Social Media Sensors to Detect Early Warnings of Influenza at Scale

Detecting early signs of an outbreak in a viral process is challenging due to its exponential nature, yet crucial given the benefits to public health it can provide. If available, the network structure where infection happens can provide rich information about the very early stages of viral outbreaks. For example, more central nodes have been used as social network sensors in biological or informational diffusion processes to detect early contagious outbreaks. Here we aim to put together both approaches to detect early warnings of a biological viral process (influenza-like illness, ILI), using its informational epidemic coverage in public social media. We use a large social media dataset covering three years in a country. We demonstrate that it is possible to use highly central users on the platform, more precisely high out-degree users from Twitter, as sensors to detect the early warning outbreaks of ILI in the physical world without monitoring the whole population. We also investigate other behavioral and content features that distinguish those early sensors in social media beyond centrality. We find that while high centrality on Twitter is the most distinctive feature of sensors, we see that they are more likely to talk about local news, language, politics, or government than the rest of the users. Our new approach could detect a better and smaller set of social sensors for epidemic outbreaks and is more operationally efficient and privacy respectful than previous ones, not requiring the collection of vast amounts of data. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement E.M. acknowledges support by Ministerio de Ciencia e Innovacion/Agencia Espanola de Investigacion (MCIN/AEI/10.13039/501100011033) through grant PID2019-106811GB-C32. M.C. was supported by the Ministry of Universities of the Government of Spain, under the program ''Convocatoria de Ayudas para la recualificacion del sistema universitario espanol para 2021-2023, de la Universidad Carlos III de Madrid, de 1 de Julio de 2021'' ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study used (or will use) ONLY openly available human data that were originally located at Twitter and Instituto Carlos III de Salud. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced are available online at * ABM : Agent based model, EWES : Early warning epidemiological systems, ILI : Influenza-like illness, IPTC : International Press Telecommunications Council, NLP : Natural Language Processing, SIR : Susceptible-infected-recovery.