The effect of Wolbachia on dengue outbreaks when dengue is repeatedly introduced

Use of the Wolbachia bacterium is a proposed new strategy to reduce dengue transmission, which results in around 390 million individuals infected annually. In places with strong variations in climatic conditions such as temperature and rainfall, dengue epidemics generally occur only at a certain time of the year. Where dengue is not endemic, the time of year in which imported cases enter the population plays a crucial role in determining the likelihood of outbreak occurrence. We use a mathematical model to study the effects of Wolbachia on dengue transmission dynamics and dengue seasonality. We focus in regions where dengue is not endemic but can spread due to the presence of a dengue vector and the arrival of people with dengue on a regular basis. Our results show that the time-window in which outbreaks can occur is reduced in the presence of Wolbachia-carrying Aedes aegypti mosquitoes by up to six weeks each year. We find that Wolbachia reduces overall case numbers by up to 80%. The strongest effect is obtained when the amplitude of the seasonal forcing is low (0.02-0.30). The benefits of Wolbachia also depend on the transmission rate, with the bacteria most effective at moderate transmission rates ranging between 0.08-0.12. Such rates are consistent with fitted estimates for Cairns, Australia.

Stimulus-dependent refractoriness in the Frankenhaeuser-Huxley model

Phenomenological neural models, such as the leaky integrate-and-fire model, normally have a fixed refractory time-course that is independent of the stimulus. The recovery of threshold following an action potential is typically based on physiological experiments that use a two-pulse paradigm in which the first pulse is suprathreshold and causes excitation and the second pulse is used to determine the threshold at various intervals following the first. In such experiments, the nerve is completely unstimulated between the two pulses. This contrasts the receptor stimuli in normal physiological systems and the electrical stimuli used by cochlear implants and other neural prostheses. A numerical study of the Frankenhaeuser-Huxley conductance-based model of nerve fibre was therefore undertaken to investigate the effect of stimulation on refractoriness. We found that the application of a depolarizing stimulus during the later part of what is classically regarded as the absolute refractory period could effectively prolong the absolute refractory period, while leaving the refractory time-constants and other refractory parameters largely unaffected. Indeed, long depolarizing pulses, which would have been suprathreshold if presented to a resting nerve fibre, appeared to block excitation indefinitely. Stimulation during what is classically regarded as the absolute refractory period can therefore greatly affect the temporal response of a nerve. We conclude that the classical definition of absolute refractory period should be refined to include only the initial period following an action potential when an ongoing stimulus would not affect threshold; this period was found to be about half as long as the classical absolute refractory period. We further conclude that the stimulus-dependent nature of the relative refractory period must be considered when developing a phenomenological nerve model for complex stimuli.

A phenomenological model of myelinated nerve with a dynamic threshold

To evaluate coding strategies for cochlear implants a model of the human cochlear nerve is required. Nerve models based on voltage-clamp experiments, such as the Frankenhaeuser-Huxley model of myelinated nerve, can have over forty parameters and are not amenable for fitting to physiological data from a different animal or type of nerve. Phenomenological nerve models, such as leaky integrate-and-fire (LIF) models, have fewer parameters but have not been validated with a wide range of stimuli. In the absence of substantial cochlear nerve data, we have used data from a toad sciatic nerve for validation (50 Hz to 2 kHz with levels up to 20 dB above threshold). We show that the standard LIF model with fixed refractory properties and a single set of parameters cannot adequately predict the toad rate-level functions. Given the deficiency of this standard model, we have abstracted the dynamics of the sodium inactivation variable in the Frankenhaeuser-Huxley model to develop a phenomenological LIF model with a dynamic threshold. This nine-parameter model predicts the physiological rate-level functions much more accurately than the standard LIF model. Because of the low number of parameters, we expect to be able to optimize the model parameters so that the model is more appropriate for cochlear implant simulations.

Parameter inference in small world network disease models with approximate Bayesian Computational methods

Small world network models have been effective in capturing the variable behaviour of reported case data of the SARS coronavirus outbreak in Hong Kong during 2003. Simulations of these models have previously been realized using informed "guesses" of the proposed model parameters and tested for consistency with the reported data by surrogate analysis. In this paper we attempt to provide statistically rigorous parameter distributions using Approximate Bayesian Computation sampling methods. We find that such sampling schemes are a useful framework for fitting parameters of stochastic small world network models where simulation of the system is straightforward but expressing a likelihood is cumbersome.