@article{fdi:010094407,
  title = {{I}nferring human behavior through online social networks may provide accurate behavioral estimates for outbreak forecasting of arboviruses},
  author = {{J}ourdain, {F}. and {C}hakraborty, {D}ebapriyo and {G}aillard, {B}{\'e}atrice and {G}autier, {A}. and {S}imard, {F}r{\'e}d{\'e}ric and {R}obert, {P}. {J}. and {D}ormont, {L}. and {D}esenclos, {J}. {C}. and {R}oche, {B}enjamin},
  editor = {},
  language = {{ENG}},
  abstract = {{H}uman behavior is known to be a fundamental, yet often neglected, component of infectious disease epidemiology, especially during outbreaks. {T}o quantify its role and fluctuations, analyzing message contents on popular online social networks - part of so-called digital epidemiology - is a promising approach. {H}owever, such methods could be biased and generate estimation errors since social media users may not be representative of the general population. {T}o address this, we systematically compared social media-derived estimates with those obtained from a large-scale opinion survey. {I}n the setting of metropolitan {F}rance, where the risk of arbovirus outbreaks is increasingly important, we compared the frequency of three types of emotional states related to human-mosquito contact identified in 160,000 messages on {X} (formerly {T}witter) with the frequency of the same emotional states expressed through a large-scale opinion survey involving 15,000 people during the same period. {B}oth sources of data were used to parametrize a mathematical model of mosquito-borne virus transmission. {W}e found that estimates of these emotional states for different age groups in the opinion survey could be highly different from estimates based on {X} data. {N}evertheless, by integrating demographic adjustments and incorporating variability into our transmission models, we showed that the predicted overall outbreak dynamics remain comparable under certain conditions. {T}his study provides the first evidence that using digital social network data to infer epidemiologically relevant behavior achieves similar results as using large-scale opinion survey data. {T}hese outcomes highlight that {X} data could be used to help forecast outbreaks dynamics, opening new opportunities for real-time assessment of human health-related behavior and the definition of control strategies.},
  keywords = {{FRANCE}},
  booktitle = {},
  journal = {{PL}o{S} {G}lobal {P}ublic {H}ealth},
  volume = {5},
  numero = {7},
  pages = {e0004889 [12 p.]},
  year = {2025},
  DOI = {10.1371/journal.pgph.0004889},
  URL = {https://www.documentation.ird.fr/hor/fdi:010094407},
}