  Evaluation of HIV large scale interventions programme is becoming
increasingly important, but impact estimates frequently hinge on knowledge of
changes in behaviour such as the frequency of condom use (CU) over time, or
other self-reported behaviour changes, for which we generally have limited or
potentially biased data. We employ a Bayesian inference methodology that
incorporates a dynamic HIV transmission dynamics model to estimate CU time
trends from HIV prevalence data. Estimation is implemented via particle Markov
Chain Monte Carlo methods, applied for the first time in this context. The
preliminary choice of the formulation for the time varying parameter reflecting
the proportion of CU is critical in the context studied, due to the very
limited amount of CU and HIV data available We consider various novel
formulations to explore the trajectory of CU in time, based on diffusion-driven
trajectories and smooth sigmoid curves. Extensive series of numerical
simulations indicate that informative results can be obtained regarding the
amplitude of the increase in CU during an intervention, with good levels of
sensitivity and specificity performance in effectively detecting changes. The
application of this method to a real life problem illustrates how it can help
evaluate HIV intervention from few observational studies and suggests that
these methods can potentially be applied in many different contexts.
