The spread of information and other stochastic processes in biology: epidemics, misinformation and collective behaviour.

非药物干预，如戴口罩和社会距离，是应对新发大流行疾病传播的重要工具。如果认真执行和严格遵守，它们可以用来减缓疫情的进展或完全遏制疫情。社交疏远策略的有效性在很大程度上取决于个人的行为；他们有多认真地听从建议，以及他们在多大程度上鼓励其他人也这样做。在文献中，行为的变化源于个人的意识。这种意识是通过联系传播信息来实现的。重要的是，行为的变化和疾病本身的发展之间存在反馈。此外，意识的传播在生物学和社会中的许多复杂系统中具有重要意义。不同之处在于意识如何反馈到其他动态中。在生态学中，动物可能会提醒群落的其他成员注意入侵者或捕食者的存在--在这种情况下，意识到的个体会变得不那么脆弱--或者传递关于食物来源位置的知识--提高那些与之分享信息的人的存活率。此外，随着人们对问题的了解越来越多，公众对政治问题的舆论可能会发生变化。近年来，我们看到虚假信息的传播越来越多，这些虚假信息旨在影响人们对公众人物、政治问题和健康问题--如气候变化和疫苗接种--的想法和观点。因此，开发一种识别虚假信息的方法和有效的控制措施来阻止错误信息的传播具有重要的意义。我的研究项目是研究信息在生物复杂系统中的传播过程及其反馈。到目前为止，对于一个由信任和不信任的个体组成的人群，我们使用了一个关于流行病的意识和行为变化的反馈模型，我们已经证明了参数空间区域的存在，在这些区域中疾病可以被抑制或仅仅是减轻。我们还研究了信息在网络中传播的随机过程，得到了理论和数值结果，这些结果推动了本课题的进一步研究。为了扩展到目前为止所做的研究，我们的目标是包括其他信息动力学，使模型更现实。这些可能包括褪色和刷新机制，以解释记忆丧失和信息生成。由于模型的复杂性，可能有必要考虑时间尺度分离框架以获得分析近似值。在这方面，Parsons和Rogers为降低具有快慢动态的系统的流形维度而开展的工作是一种有希望的初始工作工具。此外，我们对错误信息的传播及其对社会的影响感兴趣。我们正在与错误信息和人类行为方面的专家联系，以开发一个现实的模型，说明人们如何反应和传播(错误信息)。蒂姆·罗杰斯的专业知识将对在图形和网络上分析模型具有重要意义，而Kit Yates在随机建模和模拟方面的经验将是该项目成功的基础。

Non-pharmaceutical interventions, such as mask-wearing and social distancing, are important tools to tackle the spread of emergent pandemic disease. They can be used to slow the progress of an outbreak or suppress it completely if carefully implemented and strictly adhered to. Much of the effectiveness of social distancing strategies rely upon the behaviours of individuals; how conscientiously they follow the advice and how successfully they encourage others to do the same. In the literature, the changes in behaviour arise from the awareness of individuals. This awareness is modelled through the spread of information through contacts. Importantly, there is feedback between changes in behaviour and the progression of the disease itself.Besides that, the spread of awareness has important implications in many complex systems in biology and society. The differences are how the awareness feeds back into other dynamics. In ecology, animals might alert the rest of the community of the presence of invaders or predators - in which case aware individuals would become less vulnerable - or transmit knowledge on food sources' location - increasing the survival rate of those with whom the information has been shared. Furthermore, public opinion regarding a political issue might change as people become more informed about the problem. In recent years, we have seen an increase in the spread of false information designed to influence people's thoughts and views concerning public figures, political issues and health matters - such as climate change and vaccination. Consequently, it is of fundamental interest to develop a means of identifying false information and effective control measures to hinder the transmission of misinformation.My research project concerns the study of information spread processes and their feedback into biological complex systems. So far, using a model for the feedback of awareness and behaviour change on epidemics for a population comprised of trusting and distrusting individuals, we have shown the existence of regions of parameter space in which the disease can be suppressed or merely mitigated. We also studied the stochastic process of information spread in networks and obtained theoretical and numerical results that motivate further work on the topic.To extend the research done so far, we aim to include other information dynamics to make the model more realistic. These might include fading and refreshing mechanisms to account for memory loss and information generation. Due to the complexities of the model, it might be necessary to consider timescale separation frameworks to obtain analytical approximations. In that regard, the work developed by Parsons and Rogers to reduce the manifold dimension for systems with fast-slow dynamics is a prospective tool for initial work. Additionally, we are interested in the spread of misinformation and its effect on society. We are getting in contact with experts on misinformation and human behaviour to develop a realistic model of how people react to and spread (mis-)information. Tim Rogers' expertise will be significant to the analysis of the model on graphs and networks, whilst Kit Yates' experience with stochastic modelling and simulations will be fundamental for the success of the project.