Miscommunication in the age of communication: A crowdsourcing framework for symptom surveillance at the time of pandemics. (July 2021) - M. Zolbanin, Hamed; Hassan Zadeh, Amir; Davazdahemami, Behrooz
Highlights: We illustrate that a complete list of COVID-19 symptoms could have been created much earlier than it was done on CDC's website. We propose a high-level framework for symptoms surveillance systems using the intelligence obtained from social media. Crowd-sourced symptom surveillance systems can improve the effectiveness of interventions to control the spread of novel diseases. Abstract: Objective: There was a significant delay in compiling a complete list of the symptoms of COVID-19 during the 2020 outbreak of the disease. When there is little information about the symptoms of a novel disease, interventions to contain the spread of the disease would be suboptimal because people experiencing symptoms that are not yet known to be related to the disease may not limit their social activities. Our goal was to understand whether users' social media postings about the symptoms of novel diseases could be used to develop a complete list of the disease symptoms in a shorter time. Materials and Methods: We used the Twitter API to download tweets that contained 'coronavirus', 'COVID-19', and 'symptom'. After data cleaning, the resulting dataset consisted of over 95, 000 unique, English tweets posted between January 17, 2020 and March 15, 2020 that contained references to the symptoms of COVID-19. We analyzed this data using network and time series methods. Results: We found that a complete list of the symptoms of COVID-19 could have been compiled by mid-March 2020, before most states in the U.S. announced a lockdown and about 75 days earlier than the list was completed on CDC's website. Discussion & Conclusion: We conclude that national and international health agencies should use the crowd-sourced intelligence obtained from social media to develop effective symptom surveillance systems in the early stages of pandemics. We propose a high-level framework that facilitates the collection, analysis, and dissemination of information that are posted in various languages and on different social media platforms about the symptoms of novel diseases.