Data-driven Disease Surveillance

The recent and still ongoing pandemic of SARS-CoV-2 has shown that an infectious disease outbreak can have serious consequences on public health and economy. In this situation, public health officials constantly aim to control and reduce the number of infections in order to avoid overburdening health care system. Besides minimizing personal contact through political measures, a fundamental approach to contain the spread of diseases is to isolate infected individuals. The effectiveness of the latter approach strongly depends on a timely detection of the outbreak as the tracking of individuals can quickly become infeasible when the number of cases increases. Hence, a key factor in the containment of an infectious disease is the early detection of a potential larger outbreak, commonly known as outbreak detection. For this purpose, epidemiologists rely on a variety of statistical surveillance methods in order to maintain an overview of the current situation of infections by either monitoring confirmed cases or cases with early symptoms. Mainly based on statistical hypothesis testing, these methods automatically raise an alarm if an unexpected increase in the number of infections is observed. The practical usefulness of such methods highly depends on the trade-off between the ability to detect outbreaks and the chances of raising a false alarm. However, this hypothesis-based approach to disease surveillance has several limitations. On the one hand, it is a hand-crafted approach which requires domain knowledge to set up the statistical methods, especially if early symptoms are monitored. On the other hand, outbreaks of emerging infectious diseases with different symptom patterns are likely to be missed by such a surveillance system. In this thesis, we focus on data-driven disease surveillance and address these challenges in the following ways. To support epidemiologists in the process of defining reliable disease patterns for monitoring cases with early symptoms, we present a novel approach to discover such patterns in historic data. With respect to supervised learning, we propose a fusion classifier which can combine the output of multiple statistical methods using the univariate time series of infection counts as the only source of information. In addition, we develop algorithms based on unsupervised learning which frame the task of outbreak detection as a general anomaly detection task. This even includes the surveillance of emerging infectious diseases. Therefore, we contribute a novel framework and propose a new approach based on sum-product networks to monitor multiple disease patterns simultaneously. Our results show that data-driven approaches are ideal to assist epidemiologists by processing large amounts of data that cannot fully be understood and analyzed by humans. Most significantly, the incorporation of additional information into the surveillance through machine learning techniques shows reliable and promising results

DeepDB: Learn from Data, not from Queries!

The typical approach for learned DBMS components is to capture the behavior by running a representative set of queries and use the observations to train a machine learning model. This workload-driven approach, however, has two major downsides. First, collecting the training data can be very expensive, since all queries need to be executed on potentially large databases. Second, training data has to be recollected when the workload and the data changes. To overcome these limitations, we take a different route: we propose to learn a pure data-driven model that can be used for different tasks such as query answering or cardinality estimation. This data-driven model also supports ad-hoc queries and updates of the data without the need of full retraining when the workload or data changes. Indeed, one may now expect that this comes at a price of lower accuracy since workload-driven models can make use of more information. However, this is not the case. The results of our empirical evaluation demonstrate that our data-driven approach not only provides better accuracy than state-of-the-art learned components but also generalizes better to unseen queries

Feasibility studies for the measurement of time-like proton electromagnetic form factors from p¯ p→ μ+μ- at P ¯ ANDA at FAIR

This paper reports on Monte Carlo simulation results for future measurements of the moduli of time-like proton electromagnetic form factors, | GE| and | GM| , using the p¯ p→ μ+μ- reaction at P ¯ ANDA (FAIR). The electromagnetic form factors are fundamental quantities parameterizing the electric and magnetic structure of hadrons. This work estimates the statistical and total accuracy with which the form factors can be measured at P ¯ ANDA , using an analysis of simulated data within the PandaRoot software framework. The most crucial background channel is p¯ p→ π+π-, due to the very similar behavior of muons and pions in the detector. The suppression factors are evaluated for this and all other relevant background channels at different values of antiproton beam momentum. The signal/background separation is based on a multivariate analysis, using the Boosted Decision Trees method. An expected background subtraction is included in this study, based on realistic angular distributions of the background contribution. Systematic uncertainties are considered and the relative total uncertainties of the form factor measurements are presented

Technical Design Report for the: PANDA Micro Vertex Detector

This document illustrates the technical layout and the expected performance of the Micro Vertex Detector (MVD) of the PANDA experiment. The MVD will detect charged particles as close as possible to the interaction zone. Design criteria and the optimisation process as well as the technical solutions chosen are discussed and the results of this process are subjected to extensive Monte Carlo physics studies. The route towards realisation of the detector is outlined.Comment: 189 pages, 225 figures, 41 table

PANDA Phase One - PANDA collaboration

The Facility for Antiproton and Ion Research (FAIR) in Darmstadt, Germany, provides unique possibilities for a new generation of hadron-, nuclear- and atomic physics experiments. The future antiProton ANnihilations at DArmstadt (PANDA or P¯ANDA) experiment at FAIR will offer a broad physics programme, covering different aspects of the strong interaction. Understanding the latter in the non-perturbative regime remains one of the greatest challenges in contemporary physics. The antiproton–nucleon interaction studied with PANDA provides crucial tests in this area. Furthermore, the high-intensity, low-energy domain of PANDA allows for searches for physics beyond the Standard Model, e.g. through high precision symmetry tests. This paper takes into account a staged approach for the detector setup and for the delivered luminosity from the accelerator. The available detector setup at the time of the delivery of the first antiproton beams in the HESR storage ring is referred to as the Phase One setup. The physics programme that is achievable during Phase One is outlined in this paper

Precision resonance energy scans with the PANDA experiment at FAIR: Sensitivity study for width and line shape measurements of the X(3872)

This paper summarises a comprehensive Monte Carlo simulation study for precision resonance energy scan measurements. Apart from the proof of principle for natural width and line shape measurements of very narrow resonances with PANDA, the achievable sensitivities are quantified for the concrete example of the charmonium-like X(3872) state discussed to be exotic, and for a larger parameter space of various assumed signal cross-sections, input widths and luminosity combinations. PANDA is the only experiment that will be able to perform precision resonance energy scans of such narrow states with quantum numbers of spin and parities that differ from J P C = 1 - -