As COVID-19 spreads throughout the world without a straightforward treatment or widespread vaccine coverage in the near future, mathematical models of disease spread and of the potential impact of mitigation measures have been thrust into the limelight. With their popularity and ability to disseminate information relatively freely and rapidly, information from social media platforms offers a user-generated, spontaneous insight into users’ minds that may capture beliefs, opinions, attitudes, intentions and behaviour towards outbreaks of infectious disease not obtainable elsewhere. The interactive, immersive nature of social media may reveal emergent behaviour that does not occur in engagement with traditional mass media or conventional surveys. In recognition of the dramatic shift to life online during the COVID-19 pandemic to mitigate disease spread and the increasing threat of further pandemics, we examine the challenges and opportunities inherent in the use of social media data in infectious disease modelling with particular focus on their inclusion in compartmental models.

随着 COVID-19 在不久的将来在没有直接治疗或广泛疫苗覆盖的情况下在世界范围内传播，疾病传播的数学模型和缓解措施的潜在影响已成为人们关注的焦点。凭借其受欢迎程度和相对自由和快速地传播信息的能力，来自社交媒体平台的信息提供了用户生成的、对用户思想的自发洞察力，可以捕捉到其他地方无法获得的对传染病爆发的信念、意见、态度、意图和行为. 社交媒体的互动、身临其境的性质可能会揭示在传统大众媒体或传统调查中不会发生的紧急行为。