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Dynamical models based on ordinary differential equations (ODE) are increasingly used to model viral infections such as HIV. This kind of model is based on biological knowledge and takes into account complex non-linear interactions between markers. The estimation of such models is made difficult by the lack of analytical solutions and several methods based on Bayesian or frequentist approaches have been proposed. However, because of identifiability issues, in a frequentist approach some parameters have to be fixed to values taken from the literature. In this paper we propose a Maximum A Posteriori (MAP) approach to estimate all the parameters of ODE models, allowing prior knowledge on biological parameters to be taken into account. The MAP approach has two main advantages: the computation time can be fast (relative to the full Bayesian approach) and it is straightforward to incorporate complex prior information. We applied this method to an original model of primary HIV infection taking into account several biological hypotheses for the HIV-immune system interaction. Parameters were estimated using HIV RNA load and CD4 count measurements of 710 patients from the Concerted Action on SeroConversion to AIDS and Death in Europe (CASCADE) Collaboration.

Although Age-Period-Cohort (APC) models are routinely used in time-trend analyses of chronic diseases, few examples of their application to epidemic diseases are available. APC analyses of the French bovine spongiform encephalopathy (BSE) epidemic revealed an unexpected period effect, which was attributed to the design of the study, in connection with low BSE prevalence and a short surveillance period. The aim of our study was to evaluate the relevance of this hypothesis, the behaviour of the APC model in an epidemic context (e.g. the BSE epidemic) and the impact of including the period effect in an APC model on the estimate of birth cohort effects. We simulated datasets mimicking the French BSE epidemic and its variable pattern, as well as duration and the surveillance time period, and analysed them with a categorical APC model. Results showed a period effect in 44% of analysed datasets, although no period effect had been introduced in the data simulation process. This type of artefactual period effect was detected when a sudden change in cohort prevalence occurred over a short period of time. Additionally, in the context of BSE, including a period effect in the model may dramatically affect the estimation of cohort prevalence, depending on epidemic pattern and, as expected, duration and the surveillance time period according to the year in which highly infected birth cohorts are detected as BSE cases.

There has been much recent speculation regarding the potential for HIV test-and-treat strategies to provide control of the HIV endemic. In the UK, despite advanced HIV surveillance and the implementation of a number of testing initiatives and attempts to widen access to antiretroviral drugs, the number of new diagnoses persists at a high level having risen considerably over the course of the last ten years. The extent to which this high level of diagnosis is attributable to levels of HIV transmission or improved rates of testing and diagnosis is unclear. To disentangle these competing factors, we use a Bayesian back-calculation based on HIV and AIDS diagnosis data augmented by observed CD4 count levels at diagnosis. The CD4 count acts as a prognostic marker indicative of the time-since-infection for any new diagnosis. In addition to estimating time-dependent rates of infection and diagnosis, we exploit the model structure to estimate posterior distributions for a number of key epidemiological quantities such as trends in the time-to-diagnosis and the time-since infection distributions as well as the prevalence of undiagnosed infection. These quantities are stratified by CD4 count where possible. The proposed methodology is applied to HIV/AIDS surveillance data from England & Wales uncovering a decreasing trend in the time to diagnosis and stable levels of incidence in recent years.

While highly active antiretroviral therapy (HAART) has transformed HIV/AIDS to a chronic, treatable disease in Canada, HIV incidence continues to rise among male-sex-male (MSM) populations. Montreal, Canada, is a unique environment for a comprehensive surveillance study on HIV transmission dynamics. Phylogenetic strategies show that half of all new MSM infections in Montreal may arise through onward transmission by individuals who are in primary HIV infection (PHI) (< 6 months post-infection) and often unaware of their HIV status. Large cluster networks, wherein one infection leads to 5-31 onward transmissions, constitute the fastest-growing sub-epidemic, representing 25% and 39% of genotyped incident infections in 2005 and 2009, respectively. This has disturbing implications in light of the introduction of non-B subtype and drug-resistant sub-epidemics. Biological and behavioural correlates of cluster membership are being investigated to establish risk determinants implicated in the onward transmission of the MSM epidemic. Our findings underscore the opportunities and challenges in implementing new testing and tailored prevention paradigms for different MSM populations.

HIV transmission models include heterogeneous individuals with different sexual behaviors including contact rates, mixing patterns, and sexual practices. However, heterogeneity can also exist within individuals over time. In this paper we analyze a two year prospective cohort of 882 gay men with observations at six month intervals focusing on heterogeneity both within and between individuals in sexual contact rates and sexual roles. The total number of sexual contacts made over the course of the study (mean 1.55 per month) are highly variable between individuals (standard deviation 9.82 per month) as expected. At the individual level, contacts were also heterogeneous over time. For a homogeneous count process the variance should scale with the mean; however, at the individual level the variance scaled with the square root of the mean implying the presence of heterogeneity within individuals over time. We also observed a high level of movement between dichotomous sexual roles (insertive/receptive, protected/unprotected, anal/oral, and HIV status of partners). On average periods of exclusively unprotected sexual contacted lasted 16 months. Our results suggest that future HIV models should consider heterogeneities both between and within individuals in sexual contact rates and sexual roles.

A deterministic compartmental model was explored that relaxed the unrealistic assumption in most HIV transmission models that behaviors of individuals are constant over time. A simple model was formulated to better explain the effects observed. Individuals had a high and a low contact rate and went back and forth between them. This episodic risk behavior interacted with the short period of high transmissibility during acute HIV infection to cause dramatic increases in prevalence as the differences between high and low contact rates increased and as the duration of high risk better matched the duration of acute HIV infection. These same changes caused a considerable increase in the fraction of all transmissions that occurred during acute infection. These strong changes occurred despite a constant total number of contacts and a constant total transmission potential from acute infection. Two phenomena played a strong role in generating these effects. First, people were infected more often during their high contact rate phase and they remained with high contact rates during the highly contagious acute infection stage. Second, when individuals with previously low contact rates moved into an episodic high-risk period, they were more likely to be susceptible and thus provided more high contact rate susceptible individuals who could get infected. These phenomena make test and treat control strategies less effective and could cause some behavioral interventions to increase transmission. Signature effects on genetic patterns between HIV strains could make it possible to determine whether these episodic risk effects are acting in a population.