No time to waste: practical statistical contact tracing with few low-bit messages

Pandemics have a major impact on society and the economy. In the case of a new virus, such as COVID-19, high-grade tests and vaccines might be slow to develop and scarce in the crucial initial phase. With no time to waste and lock-downs being expensive, contact tracing is thus an essential tool for policymakers. In theory, statistical inference on a virus transmission model can provide an effective method for tracing infections. However, in practice, such algorithms need to run decentralized, rendering existing methods – that require hundreds or even thousands of daily messages per person – infeasible. In this paper, we develop an algorithm that (i) requires only a few (2-5) daily messages, (ii) works with extremely low bandwidths (3-5 bits) and (iii) enables quarantining and targeted testing that drastically reduces the peak and length of the pandemic. We compare the effectiveness of our algorithm using two agent-based simulators of realistic contact patterns and pandemic parameters and show that it performs well even with low bandwidth, imprecise tests, and incomplete population coverage

Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases

Introduction: The clinical assessment of mobility, and walking specifically, is still mainly based on functional tests that lack ecological validity. Thanks to inertial measurement units (IMUs), gait analysis is shifting to unsupervised monitoring in naturalistic and unconstrained settings. However, the extraction of clinically relevant gait parameters from IMU data often depends on heuristics-based algorithms that rely on empirically determined thresholds. These were mainly validated on small cohorts in supervised settings. Methods: Here, a deep learning (DL) algorithm was developed and validated for gait event detection in a heterogeneous population of different mobility-limiting disease cohorts and a cohort of healthy adults. Participants wore pressure insoles and IMUs on both feet for 2.5 h in their habitual environment. The raw accelerometer and gyroscope data from both feet were used as input to a deep convolutional neural network, while reference timings for gait events were based on the combined IMU and pressure insoles data. Results and discussion: The results showed a high-detection performance for initial contacts (ICs) (recall: 98%, precision: 96%) and final contacts (FCs) (recall: 99%, precision: 94%) and a maximum median time error of −0.02 s for ICs and 0.03 s for FCs. Subsequently derived temporal gait parameters were in good agreement with a pressure insoles-based reference with a maximum mean difference of 0.07, −0.07, and <0.01 s for stance, swing, and stride time, respectively. Thus, the DL algorithm is considered successful in detecting gait events in ecologically valid environments across different mobility-limiting diseases

No time to waste: practical statistical contact tracing with few low-bit messages

Pandemics have a major impact on society and the economy. In the case of a new virus, such as COVID-19, high-grade tests and vaccines might be slow to develop and scarce in the crucial initial phase. With no time to waste and lock-downs being expensive, contact tracing is thus an essential tool for policymakers. In theory, statistical inference on a virus transmission model can provide an effective method for tracing infections. However, in practice, such algorithms need to run decentralized, rendering existing methods – that require hundreds or even thousands of daily messages per person – infeasible. In this paper, we develop an algorithm that (i) requires only a few (2-5) daily messages, (ii) works with extremely low bandwidths (3-5 bits) and (iii) enables quarantining and targeted testing that drastically reduces the peak and length of the pandemic. We compare the effectiveness of our algorithm using two agent-based simulators of realistic contact patterns and pandemic parameters and show that it performs well even with low bandwidth, imprecise tests, and incomplete population coverage

No time to waste: practical statistical contact tracing with few low-bit messages

Pandemics have a major impact on society and the economy. In the case of a new virus, such as COVID-19, high-grade tests and vaccines might be slow to develop and scarce in the crucial initial phase. With no time to waste and lock-downs being expensive, contact tracing is thus an essential tool for policymakers. In theory, statistical inference on a virus transmission model can provide an effective method for tracing infections. However, in practice, such algorithms need to run decentralized, rendering existing methods – that require hundreds or even thousands of daily messages per person – infeasible. In this paper, we develop an algorithm that (i) requires only a few (2-5) daily messages, (ii) works with extremely low bandwidths (3-5 bits) and (iii) enables quarantining and targeted testing that drastically reduces the peak and length of the pandemic. We compare the effectiveness of our algorithm using two agent-based simulators of realistic contact patterns and pandemic parameters and show that it performs well even with low bandwidth, imprecise tests, and incomplete population coverage

The Effect of Covariate Shift and Network Training on Out-of-Distribution Detection

The field of Out-of-Distribution (OOD) detection aims to separate OOD data from in-distribution (ID) data in order to make safe predictions. With the increasing application of Convolutional Neural Networks (CNNs) in sensitive environments such as autonomous driving and security, this field is bound to become indispensable in the future. Although the OOD detection field has made some progress in recent years, a fundamental understanding of the underlying phenomena enabling the separation of datasets remains lacking. We find that the OOD detection relies heavily on the covariate shift of the data and not so much on the semantic shift, i.e. a CNN does not carry explicit semantic information and relies solely on differences in features. Although these features can be affected by the underlying semantics, this relation does not seem strong enough to rely on. Conversely, we found that since the CNN training setup determines what features are learned, that it is an important factor for the OOD performance. We found that variations in the model training can lead to an increase or decrease in the OOD detection performance. Through this insight, we obtain an increase in OOD detection performance on the common OOD detection benchmarks by changing the training procedure and using the simple Maximum Softmax Probability (MSP) model introduced by (Hendrycks and Gimpel, 2016). We hope to inspire others to look more closely into the fundamental principles underlying the separation of two datasets. The code for reproducing our results can be found at https://github.com/SimonMariani/OOD- detection

