Lourenço José, Tennant Warren, Faria Nuno R, Walker Andrew, Gupta Sunetra, Recker Mario
Department of Zoology University of Oxford Oxford UK.
Centre for Mathematics and the Environment University of Exeter Penryn UK.
Evol Appl. 2017 Nov 5;11(4):516-533. doi: 10.1111/eva.12554. eCollection 2018 Apr.
The dengue virus is now the most widespread arbovirus affecting human populations, causing significant economic and social impact in South America and South-East Asia. Increasing urbanization and globalization, coupled with insufficient resources for control, misguided policies or lack of political will, and expansion of its mosquito vectors are some of the reasons why interventions have so far failed to curb this major public health problem. Computational approaches have elucidated on dengue's population dynamics with the aim to provide not only a better understanding of the evolution and epidemiology of the virus but also robust intervention strategies. It is clear, however, that these have been insufficient to address key aspects of dengue's biology, many of which will play a crucial role for the success of future control programmes, including vaccination. Within a multiscale perspective on this biological system, with the aim of linking evolutionary, ecological and epidemiological thinking, as well as to expand on classic modelling assumptions, we here propose, discuss and exemplify a few major computational avenues-real-time computational analysis of genetic data, phylodynamic modelling frameworks, within-host model frameworks and GPU-accelerated computing. We argue that these emerging approaches should offer valuable research opportunities over the coming years, as previously applied and demonstrated in the context of other pathogens.
登革病毒是目前影响人类的传播最广泛的虫媒病毒，在南美洲和东南亚造成了重大的经济和社会影响。城市化进程的加快和全球化，加上控制资源不足、政策误导或缺乏政治意愿，以及其蚊虫媒介的扩散，是迄今为止干预措施未能遏制这一重大公共卫生问题的部分原因。计算方法已经阐明了登革病毒的种群动态，目的不仅是更好地了解病毒的进化和流行病学，而且是提供强有力的干预策略。然而，很明显，这些方法不足以解决登革病毒生物学的关键方面，其中许多方面将对包括疫苗接种在内的未来控制计划的成功起到关键作用。在对这个生物系统的多尺度视角下，为了将进化、生态和流行病学思维联系起来，并扩展经典建模假设，我们在此提出、讨论并举例说明一些主要的计算途径——遗传数据的实时计算分析、系统发育动力学建模框架、宿主内模型框架和GPU加速计算。我们认为，这些新兴方法在未来几年应该会提供有价值的研究机会，就像之前在其他病原体的背景下应用和证明的那样。
