Shenglai Yin studies how the migration influence the infection prevalence of avian influenza virus (AIV) in a migratory population, in particular the species and temporal variation in AIV prevalence, effects of complexity of migration network, land-use changes and global warming on AIV prevalence, using field surveillance data and modelling approach.
As we know, outbreaks of avian influenza have caused huge risk for human health and economic safety. Among the factors that can cause an outbreak of avian influenza, migration was considered as an important one because the outbreaks were spatially and temporally associated with it (Si et al. 2010; Tian et al. 2015).This PhD project aimed to understand how the migration process influence the prevalence of avian influenza infection in a migratory population. More specifically, this project contains four studies:
1. Species and temporal variation
In this study, a dataset from an eight-year successive field surveillance will be analysed to test whether the bean geese, barnacle geese and white-fronted geese differed significantly on their prevalence of AIV infection. Furthermore, I will also analysis which factors contribute to the difference and whether the infection impair the dispersal distance of these species. With this study, I expect that we can have a better understanding about the ecology of AIV infection among geese species, and offer some suggestions for field surveillance.
2. Complexity of migration network
Complexity of migration network was constituted by number of stopover sites and variation in using sites. Complexity of migration network and synchrony of migration timing might influence the infection prevalence of AIV by changing the density in stopover sites. In this study, I will study effects of complexity and migration synchrony on infection prevalence by using SIR model.
3. Land-use changes
Complexity of migration network could be altered by land-use changes via causing shrinkage and/or loss of stopover sites. I expect the prevalence of avian influenza will be influenced by land-use change as well. In this study, I will study effects of land-use change on prevalence of AIV infection by using SIR model and network analysis.
Furthermore, some of stopover sites are crucial to keep the connection of a migration network. The influence of these important sites on the prevalence of AIV will also be discussed.
4. Increasing temperature might spatially and temporally influence the prevalence of AIV in a migratory population by changing the birds distribution, timing of migration and viral infectivity in environment. In this proposed study, I will apply the Individual-Based Model (IBM) to simulate individual response to global warming, and explore the effect of warming on the infection prevalence.