Epidemics on

Social media sensors as early signals of influenza outbreaks at scale

Mon, 15 Jul 2024 00:00:00 +0000

Authors: David Martín-Corral, Manuel García-Herranz, Manuel Cebrián & Esteban Moro
Publication: EPJ Data Science (2024) 13:43 LINK

Abstract: Detecting early signals 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. We aim to combine both approaches to detect early signals 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 social media, more precisely high out-degree users from Twitter, as sensors to detect the early signals of ILI outbreaks 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. While high centrality on Twitter is the most distinctive feature of sensors, 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.

Media

Social Media Sensors to Detect Early Warnings of Influenza at Scale

Fri, 18 Nov 2022 00:00:00 +0000

Authors: David Martín-Corral, Manuel García-Herranz, Manuel Cebrian, Esteban Moro
Publication: medRxiv 2022.11.15.22282355 LINK

Abstract: 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.

Quantifying the importance and location of SARS-CoV-2 transmission events in large metropolitan areas

Mon, 13 Jun 2022 00:00:00 +0000

Authors: Alberto Aleta, David Martín-Corral, Michiel A.Bakker, Ana Pastore y Piontti, Marco Ajelli, Maria Litvinova , Matteo Chinazzi, Natalie E. Dean, M. Elizabeth Halloran,Ira M. Longini,Jr, Alex Pentland, Alessandro Vespignani, Yamir Moreno, and Esteban Moro. Publication: PNAS (2022). Link

Abstract: Detailed characterization of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission across different settings can help design less disruptive interventions. We used real-time, privacy-enhanced mobility data in the New York City, NY and Seattle, WA metropolitan areas to build a detailed agent-based model of SARS-CoV-2infection to estimate the where, when, and magnitude of transmission events during thepandemic’s first wave. We estimate that only 18% of individuals produce most infections (80%), with about 10% of events that can be considered superspreading events (SSEs).Although mass gatherings present an important risk for SSEs, we estimate that the bulk of transmission occurred in smaller events in settings like workplaces, grocery stores, or food venues. The places most important for transmission change during the pandemic and are different across cities, signaling the large underlying behavioral component underneath them. Our modeling complements case studies and epidemiological data and indicates that real-time tracking of transmission events could help evaluate and define targeted mitigation policies.

Modeling the impact of social distancing, testing, contact tracing and household quarantine on second-wave scenarios of the COVID-19 epidemic

Wed, 27 May 2020 00:00:00 +0000

Authors: Alberto Aleta, David Martíın-Corral, Ana Pastore y Piontti, Marco Ajelli, Maria Litvinova, Matteo Chinazzi, Natalie E. Dean, M. Elizabeth Halloran, Ira M. Longini, Jr., Stefano Merler, Alex Pentland, Alessandro Vespignani, Esteban Moro & Yamir Moreno
Publication: Nature Human Behavior(2020). Link

Abstract: While severe social-distancing measures have proven effective in slowing the coronavirus disease 2019 (COVID-19) pandemic, second-wave scenarios are likely to emerge as restrictions are lifted. Here we integrate anonymized, geolocalized mobility data with census and demographic data to build a detailed agent-based model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission in the Boston metropolitan area. We find that a period of strict social distancing followed by a robust level of testing, contact-tracing and household quarantine could keep the disease within the capacity of the healthcare system while enabling the reopening of economic activities. Our results show that a response system based on enhanced testing and contact tracing can have a major role in relaxing social-distancing interventions in the absence of herd immunity against SARS-CoV-2.

Media:

Modeling the impact of testing, tracing, and quarantine, MIT News, September 8, 2020
Measuring the Effectiveness of Coronavirus Social Distancing Policies, Wall Street Journal, April 10, 2020
More waves of virus cases could follow the first, health experts warn, The Dallas Morning News, April 10, 2020
Un estudio muestra que los test sin rastreo eficaz de contactos son insuficientes para frenar la oleada, El Pais, August 12, 2020
The effectiveness of social distancing strategies in the face of an epidemic Medical Xpress, March 27, 2020