Conveners
Epidemiology: Part A
- Edward Hill (University of Warwick)
Epidemiology: Part B
- Renee Dale (LSU)
In this research, we proposed an infectious disease spread model of influenza which was individually based and stochastic. We first described the method to build a realistic model, or to estimate realistic parameter values, based on observed data. An appropriate model has to be individually based and stochastic if we take advantage of small unit data (in this case, every day class unit data)....
In this talk, we will present a mathematical model of 2009 A/H1N1 influenza considering age structure in the Republic of Korea and suggest vaccination strategies for mitigating the epidemics. There were 750,000 confirmed cases of 2009 A/H1N1 influenza from May 2009 and August 2010. Because influenza viruses are spread through close contact, contact pattern plays an important role for the...
Networks representing the spread of infectious diseases in populations have been widely studied. Here, we formulate an SEIR model using an edge-based approach on a static random network with arbitrary degree distribution. The corresponding basic reproduction number and final epidemic size are computed. The SEIR model is used to investigate the stochasticity of the SEIR dynamics. Assuming...
Influenza A viruses have caused a number of global pandemics, the most recent being the H1N1 pandemic in 2009, resulting in considerable human mortality. Despite influenza pandemics being rare events, with it currently being nearly impossible to predict the next influenza emergence event, it may be the case that the virus itself provides us with outbreak signals that should prompt us to be...
Modelling the spread of influenza across Australia is of substantial public health concern. However, there are many challenges in creating accurate models, including how best to capture the spatial and temporal characteristics of the disease spreading process, and aligning with the actual contact process and mobility of individuals in the population. How influenza spreads spatially, whether by...
While there exist a number of mathematical approaches to modelling the spread of disease on a network, analyzing such systems in the presence of uncertainty introduces significant complexity. In scenarios where system parameters must be inferred from limited observations, general approaches to uncertainty quantification can generate approximate distributions of the unknown parameters, but...
Influenza in humans exhibits a strong seasonal cycle in temperate climates, with a peak of varying intensity appearing each winter. However, the exact cause of this seasonal cycle remains poorly understood. We develop a climate-based SIR modelling framework to understand influenza seasonality, with the transmission rate as a function of climate data. By using a variety of climate-based...
Reinfection is known to induce complex epidemiological dynamics (e.g. sustained oscillation) due to the time-series change in susceptibility. The simplest model describing reinfection shows three epidemiological dynamics; disease-free, epidemic and endemic. These three dynamics can be classified by two reproduction numbers, basic reproduction number and reproduction number by only reinfection....
Infections with Group A Streptoccocus (GAS) are highly prevalent in remote communities in
the Northern Territory, Australia. One of the primary drivers of GAS infection is scabies, a small mite which causes a break in the skin layer, potentially allowing GAS to take hold. This biological connection is reaffirmed by the observation that mass treatment for scabies in these remote communities...
