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Quantifying HIV incidence is essential for tracking epidemics but doing this in concentrated epidemic can be a particular challenge because of limited consistent high-quality data about the size, behaviour and prevalence of HIV among key populations. Here, we examine a method for estimating HIV incidence from routinely collected case-reporting data.A flexible model of HIV infection, diagnosis and survival is constructed and fit to time-series data on the number of reported cases in a Bayesian framework. The time trend in the hazard of infection is specified by a penalized B-spline. We examine the performance of the model by applying it to synthetic data and determining whether the method is capable of recovering the input incidence trend. We then apply the method to real data from Colombia and compare our estimates of incidence with those that have been derived using alternative methods.The method can feasibly be applied and it successfully recovered a range of incidence trajectories in synthetic data experiments. However, estimates for incidence in the recent past are highly uncertain. When applied to data from Colombia, a credible trajectory of incidence is generated which indicates a much lower historic level of HIV incidence than has previously been estimated using other methods.It is feasible, though not satisfactory, to estimate incidence using case-report data in settings with good data availability. Future work should examine the impact on missing or biased data, the utility of alternative formulations of flexible functions specifying incidence trends, and the benefit of also including data on deaths and programme indicators such as the numbers receiving antiretroviral therapy.