Automatic online news monitoring and classification for syndromic surveillance

Syndromic surveillance can play an important role in protecting the public's health against infectious diseases. Infectious disease outbreaks can have a devastating effect on society as well as the economy, and global awareness is therefore critical to protecting against major outbreaks. By monitoring online news sources and developing an accurate news classification system for syndromic surveillance, public health personnel can be apprised of outbreaks and potential outbreak situations. In this study, we have developed a framework for automatic online news monitoring and classification for syndromic surveillance. The framework is unique and none of the techniques adopted in this study have been previously used in the context of syndromic surveillance on infectious diseases. In recent classification experiments, we compared the performance of different feature subsets on different machine learning algorithms. The results showed that the combined feature subsets including Bag of Words, Noun Phrases, and Named Entities features outperformed the Bag of Words feature subsets. Furthermore, feature selection improved the performance of feature subsets in online news classification. The highest classification performance was achieved when using SVM upon the selected combination feature subset.     

Bayesian CAR models for syndromic surveillance on multiple data streams: Theory and practice

Syndromic surveillance has, so far, considered only simple models for Bayesian inference. This paper details the methodology for a serious, scalable solution to the problem of combining symptom data from a network of US hospitals for early detection of disease outbreaks. The approach requires high-end Bayesian modeling and significant computation, but the strategy described in this paper appears to be feasible and offers attractive advantages over the methods that are currently used in this area. The method is illustrated by application to ten quarters worth of data on opioid drug abuse surveillance from 636 reporting centers, and then compared to two other syndromic surveillance methods using simulation to create known signal in the drug abuse database.     

BioWar: scalable agent-based model of bioattacks

While structured by social and institutional networks, disease outbreaks are modulated by physical, economical, technological, communication, health, and governmental infrastructures. To systematically reason about the nature of outbreaks, the potential outcomes of media, prophylaxis, and vaccination campaigns, and the relative value of various early warning devices, social context, and infrastructure, must be considered. Numerical models provide a cost-effective ethical system for reasoning about such events. BioWar, a scalable citywide multiagent network numerical model, is described in this paper. BioWar simulates individuals as agents who are embedded in social, health, and professional networks and tracks the incidence of background and maliciously introduced diseases. In addition to epidemiology, BioWar simulates health-care-seeking behaviors, absenteeism patterns, and pharmaceutical purchases, information useful for syndromic and behavioral surveillance algorithms.     

An analytics appliance for identifying (near) optimal over-the-counter medicine products as health indicators for influenza surveillance

In the era of “Big Data”, a challenge is how to optimize our use of huge volumes of data. In this paper, we address this challenge in the context of a public health surveillance system which identifies disease outbreaks using individual and population health indicators. Our goal is to automate and improve the accuracy of the selection process of the health indicators, a process which is data-intensive and computationally expensive. The health indicators selection process traditionally has been carried out manually by public health experts in collaboration with health data providers. In particular, we present an approach for identifying sets of over-the-counter (OTC) medicine products whose aggregate sales correlate optimally with aggregate counts of emergency department (ED) visits. Towards this goal, we propose an OTC Analytics Appliance which utilizes a distributed search engine to efficiently generate time series of time-stamped records and supports “plug-and-play” search and correlation functionalities. Using the OTC Analytics Appliance with the Pearson correlation coefficient function, we evaluate Brute-force search, Greedy search, and Knapsack search for their ability to select the optimal or suboptimal set of OTC products automatically. Our results show that greedy search is the most preferable, producing a set of OTC products whose sales that correlate optimally or near optimally to ED visits, while achieving acceptable search times with large datasets. Also, our evaluations show that our approach using the greedy search can be potentially used to efficiently identify different optimal OTC medicine products for detection of different types of disease outbreaks.     

Optimizing biosurveillance systems that use threshold-based event detection methods

We describe a methodology for optimizing a threshold detection-based biosurveillance system. The goal is to maximize the system-wide probability of detecting an “event of interest” against a noisy background, subject to a constraint on the expected number of false signals. We use nonlinear programming to appropriately set detection thresholds taking into account the probability of an event of interest occurring somewhere in the coverage area. Using this approach, public health officials can “tune” their biosurveillance systems to optimally detect various threats, thereby allowing practitioners to focus their public health surveillance activities. Given some distributional assumptions, we derive a one-dimensional optimization methodology that allows for the efficient optimization of very large systems. We demonstrate that optimizing a syndromic surveillance system can improve its performance by 20-40%.     

Guest Editors' Introduction: Data Surveillance

The guest editors discuss data surveillance. Proponents hope that data surveillance technology will be able to anticipate and prevent terrorist attacks, detect disease outbreaks, and allow for detailed social science research--all without the corresponding risks to personal privacy because machines, not people, perform the surveillance.     

