Outbreak detection for temporal contact data

Epidemic spreading is a widely studied process due to its importance and possibly grave consequences for society. While the classical context of epidemic spreading refers to pathogens transmitted among humans or animals, it is straightforward to apply similar ideas to the spread of information (e.g., a rumor) or the spread of computer viruses. This paper addresses the question of how to optimally select nodes for monitoring in a network of timestamped contact events between individuals. We consider three optimization objectives: the detection likelihood, the time until detection, and the population that is affected by an outbreak. The optimization approach we use is based on a simple greedy approach and has been proposed in a seminal paper focusing on information spreading and water contamination. We extend this work to the setting of disease spreading and present its application with two example networks: a timestamped network of sexual contacts and a network of animal transports between farms. We apply the optimization procedure to a large set of outbreak scenarios that we generate with a susceptible-infectious-recovered model. We find that simple heuristic methods that select nodes with high degree or many contacts compare well in terms of outbreak detection performance with the (greedily) optimal set of nodes. Furthermore, we observe that nodes optimized on past periods may not be optimal for outbreak detection in future periods. However, seasonal effects may help in determining which past period generalizes well to some future period. Finally, we demonstrate that the detection performance depends on the simulation settings. In general, if we force the simulator to generate larger outbreaks, the detection performance will improve, as larger outbreaks tend to occur in the more connected part of the network where the top monitoring nodes are typically located. A natural progression of this work is to analyze how a representative set of outbreak scenarios can be generated, possibly taking into account more realistic propagation models

Observer Placement for Source Localization: The Effect of Budgets and Transmission Variance

When an epidemic spreads in a network, a key question is where was its source, i.e., the node that started the epidemic. If we know the time at which various nodes were infected, we can attempt to use this information in order to identify the source. However, maintaining observer nodes that can provide their infection time may be costly, and we may have a budget $k$ on the number of observer nodes we can maintain. Moreover, some nodes are more informative than others due to their location in the network. Hence, a pertinent question arises: Which nodes should we select as observers in order to maximize the probability that we can accurately identify the source? Inspired by the simple setting in which the node-to-node delays in the transmission of the epidemic are deterministic, we develop a principled approach for addressing the problem even when transmission delays are random. We show that the optimal observer-placement differs depending on the variance of the transmission delays and propose approaches in both low- and high-variance settings. We validate our methods by comparing them against state-of-the-art observer-placements and show that, in both settings, our approach identifies the source with higher accuracy.Comment: Accepted for presentation at the 54th Annual Allerton Conference on Communication, Control, and Computin

Inferring spatial source of disease outbreaks using maximum entropy

Mathematical modeling of disease outbreaks can infer the future trajectory of an epidemic, allowing for making more informed policy decisions. Another task is inferring the origin of a disease, which is relatively difficult with current mathematical models. Such frameworks, across varying levels of complexity, are typically sensitive to input data on epidemic parameters, case counts, and mortality rates, which are generally noisy and incomplete. To alleviate these limitations, we propose a maximum entropy framework that fits epidemiological models, provides calibrated infection origin probabilities, and is robust to noise due to a prior belief model. Maximum entropy is agnostic to the parameters or model structure used and allows for flexible use when faced with sparse data conditions and incomplete knowledge in the dynamical phase of disease-spread, providing for more reliable modeling at early stages of outbreaks. We evaluate the performance of our model by predicting future disease trajectories based on simulated epidemiological data in synthetic graph networks and the real mobility network of New York State. In addition, unlike existing approaches, we demonstrate that the method can be used to infer the origin of the outbreak with accurate confidence. Indeed, despite the prevalent belief on the feasibility of contact-tracing being limited to the initial stages of an outbreak, we report the possibility of reconstructing early disease dynamics, including the epidemic seed, at advanced stages

SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity

Social networking websites allow users to create and share content. Big information cascades of post resharing can form as users of these sites reshare others' posts with their friends and followers. One of the central challenges in understanding such cascading behaviors is in forecasting information outbreaks, where a single post becomes widely popular by being reshared by many users. In this paper, we focus on predicting the final number of reshares of a given post. We build on the theory of self-exciting point processes to develop a statistical model that allows us to make accurate predictions. Our model requires no training or expensive feature engineering. It results in a simple and efficiently computable formula that allows us to answer questions, in real-time, such as: Given a post's resharing history so far, what is our current estimate of its final number of reshares? Is the post resharing cascade past the initial stage of explosive growth? And, which posts will be the most reshared in the future? We validate our model using one month of complete Twitter data and demonstrate a strong improvement in predictive accuracy over existing approaches. Our model gives only 15% relative error in predicting final size of an average information cascade after observing it for just one hour.Comment: 10 pages, published in KDD 201

