Global monitoring of the impact of the COVID-19 pandemic through online surveys sampled from the Facebook user base

Abstract

Simultaneously tracking the global impact of COVID-19 is challenging because of regional variation in resources and reporting. Leveraging self-reported survey outcomes via an existing international social media network has the potential to provide standardized data streams to support monitoring and decision-making worldwide, in real time, and with limited local resources. The University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS), in partnership with Facebook, has invited daily cross-sectional samples from the social media platform's active users to participate in the survey since its launch on April 23, 2020. We analyzed UMD-CTIS survey data through December 20, 2020, from 31,142,582 responses representing 114 countries/territories weighted for nonresponse and adjusted to basic demographics. We show consistent respondent demographics over time for many countries/territories. Machine Learning models trained on national and pooled global data verified known symptom indicators. COVID-like illness (CLI) signals were correlated with government benchmark data. Importantly, the best benchmarked UMD-CTIS signal uses a single survey item whereby respondents report on CLI in their local community. In regions with strained health infrastructure but active social media users, we show it is possible to define COVID-19 impact trajectories using a remote platform independent of local government resources. This syndromic surveillance public health tool is the largest global health survey to date and, with brief participant engagement, can provide meaningful, timely insights into the global COVID-19 pandemic at a local scale.

Epidemiology of SARS-CoV-2 Infection in Italy Using Real-World Data: Methodology and Cohort Description of the Second Phase of Web-Based EPICOVID19 Study

“…This present study has some weaknesses, primarily because the online system and voluntary participation suffers from inherent selection bias and generalizability. Similar to other web-based surveys [8,58,59], some of the characteristics of the sample were not adequately representative of the Italian adult population. Indeed, females, younger, healthier, and wealthier people were more represented in the enrolled sample with respect to the general population.…”

Epidemiology of SARS-CoV-2 infection in Italy using real-world data: methodology and cohort description of the second phase of web-based EPICOVID19 study

“…The present study has some weaknesses, primarily because the online system and voluntary participation suffers from inherent selection bias and generalizability. Similarly to other web-based survey [8,58,59], some of the characteristics of the sample were not adequately representative of the Italian adult population. Indeed, females, younger, healthier and wealthier people were proportionally more represented in the enrolled sample than in the general population.…”

Relationship Between COVID-19 Threat Beliefs and Individual Differences in Demographics, Personality, and Related Beliefs

“…Understanding individual differences that contribute to coronavirus disease 2019 (COVID-19) threat beliefs is important for better management of the disease (Bruine de Bruin and Bennett, 2020;Astley et al, 2021). For example, gender differences in both beliefs and compliance with rules were found in Australia, Austria, France, Germany, Italy, New Zealand, the United Kingdom, and the United States (Galasso et al, 2020).…”