A novel Bayesian approach to predicting reductions in HIV incidence following increased testing interventions among gay, bisexual and other men who have sex with men in Vancouver, Canada.

Increasing HIV testing rates among high-risk groups should lead to increased numbers of cases being detected. Coupled with effective treatment and behavioural change among individuals with detected infection, increased testing should also reduce onward incidence of HIV in the population. However, it can be difficult to predict the strengths of these effects and thus the overall impact of testing. We construct a mathematical model of an ongoing HIV epidemic in a population of gay, bisexual and other men who have sex with men. The model incorporates different levels of infection risk, testing habits and awareness of HIV status among members of the population. We introduce a novel Bayesian analysis that is able to incorporate potentially unreliable sexual health survey data along with firm clinical diagnosis data. We parameterize the model using survey and diagnostic data drawn from a population of men in Vancouver, Canada. We predict that increasing testing frequency will yield a small-scale but long-term impact on the epidemic in terms of new infections averted, as well as a large short-term impact on numbers of detected cases. These effects are predicted to occur even when a testing intervention is short-lived. We show that a short-lived but intensive testing campaign can potentially produce many of the same benefits as a campaign that is less intensive but of longer duration.

On the duration of the period between exposure to HIV and detectable infection.

HIV infection cannot be detected immediately after exposure because plasma viral loads are too small initially. The duration of this phase of infection (the "eclipse period") is difficult to estimate because precise dates of exposure are rarely known. Therefore, the reliability of clinical HIV testing during the first few weeks of infection is unknown, creating anxiety among HIV-exposed individuals and their physicians. We address this by fitting stochastic models of early HIV infection to detailed viral load records for 78 plasma donors, taken during the period of exposure and infection. We first show that the classic stochastic birth-death model does not satisfactorily describe early infection. We therefore apply a different stochastic model that includes infected cells and virions separately. Since every plasma donor in our data eventually becomes infected, we must condition the model to reflect this bias, before fitting to the data. Applying our best estimates of unknown parameter values, we estimate the mean eclipse period to be 8-10 days. We further estimate the reliability of a negative test t days after potential exposure.

Assessing the optimal virulence of malaria-targeting mosquito pathogens: a mathematical study of engineered Metarhizium anisopliae.

BACKGROUND: Metarhizium anisopliae is a naturally occurring fungal pathogen of mosquitoes. Recently, Metarhizium has been engineered to act against malaria by directly killing the disease agent within mosquito vectors and also effectively blocking onward transmission. It has been proposed that efforts should be made to minimize the virulence of the fungal pathogen, in order to slow the development of resistant mosquitoes following an actual deployment. RESULTS: Two mathematical models were developed and analysed to examine the efficacy of the fungal pathogen. It was found that, in many plausible scenarios, the best effects are achieved with a reduced or minimal pathogen virulence, even if the likelihood of resistance to the fungus is negligible. The results for both models depend on the interplay between two main effects: the ability of the fungus to reduce the mosquito population, and the ability of fungus-infected mosquitoes to compete for resources with non-fungus-infected mosquitoes. CONCLUSIONS: The results indicate that there is no obvious choice of virulence for engineered Metarhizium or similar pathogens, and that all available information regarding the population ecology of the combined mosquito-fungus system should be carefully considered. The models provide a basic framework for examination of anti-malarial mosquito pathogens that should be extended and improved as new laboratory and field data become available.