The COVID-19 pandemic has challenged scientists and policy-makers internationally to develop novel approaches to public health policy. Furthermore, it has also been observed that the prevalence and spread of COVID-19 vary across different spatial, temporal, and demographics. Despite ramping up testing, we still are not at the required level in most parts of the globe. Therefore, we utilize self-reported symptoms survey data to understand trends in the spread of COVID-19. The aim of this study is to segment populations that are highly susceptible. In order to understand such populations, we perform exploratory data analysis, outbreak prediction, and time-series forecasting using public health and policy datasets. From our studies, we try to predict the likely % of the population that tested positive for COVID-19 based on self-reported symptoms. Our findings reaffirm the predictive value of symptoms, such as anosmia and ageusia. And we forecast that % of the population having COVID-19-like illness (CLI) and those tested positive as 0.15% and 1.14% absolute error respectively. These findings could help aid faster development of the public health policy, particularly in areas with low levels of testing and having a greater reliance on self-reported symptoms. Our analysis sheds light on identifying clinical attributes of interest across different demographics. We also provide insights into the effects of various policy enactments on COVID-19 prevalence.
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Distributed Stream Processing (DSP) systems enable processing large streams of continuous data to produce results in near to real time. They are an essential part of many data-intensive applications and analytics platforms. The rate at which events arrive at DSP systems can vary considerably over time, which may be due to trends, cyclic, and seasonal patterns within the data streams. A priori knowledge of incoming workloads enables proactive approaches to resource management and optimization tasks such as dynamic scaling, live migration of resources, and the tuning of configuration parameters during run-times, thus leading to a potentially better Quality of Service. In this paper we conduct a comprehensive evaluation of different load prediction techniques for DSP jobs. We identify three use-cases and formulate requirements for making load predictions specific to DSP jobs. Automatically optimized classical and Deep Learning methods are being evaluated on nine different datasets from typical DSP domains, i.e. the IoT, Web 2.0, and cluster monitoring. We compare model performance with respect to overall accuracy and training duration. Our results show that the Deep Learning methods provide the most accurate load predictions for the majority of the evaluated datasets.
The human face has a high potential for biometric identification due to its many individual traits. At the same time, such identification is vulnerable to biometric copies. These presentation attacks pose a great challenge in unsupervised authentication settings. As a countermeasure, we propose a method that automatically analyzes the plausibility of facial behavior based on a sequence of 3D face scans. A compact feature representation measures facial behavior using the temporal curvature change. Finally, we train our method only on genuine faces in an anomaly detection scenario. Our method can detect presentation attacks using elastic 3D masks, bent photographs with eye holes, and monitor replay-attacks. For evaluation, we recorded a challenging database containing such cases using a high-quality 3D sensor. It features 109 4D face scans including eleven different types of presentation attacks. We achieve error rates of 11% and 6% for APCER and BPCER, respectively.
Social distancing is widely acknowledged as an effective public health policy combating the novel coronavirus. But extreme social distancing has costs and it is not clear how much social distancing is needed to achieve public health effects. In this article, we develop a design-based framework to make inference about the dose-response relationship between social distancing and COVID-19 related death toll and case numbers. We first discuss how to embed observational data with a time-independent, continuous treatment dose into an approximate randomized experiment, and develop a randomization-based procedure that tests if a structured dose-response relationship fits the data. We then generalize the design and testing procedure to accommodate a time-dependent, treatment dose trajectory, and generalize a dose-response relationship to a longitudinal setting. Finally, we apply the proposed design and testing procedures to investigate the effect of social distancing during the phased reopening in the United States on public health outcomes using data compiled from sources including Unacast, the United States Census Bureau, and the County Health Rankings and Roadmaps Program. We rejected a primary analysis null hypothesis that stated the social distancing from April 27, 2020, to June 28, 2020, had no effect on the COVID-19-related death toll from June 29, 2020, to August 2, 2020 (p-value < 0.001), and found that it took more reduction in mobility to prevent exponential growth in case numbers for non-rural counties compared to rural counties.
As a reaction to the high infectiousness and lethality of the COVID-19 virus, countries around the world have adopted drastic policy measures to contain the pandemic. However, it remains unclear which effect these measures, so-called non-pharmaceutical interventions (NPIs), have on the spread of the virus. In this article, we use machine learning and apply drift detection methods in a novel way to predict the time lag of policy interventions with respect to the development of daily case numbers of COVID-19 across 9 European countries and 28 US states. Our analysis shows that there are, on average, more than two weeks between NPI enactment and a drift in the case numbers.