公共卫生地理信息的应用与展望

传染病防治已不再是单一国家的问题,全球任何一地的疫情也随时可能在下一刻影响到自己国家,因此完善的疾病监测体系成为最重要的防疫武器,而地理信息系统在其中扮演了关键的角色。经由疾病病例的时空分布可视化,即能快速辅助防疫策略的规划、施行与评估,达成决策支持的目标,另外整合空间统计方法的运用,可系统化与科学化地从大量历史资料中侦测出异常事件,以便公共卫生人员进行研判与调查。由于互联网、移动设备、社交媒体的大量普及使用而建立起了新兴社交网络,开启了由下而上的民众监测体系,跳脱了传统式官方自上而下的权威式公共卫生治理,不仅强化民众参与,更能从社区角度提早至病患就医前就能掌握疫情走向,提升社区侦测效果,再辅以空间资讯对风险区提早做适当的介入。因此地理信息系统除了传统回溯性的疾病聚集研究外,也能有前瞻性防杜下一波流行的功效。     

DIVA: Exploration and Validation of Hypothesized Drug‐Drug Interactions

Adverse reactions caused by drug‐drug interactions are a major public health concern. Currently, adverse reaction signals are detected through a tedious manual process in which drug safety analysts review a large number of reports collected through post‐marketing drug surveillance. While computational techniques in support of this signal analysis are necessary, alone they are not sufficient. In particular, when machine learning techniques are applied to extract candidate signals from reports, the resulting set is (1) too large in size, i.e., exponential to the number of unique drugs and reactions in reports, (2) disconnected from the underlying reports that serve as evidence and context, and (3) ultimately requires human intervention to be validated in the domain context as a true signal warranting action. In this work, we address these challenges though a visual analytics system, DIVA, designed to align with the drug safety analysis workflow by supporting the detection, screening, and verification of candidate drug interaction signals. DTVA's abstractions and encodings are informed by formative interviews with drug safety analysts. DIVA's coordinated visualizations realize a proposed novel augmented interaction data model (AIM) which links signals generated by machine learning techniques with domain‐specific metadata critical for signal analysis. DIVA's alignment with the drug review process allows an analyst to interactively screen for important signals, triage signals for in‐depth investigation, and validate signals by reviewing the underlying reports that serve as evidence. The evaluation of DIVA encompasses case‐studies and interviews by drug analysts at the US Food and Drug Administration ‐ both of which confirm that DIVA indeed is effective in supporting analysts in the critical task of exploring and verifying dangerous drug‐drug interactions.     

An information supply chain system view for managing rare infectious diseases: The need to improve timeliness

Timely reporting of rare infectious disease cases to the public health system, especially after identification at laboratories, is essential to initiate quick and effective public health response. To ensure that the public health reporting system is appropriately monitoring the rare infectious diseases under surveillance, it is recommended to have a regular assessment of timeliness, especially after the rare infectious case is confirmed. This study aimed to evaluate the timeliness of data reported to the Ohio Disease Reporting System (ODRS), a public health reporting system in Ohio, for managing rare infectious diseases. In a cross-sectional analysis of rare infectious disease reporting data in four local health jurisdictions (LHJs) in the state of Ohio, wide delays were found between various reporting steps, particularly when the laboratories were not using the electronic method of reporting, and the delay observed was mainly at the hospital level and at the LHJ level. This study highlights the supply chain nature of information transfer and calculates the delay at various interacting points of the information supply chain system. The results establish that a centralized approach with an electronic disease reporting system conveys information faster than traditional reporting channels (decentralized approach). Delays of the decentralized approach are isolated at various stakeholder levels and with respect to various types of rare infectious diseases for better understanding of the information supply chain system for managing rare infectious diseases.     

A real-time temporal Bayesian architecture for event surveillance and its application to patient-specific multiple disease outbreak detection

