using mobile phone location data of Facebook users. Average distance traveled in high-intensity counties decreased by -0.993 percentage points (95% confidence interval (CI): -1.616, -0.371; P = 0.002) for the 3 days before each holiday compared to low-intensity counties. The fraction of people who left home on the holiday was not significantly affected (adjusted difference: 0.030; 95% CI: -0.361, 0.420; P = 0.881). The third primary outcome was COVID-19 infections recorded at the zip code level in the 2-week period starting 5 days after the holiday. Infections declined by 3.5% (adjusted 95% CI: -6.2%, -0.7%; P = 0.013) in intervention compared to control zip codes. Social media messages recorded by health professionals before the winter holidays in the United States led to a significant reduction in holiday travel and subsequent COVID-19 infections.

Effect of Physician-Delivered COVID-19 Public Health Messages and Messages Acknowledging Racial Inequity on Black and White Adults' Knowledge, Beliefs, and Practices Related to COVID-19: A Randomized Clinical Trial.

Importance: Social distancing is critical to the control of COVID-19, which has disproportionately affected the Black community. Physician-delivered messages may increase adherence to these behaviors. Objectives: To determine whether messages delivered by physicians improve COVID-19 knowledge and preventive behaviors and to assess the differential effectiveness of messages tailored to the Black community. Design, Setting, and Participants: This randomized clinical trial of self-identified White and Black adults with less than a college education was conducted from August 7 to September 6, 2020. Of 44 743 volunteers screened, 30 174 were eligible, 5534 did not consent or failed attention checks, and 4163 left the survey before randomization. The final sample had 20 460 individuals (participation rate, 68%). Participants were randomly assigned to receive video messages on COVID-19 or other health topics. Interventions: Participants saw video messages delivered either by a Black or a White study physician. In the control groups, participants saw 3 placebo videos with generic health topics. In the treatment group, they saw 3 videos on COVID-19, recorded by several physicians of varied age, gender, and race. Video 1 discussed common symptoms. Video 2 highlighted case numbers; in one group, the unequal burden of the disease by race was discussed. Video 3 described US Centers for Disease Control and Prevention social distancing guidelines. Participants in both the control and intervention groups were also randomly assigned to see 1 of 2 American Medical Association statements, one on structural racism and the other on drug price transparency. Main Outcomes and Measures: Knowledge, beliefs, and practices related to COVID-19, demand for information, willingness to pay for masks, and self-reported behavior. Results: Overall, 18 223 participants (9168 Black; 9055 White) completed the survey (9980 [55.9%] women, mean [SD] age, 40.2 [17.8] years). Overall, 6303 Black participants (34.6%) and 7842 White participants (43.0%) were assigned to the intervention group, and 1576 Black participants (8.6%) and 1968 White participants (10.8%) were assigned to the control group. Compared with the control group, the intervention group had smaller gaps in COVID-19 knowledge (incidence rate ratio [IRR], 0.89 [95% CI, 0.87-0.91]) and greater demand for COVID-19 information (IRR, 1.05 [95% CI, 1.01-1.11]), willingness to pay for a mask (difference, $0.50 [95% CI, $0.15-$0.85]). Self-reported safety behavior improved, although the difference was not statistically significant (IRR, 0.96 [95% CI, 0.92-1.01]; P = .08). Effects did not differ by race (F = 0.0112; P > .99) or in different intervention groups (F = 0.324; P > .99). Conclusions and Relevance: In this study, a physician messaging campaign was effective in increasing COVID-19 knowledge, information-seeking, and self-reported protective behaviors among diverse groups. Studies implemented at scale are needed to confirm clinical importance. Trial Registration: ClinicalTrials.gov Identifier: NCT04502056.

Doctors' and Nurses' Social Media Ads Reduced Holiday Travel and COVID-19 infections: A cluster randomized controlled trial in 13 States.

During the COVID-19 epidemic, many health professionals started using mass communication on social media to relay critical information and persuade individuals to adopt preventative health behaviors. Our group of clinicians and nurses developed and recorded short video messages to encourage viewers to stay home for the Thanksgiving and Christmas Holidays. We then conducted a two-stage clustered randomized controlled trial in 820 counties (covering 13 States) in the United States of a large-scale Facebook ad campaign disseminating these messages. In the first level of randomization, we randomly divided the counties into two groups: high intensity and low intensity. In the second level, we randomly assigned zip codes to either treatment or control such that 75% of zip codes in high intensity counties received the treatment, while 25% of zip codes in low intensity counties received the treatment. In each treated zip code, we sent the ad to as many Facebook subscribers as possible (11,954,109 users received at least one ad at Thanksgiving and 23,302,290 users received at least one ad at Christmas). The first primary outcome was aggregate holiday travel, measured using mobile phone location data, available at the county level: we find that average distance travelled in high-intensity counties decreased by -0.993 percentage points (95% CI -1.616, -0.371, p -value 0.002) the three days before each holiday. The second primary outcome was COVID-19 infection at the zip-code level: COVID-19 infections recorded in the two-week period starting five days post-holiday declined by 3.5 percent (adjusted 95% CI [-6.2 percent, -0.7 percent], p -value 0.013) in intervention zip codes compared to control zip codes. ONE SENTENCE SUMMARY: In a large scale clustered randomized controlled trial, short messages recorded by health professionals before the winter holidays in the United States and sent as ads to social media users led to a significant reduction in holiday travel, and to a decrease in subsequent COVID-19 infection at the population level.

Doctors and Nurses Social Media Ads Reduced Holiday Travel and COVID-19 infections: A cluster randomized controlled trial in 13 States.