<i>Social Support, Depression, Self-Esteem, and Coping Among LGBTQ Adolescents Participating in</i> Hatch Youth

Evidence-based interventions that increase social support have the potential to improve the health of lesbian, gay, bisexual, transgender, and queer (LGBTQ) youth. Hatch Youth is a group-level intervention that provides services four nights a week to LGBTQ youth between 13 and 20 years of age. Each Hatch Youth meeting is organized into three 1-hour sections: unstructured social time, consciousness-raising (education), and a youth-led peer support group. Youth attending a Hatch Youth meeting between March and June 2014 (N = 108) completed a cross-sectional survey. Covariate adjusted regression models were used to examine the association between attendance, perceived social support, depressive symptomology, self-esteem, and coping ability. Compared to those who attended Hatch Youth for less than 1 month, participants who attended 1 to 6 months or more than 6 months reported higher social support (beta(1-6mo.) = 0.57 [0.07, 1.07]; beta(6+mo.) = 0.44, 95% confidence interval [CI; 0.14, 0.75], respectively). Increased social support was associated with decreased depressive symptomology (beta = -4.84, 95% CI [-6.56, -3.12]), increased self-esteem (beta = 0.72, 95% CI [0.38, 1.06]), and improved coping ability (beta = 1.00, 95% CI [0.66, 1.35]). Hatch Youth is a promising intervention that has the potential to improve the mental health and reduce risk behavior of LGBTQ youth.</p

Cerebellar rTMS in PSP: a Double-Blind Sham-Controlled Study Using Mobile Health Technology

There are no effective treatments in progressive supranuclear palsy (PSP). The aim of this study was to test the efficacy of theta burst repetitive transcranial magnetic stimulation (rTMS) on postural instability in PSP. Twenty PSP patients underwent a session of sham or real cerebellar rTMS in a crossover design. Before and after stimulation, static balance was evaluated with instrumented (lower back accelerometer, Rehagait®, Hasomed, Germany) 30-s trials in semitandem and tandem positions. In tandem and semitandem tasks, active stimulation was associated with increase in time without falls (both p=0.04). In the same tasks, device-extracted parameters revealed significant improvement in area (p=0.007), velocity (p=0.005), acceleration and jerkiness of sway (p=0.008) in real versus sham stimulation. Cerebellar rTMS showed a significant effect on stability in PSP patients, when assessed with mobile digital technology, in a double-blind design. These results should motivate larger and longer trials using non-invasive brain stimulation for PSP patients

The National Immunisation Programme in the Netherlands: surveillance and developments in 2016-2017.

Surveillance and developments in 2016-2017 In 2016, about 760,000 children aged 0 to 19 years received a total of 2,140,000 vaccinations within the National Immunisation Programme (NIP). Participation in the NIP was high (more than 90% depending on the vaccine), but dropped by around 0.5% for newborns for the third consecutive year. The participation in vaccinations against human papillomavirus (HPV) declined from 61 to 53 per cent. The number of reports (1,483) of adverse events following immunisation (in total 3,665) in 2016 was comparable to the number of reports in 2015. NIP target diseases The number of reported cases of most NIP target diseases was again low. However, the number of cases of Haemophilus influenzae type b (Hib) disease in 2016 (n=44) was considerably higher than in the previous five years (22-34 cases), with the highest incidence occurring among children under five years of age. Pertussis incidence in 2016 fits within the usual fluctuations. However, six people died from pertussis in 2016.The incidence of cervical cancer cases increased in 2016 (9.3 per 100,000 compared with 7.7 per 100,000 in 2015). In 2017, two fully vaccinated employees were exposed to a wild poliovirus type 2 (WPV2). Due to strict isolation, no transmission was detected. Potential NIP target diseases An increase in the number of meningococcal (Men) disease was observed after more than two decades of decrease. An ongoing increase in the number of cases of MenW disease has been observed (9, 50 and 34, respectively, in 2015, 2016 and the first five months of 2017). Dutch Health Council recommendations The RIVM facilitate the Dutch Health Council with their recommendations on vaccinations and therefore has collected and structured relevant national and international information in background documents concerning rotavirus, meningococcal disease and HPV.The Health Council has advised earlier that maternal pertussis vaccination should be provided. The Ministry of Health, Welfare and Sport (VWS) has expressed a positive attitude towards the advice but still has to make a decision. In 2017, the Health Council also advised that all employees who are in close contact with young infants during work should be offered vaccination against pertussis. In addition, the Dutch Health Council advised in September 2017 positive on vaccination against rotavirus and the minister decided to vaccinate against MenACWY in 2018. (aut. ref.