A pilot study evaluating the preventive effects of platform-end lengthwise fencing on trespassing, person struck by train and traffic delays.

BACKGROUND: Trespassing at train tracks and "person under train" (PUT) incidents are serious health, societal and transportation concerns. There is a need for developing different measures to prevent these events. Here, we hypothesized that platform-end lengthwise fences (PLF) reduce trespassing, the number of PUT incidents (suicides and accidents), and train traffic delays. METHOD: PLFs were installed as the intervention at one station in Stockholm in 2020. The number of trespassers detected using CCTV-cameras was compared before and after at the intervention station over a total period of 29 months (using incidence rate ratio, IRR). The reduction in the number of PUT (over 20 years) and train traffic delays (over 9 years) was also investigated by IRR, and by using three control groups. RESULTS: After installation of PLF there was a significant ∼90% reduction in trespasses (IRR = 0.10, 95%CI 0.04-0.23; one-sided exact p < 0.0001). No PUT incident occurred at the intervention station after the installation, compared to 1.11 per year before installation (IRR = 0.32, 95%CI 0-1.82; one-sided exact p = 0.1216). There was a significant reduction in delay minutes post installation compared to before the installation (Mann Whitney U = 0, upper one-sided exact p = 0.0357). The effect of the PLF was also observable in comparison to the three control groups, suggesting that the preventive effect was not due to wider societal events affecting all stations. CONCLUSION: PLF had a large effect on reducing the number of trespasses and the number of delay minutes due to trespasses and PUT incidents. PLF may also have an effect of reducing PUT incidents. PRACTICAL APPLICATIONS: PLF is deemed to be relatively easy and cheap to install and thus scalable (as compared to full barriers, e.g., platform screen doors) and may be considered at platform-ends having an exit, provided there is enough space to install them.

Spatio-temporal predictions of COVID-19 test positivity in Uppsala County, Sweden: a comparative approach.

Previous spatio-temporal COVID-19 prediction models have focused on the prediction of subsequent number of cases, and have shown varying accuracy and lack of high geographical resolution. We aimed to predict trends in COVID-19 test positivity, an important marker for planning local testing capacity and accessibility. We included a full year of information (June 29, 2020-July 4, 2021) with both direct and indirect indicators of transmission, e.g. mobility data, number of calls to the national healthcare advice line and vaccination coverage from Uppsala County, Sweden, as potential predictors. We developed four models for a 1-week-window, based on gradient boosting (GB), random forest (RF), autoregressive integrated moving average (ARIMA) and integrated nested laplace approximations (INLA). Three of the models (GB, RF and INLA) outperformed the naïve baseline model after data from a full pandemic wave became available and demonstrated moderate accuracy. An ensemble model of these three models slightly improved the average root mean square error to 0.039 compared to 0.040 for GB, RF and INLA, 0.055 for ARIMA and 0.046 for the naïve model. Our findings indicate that the collection of a wide variety of data can contribute to spatio-temporal predictions of COVID-19 test positivity.