Transmission dynamics and molecular epidemiology of arboviruses in Indonesia

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Large scale outbreaks of dengue virus (DENV) and dengue haemorrhagic fever are reported every year in Indonesia. In addition, Chikungunya virus (CHIKV) is endemic in parts of Indonesia, and two positive cases of Zika virus (ZIKV) have been described, one from an Australian who travelled to Indonesia, a second from Jambi province. DENV, CHIKV, and ZIKV are all transmitted by the mosquito Aedes aegypti, and are co-circulating in the same population. They also cause overlapping symptoms in adults, and hence are difficult to differentiate by symptoms alone. Indonesia has a large population - around 250 million people - with around 90% of the population living in areas suitable for the transmission of 'arboviruses' (short for arthropod-borne viruses), and collectively, arboviruses represent a major public health concern in Indonesia. The number of cases varies both geographically and over time, and relatively little is known about the drivers of this variation in Indonesia.DENV, CHIKV, and ZIKV are all RNA viruses, which evolve at a high rate. Consequently, these viruses are genetically variable, and through the application of statistical models to viral sequence data, information on the dynamics of transmission, both over time and space, can be obtained. However, although the number of arbovirus infections per year is high, the number of viral sequences from Indonesia is relatively low, and often, these sequences only span a small part of the viral genome.This project aims to characterise the transmission dynamics of DENV, CHIKV, and ZIKV through viral sequencing of clinical samples from humans. We will harness a network of collaborators to provide archived samples, and prospective studies in locations representing west (Sumatra - Palembang City), central (Kalimantan - Banjarmasin city), and east (Maluku - Ambon city) Indonesia. In addition, we will study outbreaks that may occur during the study period. Using next-generation sequencing platforms, we aim to generate a large dataset of full-length viral genomes that will significantly add to the existing body of viral sequence data currently available.Sequences of DENV and CHIKV (and potentially ZIKV) will be analysed using a 'phylodynamic' approach, integrating the sequence data with epidemiological data using mathematical models in order to gain a more complete understanding of the transmission dynamics of these arboviruses.