Reliable and accurate detection of disease outbreaks remains an important research topic in disease outbreak surveillance. A temporal surveillance system bases its analysis on data not only from the most recent time period, but also on data from previous time periods. A non-temporal system only looks at data from the most recent time period. There are two difficulties with a non-temporal system when it is used to monitor real data which often contain noise. First, it is prone to produce false positive signals during non-outbreak time periods. Second, during an outbreak, it tends to release false negative signals early in the outbreak, which can adversely affect the decision making process of the user of the system. We conjecture that by converting a non-temporal system to a temporal one, we may attenuate these difficulties inherent in a non-temporal system. In this paper, we propose a Bayesian network architecture for a class of temporal event surveillance models called BayesNet-T. Using this Bayesian network architecture, we can convert certain non-temporal surveillance systems to temporal ones. We apply this architecture to a previously developed non-temporal multiple-disease outbreak detection system called PC and create a temporal system called PCT. PCT takes Emergency Department (ED) patient chief complaint data as its input. The PCT system was constructed using both data (non-outbreak diseases) and expert assessments (outbreak diseases). We compare PCT to PC using a real influenza outbreak. Furthermore, we compare PCT to both PC and the classic statistical methods CUSUM and EWMA using a total of 240 influenza and Cryptosporidium disease outbreaks created by injecting stochastically simulated outbreak cases into real ED admission data. Our results indicate that PCT has a smaller mean time to detection than PC at low false alarm rates, and that PCT is more stable than PC in that once an outbreak is detected, PCT is better at maintaining the detection signal on future days.     

地市级疾病监测干预系统设计与实现

疾病监测干预是公共卫生事件应急反应的重要手段,根据地市级建设疾病监测干预系统的特点,给出了系统实现的架构与业务流程,并对基于MapABC的前台重难点实现作了详细说明。     

Syndromic surveillance of Flu on Twitter using weakly supervised temporal topic models

Surveillance of epidemic outbreaks and spread from social media is an important tool for governments and public health authorities. Machine learning techniques for nowcasting the Flu have made significant inroads into correlating social media trends to case counts and prevalence of epidemics in a population. There is a disconnect between data-driven methods for forecasting Flu incidence and epidemiological models that adopt a state based understanding of transitions, that can lead to sub-optimal predictions. Furthermore, models for epidemiological activity and social activity like on Twitter predict different shapes and have important differences. In this paper, we propose two temporal topic models (one unsupervised model as well as one improved weakly-supervised model) to capture hidden states of a user from his tweets and aggregate states in a geographical region for better estimation of trends. We show that our approaches help fill the gap between phenomenological methods for disease surveillance and epidemiological models. We validate our approaches by modeling the Flu using Twitter in multiple countries of South America. We demonstrate that our models can consistently outperform plain vocabulary assessment in Flu case-count predictions, and at the same time get better Flu-peak predictions than competitors. We also show that our fine-grained modeling can reconcile some contrasting behaviors between epidemiological and social models.     

Identifying Critical Components of a Public Transit System for Outbreak Control

Modern public transport networks provide an efficient medium for the spread of infectious diseases within a region. The ability to identify components of the public transit system most likely to be carrying infected individuals during an outbreak is critical for public health authorities to be able to plan for outbreaks, and control their spread. In this study we propose a novel network structure, denoted as the vehicle trip network, to capture the dynamic public transit ridership patterns in a compact form, and illustrate how it can be used for efficient detection of the high risk network components. We evaluate a range of network-based statistics for the vehicle trip network, and validate their ability to identify the routes and individual vehicles most likely to spread infection using simulated epidemic scenarios. A variety of outbreak scenarios are simulated, which vary by their set of initially infected individuals and disease parameters. Results from a case study using the public transit network from Twin Cities, MN are presented. The results indicate that the set of transit vehicle trips at highest risk of infection can be efficiently identified, and are relatively robust to the initial conditions of the outbreak. Furthermore, the methods are illustrated to be robust to two types of data uncertainty, those being passenger infection levels and travel patterns of the passengers.     

Verification servers: Enabling analysts to assess the quality of inferences from public use data

To protect confidentiality, statistical agencies typically alter data before releasing them to the public. Ideally, although generally not done, the agency also provides a way for secondary data analysts to assess the quality of inferences obtained with the released data. Quality measures can help secondary data analysts to identify inaccurate conclusions resulting from the disclosure limitation procedures, as well as have confidence in accurate conclusions. We propose a framework for an interactive, web-based system that analysts can query for measures of inferential quality. As we illustrate, agencies seeking to build such systems must consider the additional disclosure risks from releasing quality measures. We suggest some avenues of research on limiting these risks.     

Geospatial information infrastructures to address spatial needs in health: Collaboration,challenges and opportunities

Most health-related issues such as public health outbreaks and epidemiological threats are better understood from a spatial–temporal perspective and, clearly demand related geospatial datasets and services so that decision makers may jointly make informed decisions and coordinate response plans. Although current health applications support a kind of geospatial features, these are still disconnected from the wide range of geospatial services and datasets that geospatial information infrastructures may bring into health. In this paper we are questioning the hypothesis whether geospatial information infrastructures, in terms of standards-based geospatial services, technologies, and data models as operational assets already in place, can be exploited by health applications for which the geospatial dimension is of great importance. This may be certainly addressed by defining better collaboration strategies to uncover and promote geospatial assets to the health community. We discuss the value of collaboration, as well as the opportunities that geographic information infrastructures offer to address geospatial challenges in health applications.     

