Spatio-temporal modelling and analysis of bacterial and viral infections

这个项目将从调查一个关于退伍军人症的实际问题开始，退伍军人病是一种相对罕见但有充分记录的疾病。监测数据的季节性趋势将被考虑建立在以前区域工作的基础上(Rickketts天气模式和退伍军人病：气象研究|流行病学和感染|剑桥核心和Sansom等人的集群检测工具，一个应用于退伍军人病的病例关联集群检测和可视化工具-PubMed(nih.gov))，使用不同的统计技术和最新的流行病学和气象数据。人们对感染过程或天气的影响的关注有限，超出了方便/容易获取数据集的范围。在回顾统计方法和数据科学技术的同时，数学建模工作将考虑感染的机制应通知天气变量进行考虑。在此基础上，该项目将审议替代疾病的应用和关于病例定义的监测的具体问题。

This project will begin with investigating a practical question on Legionnaires disease, a relatively rare but well documented disease. Seasonal trends in surveillance data will be considered building on previous work in area (Rickketts Weather patterns and Legionnaires' disease: a meteorological study | Epidemiology & Infection | Cambridge Core and Sansom et al Cluster Detection tool, A case-association cluster detection and visualisation tool with an application to Legionnaires' disease - PubMed (nih.gov)) using different statistical techniques and up to date epidemiological and meteorological data. Limited attention has been drawn to the process of infection or the impact of weather beyond convenient/easy to access data sets. In tandem with a review of statistical methods and data science techniques, mathematical modelling work will consider the mechanisms of infection should inform the weather variables to consider. Building on this the project will consider application to alternative diseases and specific question on surveillance around case definitions.