Deep Neural Network for Anomaly Detection

학위논문(석사)--서울대학교 대학원 :공과대학 협동과정 기술경영·경제·정책전공,2019. 8. 황준석.Human insurgency is one of the prevalent, incessant, and threatening events happening worldwide. Among many topics of developmental studies, one of the seminal research focuses is to understand and model armed conflicts, which have been suspected to be linked to the capacity of a country in various ways, such as food security, child nutrition, economic welfare, and even environmental issues. Mapping human insurgencies is, therefore, imperative. To cope with the atrocities, there have been previous attempts to uncover the latent patterns of human insurgent incidents. The salient behavior of these insurgencies follows the 'power-law' distribution, which exhibits a heavy-tail. This feature implies that events far from the norm are nontrivial when compared with the normal distribution, where essentially no weight is far from the mean. This pattern indicates that the insurgencies are the few incidents happening with relentless severity, while the majority of the events occur with mere severity. To fully exploit the latent behavior of human insurgencies, this research focuses on the anomalies — the events that have a great number of fatalities but little probability of occurrence, lying on the heavy tail—. To detect such anomalies, a novel approach, variational autoencoder, is used. The seminal essence of this model lies in processing high-volume data and capturing their non-linearity, which makes data-driven detection possible. The results show that the trained model successfully detects anomalies when given test data, showing no false negatives (Type III error) or false positives (Type I error). This predictive model, if well deployed, can provide humanitarian aid agencies and governments the ability to efficiently allocate resources, reducing wastes and mitigating the level of conflict through targeted preventive policies무력 충돌, 급진적인 테러 등을 포함하는 인간 폭등 (Human Insurgency)은 지금껏 만연하고 끊임없이 전 세계적으로 위협을 주는 현상이다. 세계 개발 분야의 여러 주제 중 기관 및 조직의 주요한 관심은 이러한 충돌을 이해하고 모델 하는 것이었다. 이러한 현상은 한 체제의 식량 안보, 아동 영양, 경제적, 그리고 환경적 문제와도 연관이 되는 것으로 간주되고 있다. 따라서 무력 충돌을 포함한 인간 폭등 현상을 매핑하는 것은 필수적으로 요구되고 있다. 기존의 연구들에서는 이런 현상들의 숨어 있는 패턴을 분석하고자 하는 시도들이 있었다. 그중 두드러진 특징은 멱법칙 (power law) 분포를 따른다는 것이다. 멱법칙 분포는 꼬리가 긴 형태를 가지고 있다. 기존 정규분포와는 달리, 멱법칙 분포는 중심에서 멀리 위치하고 있는 현상이 더 이상 간소히 존재하는 것이 아니라, 상당히 존재한다고 볼 수 있다. 이러한 급증 규칙은 소수의 사건들이 작은 확률로 큰 규모로 발생하는 반면, 다수의 사건들은 작은 규모로 발생한다는 것을 암시하고 있다. 이러한 규칙들을 활용하여, 본 연구에서는 폭등을 매핑하기 위해 비이상적 사건들에 초점을 맞춘다. 비이상적 사건들은 멱법칙의 꼬리에 위치하는, 규모가 크지만 작은 확률로 발생하는 사건들을 일컫는다. 이러한 비이상적 사건들을 판별하기 위해, Variational Autoencoder (VAE)라는 새로운 방법론을 도입하였다. 이 모델의 장점은 많은 량의 데이터를 처리하고, 비 선형적인 관계들을 포착할 수 있다는 것이다. 이러한 방법으로 data-driven 판별을 할 수 있게 한다. 이러한 모델로 훈련한 결과, False Negative (Type III error)와 False Positive (Type I error)이 발생하지 않았고, 비이상적인 사건들을 성공적으로 판별할 수 있었다. 본 연구에서는 이러한 모델을 제시함으로 실제 인도주의 단체나 정부에 적용되었을 경우, 제한된 자원을 효율적으로 배치하여 향후 폭등 규모를 완화할 수 있다고 본다.Abstract i Contents iii List of Tables v List of Figures vi Chapter 1. Introduction 1 Chapter 2. Literature Review 5 2.1 Human Insurgency 5 2.1.1 Definition of Human Insurgency 7 2.2 Predictive Models of Human Insurgency 8 2.2.1 The limitation of previous literature 10 2.3 Latent Behavior of Human Insurgency 10 2.4 Anomaly Detection 12 2.4.1 Anomaly Detection Methods 13 Chapter 3. Methodology and Data 15 3.1 Model 15 3.1.1 Variational Inference 15 3.1.2 Autoencoder 17 3.1.3 Variational Autoencoder (VAE) 18 3.2 VAE Anomaly detection 23 3.3 Analysis Sequence 25 3.4 Data Description 26 Chapter 4. Result 31 4.1 Reconstruction Error 31 4.2 Performance Analysis 33 4.2.1 Accuracy 34 4.2.2 ROC (Receiver Operating Characteristics) 35 4.2.3 Precision 36 4.2.4 Recall (Sensitivity) 37 4.2.5 Precision vs Recall 38 Chapter 5. Discussion and Conclusion 40 5.1 Implication 40 5.2 Limitations 41 5.3 Further Research 42 Bibliography 43 Abstract (Korean) 51Maste