Background SARS-CoV-2 IgG antibody measurements can be used to estimate the proportion of a population exposed or infected and may be informative about the risk of future infection. Previous estimates of the duration of antibody responses vary. Methods We present 6 months of data from a longitudinal seroprevalence study of 3276 UK healthcare workers (HCWs). Serial measurements of SARS-CoV-2 anti-nucleocapsid and anti-spike IgG were obtained. Interval censored survival analysis was used to investigate the duration of detectable responses. Additionally, Bayesian mixed linear models were used to investigate anti-nucleocapsid waning. Results Anti-spike IgG levels remained stably detected after a positive result, e.g., in 94% (95% credibility interval, CrI, 91-96%) of HCWs at 180 days. Anti-nucleocapsid IgG levels rose to a peak at 24 (95% credibility interval, CrI 19-31) days post first PCR-positive test, before beginning to fall. Considering 452 anti-nucleocapsid seropositive HCWs over a median of 121 days from their maximum positive IgG titre, the mean estimated antibody half-life was 85 (95%CrI, 81-90) days. Higher maximum observed anti-nucleocapsid titres were associated with longer estimated antibody half-lives. Increasing age, Asian ethnicity and prior self-reported symptoms were independently associated with higher maximum anti-nucleocapsid levels and increasing age and a positive PCR test undertaken for symptoms with longer anti-nucleocapsid half-lives. Conclusion SARS-CoV-2 anti-nucleocapsid antibodies wane within months, and faster in younger adults and those without symptoms. However, anti-spike IgG remains stably detected. Ongoing longitudinal studies are required to track the long-term duration of antibody levels and their association with immunity to SARS-CoV-2 reinfection.

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This paper presents an end-to-end approach for tracking static and dynamic objects for an autonomous vehicle driving through crowded urban environments. Unlike traditional approaches to tracking, this method is learned end-to-end, and is able to directly predict a full unoccluded occupancy grid map from raw laser input data. Inspired by the recently presented DeepTracking approach ([1], [2]), we employ a recurrent neural network (RNN) to capture the temporal evolution of the state of the environment, and propose to use Spatial Transformer modules to exploit estimates of the egomotion of the vehicle. Our results demonstrate the ability to track a range of objects, including cars, buses, pedestrians, and cyclists through occlusion, from both moving and stationary platforms, using a single learned model. Experimental results demonstrate that the model can also predict the future states of objects from current inputs, with greater accuracy than previous work.

This paper proposes an appearance-based approach to estimating localisation performance in the context of visual teach and repeat. Specifically, it aims to estimate the likely corridor around a taught trajectory within which a visionbased localisation system is still able to localise itself. In contrast to prior art, our system is able to predict this localisation envelope for trajectories in similar, yet geographically distant locations where no repeat runs have yet been performed. Thus, by characterising the localisation performance in one region, we are able to predict performance in another. To achieve this, we leverage a Gaussian Process regressor to estimate the likely number of feature matches for any keyframe in the teach run, based on a combination of trajectory properties such as curvature and an appearance model of the keyframe. Using data from real traversals, we demonstrate that our approach performs as well as prior art when it comes to interpolating localisation performance based on a number of repeat runs, while also performing well at generalising performance estimation to freshly taught trajectories.