Data mining techniques on satellite images for discovery of risk areas

The high rates of cholera epidemic mortality in less developed countries is a challenge for health facilities to which it is necessary to equip itself with the epidemiological surveillance. To strengthen the capacity of epidemiological surveillance, this paper focuses on remote sensing satellite data processing using data mining methods to discover risk areas of the epidemic disease by connecting the environment, climate and health. These satellite data are combined with field data collected during the same set of periods in order to explain and deduct the causes of the epidemic evolution from one period to another in relation to the environment. The existing technical (algorithms) for processing satellite images are mature and efficient, so the challenge today is to provide the most suitable means allowing the best interpretation of obtained results. For that, we focus on supervised classification algorithm to process a set of satellite images from the same area but on different periods. A novel research methodology (describing pre-treatment, data mining, and post-treatment) is proposed to ensure suitable means for transforming data, generating information and extracting knowledge. This methodology consists of six phases: (1.A) Acquisition of information from the field about epidemic, (1.B) Satellite data acquisition, (2) Selection and transformation of data (Data derived from images), (3) Remote sensing measurements, (4) Discretization of data, (5) Data treatment, and (6) Interpretation of results. The main contributions of the paper are: to establish the nature of links between the environment and the epidemic, and to highlight those risky environments when the public awareness of the problem and the prevention policies are absolutely necessary for mitigation of the propagation and emergence of the epidemic. This will allow national governments, local authorities and the public health officials to effective management according to risk areas. The case study concerns the knowledge discovery in databases related to risk areas of the cholera epidemic in Mopti region, Mali (West Africa). The results generate from data mining association rules indicate that the level of the Niger River in the wintering periods and some societal factors have an impact on the variation of cholera epidemic rate in Mopti town. More the river level is high, at 66% the rate of contamination is high.     

Influenza-like illness prediction using a long short-term memory deep learning model with multiple open data sources

The influenza problem has always been an important global issue. It not only affects people’s health problems but is also an essential topic of governments and health care facilities. Early prediction and response is the most effective control method for flu epidemics. It can effectively predict the influenza-like illness morbidity, and provide reliable information to the relevant facilities. For social facilities, it is possible to strengthen epidemic prevention and care for highly sick groups. It can also be used as a reminder for the public. This study collects information on the influenza-like illness emergency department visits to the Taiwan Centers for Disease Control, and the PM_2.5 open-source data from the Taiwan Environmental Protection Administration's air quality monitoring network. By using deep learning techniques, the relevance of short-term estimates and the outbreak calculation method can be determined. The techniques are published by the WHO to determine whether the influenza-like illness situation is still in a stage of reasonable control. Finally, historical data and future forecasted data are integrated on the web page for visual presentation, to show the actual regional air quality situation and influenza-like illness data and to predict whether there is an outbreak of influenza in the region.

     

公共区域监控视频数据目标特征跟踪定位方法

为了提高公共区域监控视频的目标定位检测能力,需要进行目标特征跟踪定位算法设计,提出一种基于图像超分辨率重建的公共区域监控视频数据目标特征跟踪定位方法。构建公共区域监控视频的三维图像重建模型,采用边缘层的高分辨融合方法进行公共区域监控视频图像数据的三维结构重组,提取公共区域监控视频的关键特征点,用图像退化模型进行公共区域监控视频数据目标特征检测,结合线性滤波模型使得监测输出图像满足最优匹配特征解,提高对公共区域监控视频数据目标特征跟踪能力。引入引导滤波方法进行公共区域监控视频数据的图像超分辨重建,实现对目标特征准确跟踪定位。仿真结果表明,采用该方法进行公共区域监控视频数据目标特征跟踪定位的准确性较高,图像重建能力较强,归一化均方根误差较小。     

Formal interaction specification in public health surveillance systems using pi-calculus

This paper provides formal specification of interactions in typical public health surveillance systems involving healthcare agencies at local, state and federal levels. Although few standards exist for exchange of healthcare information, there is a general lack of formal models of the protocols involved in the interactions between the agencies. The quality of medical care provided is an end result of a well designed choreography of diverse services provided by different healthcare entities. One of the major challenges in this field appears to be explicit formal specification of such interactions. Such formal specification work is the first step leading to both design and verification of important properties of public healthcare systems. pi-calculus is a formal modeling technique for precise specification of semantics in interacting concurrent systems where mobility is involved. Two different configurations of public health surveillance systems are modelled using pi-calculus in this paper.