During the COVID-19 epidemic, many health professionals started using mass communication on social media to relay critical information and persuade individuals to adopt preventative health behaviors. Our group of clinicians and nurses developed and recorded short video messages to encourage viewers to stay home for the Thanksgiving and Christmas Holidays. We then conducted a two-stage clustered randomized controlled trial in 820 counties (covering 13 States) in the United States of a large-scale Facebook ad campaign disseminating these messages. In the first level of randomization, we randomly divided the counties into two groups: high intensity and low intensity. In the second level, we randomly assigned zip codes to either treatment or control such that 75% of zip codes in high intensity counties received the treatment, while 25% of zip codes in low intensity counties received the treatment. In each treated zip code, we sent the ad to as many Facebook subscribers as possible (11,954,109 users received at least one ad at Thanksgiving and 23,302,290 users received at least one ad at Christmas). The first primary outcome was aggregate holiday travel, measured using mobile phone location data, available at the county level: we find that average distance travelled in high-intensity counties decreased by -0.993 percentage points (95% CI -1.616, -0.371, p-value 0.002) the three days before each holiday. The second primary outcome was COVID-19 infection at the zip-code level: COVID-19 infections recorded in the two-week period starting five days post-holiday declined by 3.5 percent (adjusted 95% CI [-6.2 percent, -0.7 percent], p-value 0.013) in intervention zip codes compared to control zip codes.

Interacting regional policies in containing a disease.

Regional quarantine policies, in which a portion of a population surrounding infections is locked down, are an important tool to contain disease. However, jurisdictional governments-such as cities, counties, states, and countries-act with minimal coordination across borders. We show that a regional quarantine policy's effectiveness depends on whether 1) the network of interactions satisfies a growth balance condition, 2) infections have a short delay in detection, and 3) the government has control over and knowledge of the necessary parts of the network (no leakage of behaviors). As these conditions generally fail to be satisfied, especially when interactions cross borders, we show that substantial improvements are possible if governments are outward looking and proactive: triggering quarantines in reaction to neighbors' infection rates, in some cases even before infections are detected internally. We also show that even a few lax governments-those that wait for nontrivial internal infection rates before quarantining-impose substantial costs on the whole system. Our results illustrate the importance of understanding contagion across policy borders and offer a starting point in designing proactive policies for decentralized jurisdictions.

Comparison of Knowledge and Information-Seeking Behavior After General COVID-19 Public Health Messages and Messages Tailored for Black and Latinx Communities : A Randomized Controlled Trial.

BACKGROUND: The paucity of public health messages that directly address communities of color might contribute to racial and ethnic disparities in knowledge and behavior related to coronavirus disease 2019 (COVID-19). OBJECTIVE: To determine whether physician-delivered prevention messages affect knowledge and information-seeking behavior of Black and Latinx individuals and whether this differs according to the race/ethnicity of the physician and tailored content. DESIGN: Randomized controlled trial. (Registration: ClinicalTrials.gov, NCT04371419; American Economic Association RCT Registry, AEARCTR-0005789). SETTING: United States, 13 May 2020 to 26 May 2020. PARTICIPANTS: 14 267 self-identified Black or Latinx adults recruited via Lucid survey platform. INTERVENTION: Participants viewed 3 video messages regarding COVID-19 that varied by physician race/ethnicity, acknowledgment of racism/inequality, and community perceptions of mask wearing. MEASUREMENTS: Knowledge gaps (number of errors on 7 facts on COVID-19 symptoms and prevention) and information-seeking behavior (number of web links demanded out of 10 proposed). RESULTS: 7174 Black (61.3%) and 4520 Latinx (38.7%) participants were included in the analysis. The intervention reduced the knowledge gap incidence from 0.085 to 0.065 (incidence rate ratio [IRR], 0.737 [95% CI, 0.600 to 0.874]) but did not significantly change information-seeking incidence. For Black participants, messages from race/ethnicity-concordant physicians increased information-seeking incidence from 0.329 (for discordant physicians) to 0.357 (IRR, 1.085 [CI, 1.026 to 1.145]). LIMITATIONS: Participants' behavior was not directly observed, outcomes were measured immediately postintervention in May 2020, and online recruitment may not be representative. CONCLUSION: Physician-delivered messages increased knowledge of COVID-19 symptoms and prevention methods for Black and Latinx respondents. The desire for additional information increased with race-concordant messages for Black but not Latinx respondents. Other tailoring of the content did not make a significant difference. PRIMARY FUNDING SOURCE: National Science Foundation; Massachusetts General Hospital; and National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases.

Using Aggregated Relational Data to Feasibly Identify Network Structure without Network Data.

Social network data are often prohibitively expensive to collect, limiting empirical network research. We propose an inexpensive and feasible strategy for network elicitation using Aggregated Relational Data (ARD): responses to questions of the form "how many of your links have trait k ?" Our method uses ARD to recover parameters of a network formation model, which permits sampling from a distribution over node- or graph-level statistics. We replicate the results of two field experiments that used network data and draw similar conclusions with ARD alone.

The diffusion of microfinance.

To study the impact of the choice of injection points in the diffusion of a new product in a society, we developed a model of word-of-mouth diffusion and then applied it to data on social networks and participation in a newly available microfinance loan program in 43 Indian villages. Our model allows us to distinguish information passing among neighbors from direct influence of neighbors' participation decisions, as well as information passing by participants versus nonparticipants. The model estimates suggest that participants are seven times as likely to pass information compared to informed nonparticipants, but information passed by nonparticipants still accounts for roughly one-third of eventual participation. An informed household is not more likely to participate if its informed friends participate. We then propose two new measures of how effective a given household would be as an injection point. We show that the centrality of the injection points according to these measures constitutes a strong and significant predictor of eventual village-level participation.