Doctoral Dissertation Research: Using Phylogeography To Understand the Spatiotemporal Clustering of Dengue Cases in Bangkok

地方性环境中传染病的时空动态特征往往很差。 在感染不断发生的情况下，很难阐明传播链和病原体在空间的移动。 生物地理学领域提供了一个潜在的解决方案。 通过将感染者的时空位置与感染病原体的基因序列相结合，可以在病毒在一个地区移动时跟踪单个病毒谱系。 这些信息对于建立反应和理解创造和维持病原体多样性的机制非常有用。本博士论文研究项目将使用地理编码的家庭地址和感染血清型的6,659例患者谁被诊断为登革热的四种血清型之一，在曼谷医院在1995年和2010年之间。 此外，将对2006年以来的一部分病例进行感染病毒基因组测序。 博士生将使用适应的时空统计数据分析登革热病例之间的时空依赖性，以了解来自同一病毒谱系的病例在距离和空间上的相互影响。 此外，还将对病例在时间滞后期间的聚集行为进行表征，以描述在小空间尺度上随时间变化的空间依赖性模式，这种模式可能通过特定类型的社区免疫效应引起。登革热是一种潜在的威胁生命的蚊媒病毒性疾病，每年在地球仪各地造成至少36，000，000例有症状的病例。 通过了解感染同一谱系病毒的个体的时空聚集，该项目将深入了解可能推动疾病传播的因素。 此外，了解传播相关病例发生的空间和时间范围将有助于确定有感染风险的人群，并将为在发现指示病例后对邻近家庭喷洒杀虫剂的空间距离提供指导。 该项目将提供一个机会，以了解疾病生态的潜在驱动因素，在一个大的地方性城市环境，已成为登革热感染的关键位置。 在这个项目中开发的方法将推广到其他时空点模式，其中有异质性的标签附加到点（如基因型或物种）或在动态系统中，有变化的模式在底层时空依赖。 作为博士论文研究改进奖，该奖项还将提供支持，使有前途的学生建立一个强大的独立的研究生涯。

The spatiotemporal dynamics of infectious diseases in endemic settings often are poorly characterized. In settings where infections are continuously occurring, it is difficult to elucidate transmission chains and the movement of pathogens in space. The field of phylogeography presents a potential solution. By combining the spatiotemporal location of individuals who become infected with the genetic sequence of the infecting pathogen, it is possible to track individual viral lineages as they move through a region. This information can be extremely useful for mounting responses and for understanding the mechanisms that create and maintain pathogen diversity. This doctoral dissertation research project will use geocoded home addresses and infecting serotypes of 6,659 patients who were diagnosed with one of the four serotypes of dengue fever at a Bangkok hospital between 1995 and 2010. In addition, the genome of the infecting virus will be sequenced for a subset of cases from 2006. The doctoral student will analyze spatiotemporal dependence between dengue cases using adapted space-time statistics to understand how far, both in distance and space, cases from the same viral lineage tend to occur from each other. Furthermore, the clustering behavior of cases across time lags will be characterized to describe the changing patterns of spatial dependence at small spatial scales through time that could be induced through serotype-specific community immunity effects.Dengue fever is a potentially life-threatening mosquito-borne viral disease that causes at least 36,000,000 symptomatic cases every year across the globe. By understanding the spatiotemporal clustering of individuals infected with viruses from the same lineage, this project will provide insight into what may be driving the dispersal of the disease. Furthermore, understanding the spatial and temporal extent at which transmission-related cases occur will facilitate the identification of populations at risk of infection and will provide guidance regarding the spatial distances at which to implement insecticide spraying of neighboring homes upon detection of an index case. This project will provide an opportunity to understand the potential drivers of disease ecology in a large endemic urban setting that have become the key location for dengue infections. The approaches developed in this project will be generalizable to other spatiotemporal point patterns for which there are heterogeneities in the labels attached to points (such as genotype or species) or in dynamic systems where there are changing patterns in underlying spatiotemporal dependence. As a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career.