Pedestrian behavior prediction is one of the major challenges for intelligent driving systems. Pedestrians often exhibit complex behaviors influenced by various contextual elements. To address this problem, we propose BiPed, a multitask learning framework that simultaneously predicts trajectories and actions of pedestrians by relying on multimodal data. Our method benefits from 1) a bifold encoding approach where different data modalities are processed independently allowing them to develop their own representations, and jointly to produce a representation for all modalities using shared parameters; 2) a novel interaction modeling technique that relies on categorical semantic parsing of the scenes to capture interactions between target pedestrians and their surroundings; and 3) a bifold prediction mechanism that uses both independent and shared decoding of multimodal representations. Using public pedestrian behavior benchmark datasets for driving, PIE and JAAD, we highlight the benefits of the proposed method for behavior prediction and show that our model achieves state-of-the-art performance and improves trajectory and action prediction by up to 22% and 9% respectively. We further investigate the contributions of the proposed reasoning techniques via extensive ablation studies.
The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries.
COVID-19 has caused many deaths worldwide. The automation of the diagnosis of this virus is highly desired. Convolutional neural networks (CNNs) have shown outstanding classification performance on image datasets. To date, it appears that COVID computer-aided diagnosis systems based on CNNs and clinical information have not yet been analysed or explored. We propose a novel method, named the CNN-AE, to predict the survival chance of COVID-19 patients using a CNN trained with clinical information. Notably, the required resources to prepare CT images are expensive and limited compared to those required to collect clinical data, such as blood pressure, liver disease, etc. We evaluated our method using a publicly available clinical dataset that we collected. The dataset properties were carefully analysed to extract important features and compute the correlations of features. A data augmentation procedure based on autoencoders (AEs) was proposed to balance the dataset. The experimental results revealed that the average accuracy of the CNN-AE (96.05%) was higher than that of the CNN (92.49%). To demonstrate the generality of our augmentation method, we trained some existing mortality risk prediction methods on our dataset (with and without data augmentation) and compared their performances. We also evaluated our method using another dataset for further generality verification. To show that clinical data can be used for COVID-19 survival chance prediction, the CNN-AE was compared with multiple pre-trained deep models that were tuned based on CT images.
Trending topics are often the result of the spreading of information between users of social networks. These special topics can be regarded as rumors. The spreading of a rumor is often studied with the same techniques as in epidemics spreading. It is common that many datasets may not have enough measured variables, so we propose a method for studying the general behavior of the spreaders by selecting estimated variables given by the deterministic model. In order to provide a good approximation, we implemented an adaptative neuro fuzzy inference system (ANFIS). So, in our numerical experimentations, a deterministic approach using SIR and SIRS models (with delay) for two different topics is used. Thus, the authors just applied the ANFIS model for their application and the deterministic model as the preprocessing input variable.
Although cancer patients survive years after oncologic therapy, they are plagued with long-lasting or permanent residual symptoms, whose severity, rate of development, and resolution after treatment vary largely between survivors. The analysis and interpretation of symptoms is complicated by their partial co-occurrence, variability across populations and across time, and, in the case of cancers that use radiotherapy, by further symptom dependency on the tumor location and prescribed treatment. We describe THALIS, an environment for visual analysis and knowledge discovery from cancer therapy symptom data, developed in close collaboration with oncology experts. Our approach leverages unsupervised machine learning methodology over cohorts of patients, and, in conjunction with custom visual encodings and interactions, provides context for new patients based on patients with similar diagnostic features and symptom evolution. We evaluate this approach on data collected from a cohort of head and neck cancer patients. Feedback from our clinician collaborators indicates that THALIS supports knowledge discovery beyond the limits of machines or humans alone, and that it serves as a valuable tool in both the clinic and symptom research.
The notion of "in-domain data" in NLP is often over-simplistic and vague, as textual data varies in many nuanced linguistic aspects such as topic, style or level of formality. In addition, domain labels are many times unavailable, making it challenging to build domain-specific systems. We show that massive pre-trained language models implicitly learn sentence representations that cluster by domains without supervision -- suggesting a simple data-driven definition of domains in textual data. We harness this property and propose domain data selection methods based on such models, which require only a small set of in-domain monolingual data. We evaluate our data selection methods for neural machine translation across five diverse domains, where they outperform an established approach as measured by both BLEU and by precision and recall of sentence selection with respect to an oracle.
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