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

非药物干预措施，如戴口罩和保持社交距离，是应对突发大流行疾病传播的重要工具。如果仔细实施并严格遵守，它们可用于减缓疫情的进展，或完全抑制疫情。社交距离策略的有效性在很大程度上取决于个人的行为；他们多么认真地听从别人的建议，又多么成功地鼓励别人也这样做。在文献中，行为的改变源于个人的意识。这种意识是通过接触传播信息而形成的。重要的是，在行为变化和疾病本身的进展之间存在反馈。此外，意识的传播在生物和社会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入侵者或捕食者的存在——在这种情况下，有意识的个体将变得不那么脆弱——或者传递关于食物来源位置的知识——增加那些与之共享信息的人的存活率。此外，公众对政治问题的看法可能会随着人们对问题的了解程度的提高而改变。近年来，我们看到虚假信息的传播有所增加，这些虚假信息旨在影响人们对公众人物、政治问题和卫生问题（如气候变化和疫苗接种）的思想和观点。因此，开发一种识别虚假信息的手段和有效的控制措施以阻止虚假信息的传播是至关重要的。我的研究项目涉及信息传播过程及其反馈到生物复杂系统的研究。到目前为止，我们使用一个模型，对由信任和不信任的个人组成的人口对流行病的认识和行为变化进行反馈，表明存在一些参数空间区域，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，并获得了理论和数值结果，激励了该主题的进一步工作。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态，使模型更现实。这些可能包括消退和刷新机制，以解释记忆丧失和信息生成。由于模型的复杂性，可能需要考虑时间尺度分离框架来获得解析近似。在这方面，由Parsons和Rogers开发的减少具有快慢动力学系统的流形维数的工作是初始工作的一个有前途的工具。此外，我们对错误信息的传播及其对社会的影响很感兴趣。我们正在与错误信息和人类行为方面的专家联系，以建立一个现实的模型，说明人们对（错误）信息的反应和传播。Tim Rogers的专业知识将对图和网络模型的分析具有重要意义，而Kit Yates在随机建模和模拟方面的经验将是项目成功的基础。

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.