Arbovirus transmission dynamics in Fiji and the wider Pacific region

这项研究将对斐济和其他太平洋岛国的蚊子传播的传染病进行统计分析和数学建模。这些疾病是通过伊蚊属传播的，并正在成为一个日益严重的全球健康问题。这些病毒都很容易在这些蚊子丰富的太平洋岛屿上传播。这些岛屿是有价值的案例研究，因为人口较少，与其他国家相对隔绝，因此对入侵病毒的免疫力通常较低。因此，与能够维持地方性传播且分析更加复杂的大型异质人口相比，它们通常经历较短的流行时间。该项目将使用数学模型来研究太平洋地区虫媒病毒疾病传播的动态，这些病毒如何相互作用，以及疾病传播在过去十年中发生了怎样的变化。到目前为止，有几项研究审查了该区域个别疾病的传播情况，但这些研究的应用范围有限。因此，这项研究提供了测试当前模型的机会，并通过结合整个区域的数据源来提高其准确性。这项研究的重点是将现有的统计建模方法应用于新数据的量化技能，以及方法的潜在改进。从历史上看，登革热病毒(DENV)在该地区传播，四种血清型中的每一种都需要大约12年的时间才能重新出现，这是因为随着人口的转变，易感人群逐渐积累。但这些周期最近似乎变得越来越短，导致该地区卫生系统的疾病负担加重。此外，最近还爆发了其他虫媒病毒，2011年发现了基孔肯雅病毒(CHIKV)，斐济爆发了寨卡病毒(ZIKV)，首次发现于2015年。对这些疫情的数据进行数学建模，有助于确定一年中传播的风险较高的时期，并突出更易受虫媒病毒传播影响的地理区域。该项目还提供了一个机会，以开发和测试将多种数据来源与数学模型相结合的方法。由于公共卫生监测系统负担过重，以及大量无法检测到的沉默无症状传播，太平洋地区虫媒病毒暴发的数据变得复杂。为了更好地了解虫媒病毒传播的真正负担，于2013年、2015年和2017年从斐济的参与者那里收集了纵向血清学数据。然后，可以将流行前和流行后的血清与监测数据和病毒序列数据结合起来，以分析该岛的感染程度。这证明了数学建模的潜力，可以组合多个不同的数据源，以更好地了解虫媒病毒如何传播。关键词：虫媒病毒、数学模型、抗体动力学、血清学、监测

This research will perform statistical analysis and mathematical modelling of infectious diseases transmitted by mosquitoes in Fiji and other Pacific island countries. The diseases in question are transmitted by the Aedes genus of mosquitoes and are becoming an increasing global health problem. These viruses can all spread easily in the Pacific islands where these mosquitoes are abundant. These islands are valuable case studies because the small populations are relatively isolated from other countries so typically have lower levels of immunity to an invasive virus. As a result, they usually experience short epidemics compared to large, heterogeneous populations which can sustain endemic transmission and is more complicated to analyse. This project will use mathematical modelling to study the dynamics of arbovirus disease transmission in the Pacific, how these viruses interact and how disease transmission has changed over the past decade. Several studies have examined the spread of individual diseases in the region so far but their application have had limited scope in application. This research therefore offers the opportunity to test current models and improve on their accuracy by combining data sources from across the region. This research is focused on quantitative skills with the application of existing statistical modelling methods to new data, and the potential improvement of methods. Historically, dengue virus (DENV) circulates in the region with each of the four serotypes taking approximately 12 years to reappear due to the gradual accumulation of susceptible individuals as the population turns over. But these cycles seem to be getting shorter recently leading to a greater disease burden for health systems across the region. In addition, there have been outbreaks of other arboviruses recently with Chikungunya virus (CHIKV) detected in 2011 and there was an outbreak of Zika virus (ZIKV) in Fiji, first detected in 2015. Applying mathematical modelling to data from these outbreaks can help identify riskier periods for transmission during the year and highlight geographic areas more vulnerable to the spread of arboviruses.This project also affords an opportunity to develop and test methods that combine multiple data sources with mathematical models. Data on arbovirus outbreaks in the Pacific is complicated by overburdened public health surveillance systems, and the abundance of silent asymptomatic transmission that cannot be detected. To better capture the true burden of arbovirus transmission, longitudinal serological data were collected from participants in Fiji in 2013, 2015 and 2017. Pre and post epidemic sera can then be combined with surveillance data and viral sequence data to analyse the extent of infection on the island. This demonstrates the potential of mathematical modelling to combine multiple, disparate data sources to better understand how arboviruses transmit. Keywords: arbovirus, mathematical modelling, antibody dynamics, serology, surveillance