Modelling the Wolbachia incompatible insect technique: strategies for effective mosquito population elimination.

BACKGROUND: The Wolbachia incompatible insect technique (IIT) shows promise as a method for eliminating populations of invasive mosquitoes such as Aedes aegypti (Linnaeus) (Diptera: Culicidae) and reducing the incidence of vector-borne diseases such as dengue, chikungunya and Zika. Successful implementation of this biological control strategy relies on high-fidelity separation of male from female insects in mass production systems for inundative release into landscapes. Processes for sex-separating mosquitoes are typically error-prone and laborious, and IIT programmes run the risk of releasing Wolbachia-infected females and replacing wild mosquito populations. RESULTS: We introduce a simple Markov population process model for studying mosquito populations subjected to a Wolbachia-IIT programme which exhibit an unstable equilibrium threshold. The model is used to study, in silico, scenarios that are likely to yield a successful elimination result. Our results suggest that elimination is best achieved by releasing males at rates that adapt to the ever-decreasing wild population, thus reducing the risk of releasing Wolbachia-infected females while reducing costs. CONCLUSIONS: While very high-fidelity sex separation is required to avoid establishment, release programmes tend to be robust to the release of a small number of Wolbachia-infected females. These findings will inform and enhance the next generation of Wolbachia-IIT population control strategies that are already showing great promise in field trials.

On parameter estimation in population models II: multi-dimensional processes and transient dynamics.

Recently, a computationally-efficient method was presented for calibrating a wide-class of Markov processes from discrete-sampled abundance data. The method was illustrated with respect to one-dimensional processes and required the assumption of stationarity. Here we demonstrate that the approach may be directly extended to multi-dimensional processes, and two analogous computationally-efficient methods for non-stationary processes are developed. These methods are illustrated with respect to disease and population models, including application to infectious count data from an outbreak of "Russian influenza" (A/USSR/1977 H1N1) in an educational institution. The methodology is also shown to provide an efficient, simple and yet rigorous approach to calibrating disease processes with gamma-distributed infectious period.

A bead-based suspension array for the multiplexed detection of begomoviruses and their whitefly vectors.

Bead-based suspension array systems enable simultaneous fluorescence-based identification of multiple nucleic acid targets in a single reaction. This study describes the development of a novel approach to plant virus and vector diagnostics, a multiplexed 7-plex array that comprises a hierarchical set of assays for the simultaneous detection of begomoviruses and Bemisia tabaci, from both plant and whitefly samples. The multiplexed array incorporates genus, species and strain-specific assays, offering a unique approach for identifying both known and unknown viruses and B. tabaci species. When tested against a large panel of sequence-characterized begomovirus and whitefly samples, the array was shown to be 100% specific to the homologous target. Additionally, the multiplexed array was highly sensitive, efficiently and concurrently determining both virus and whitefly identity from single viruliferous whitefly samples. The detection limit for one assay within the multiplexed array that specifically detects Tomato yellow leaf curl virus-Israel (TYLCV-IL) was quantified as 200fg of TYLCV-IL DNA, directly equivalent to that of TYLCV-specific qPCR. Highly reproducible results were obtained over multiple tests. The flexible multiplexed array described in this study has great potential for use in plant quarantine, biosecurity and disease management programs worldwide.