A method for detecting and characterizing outbreaks of infectious disease from clinical reports

Outbreaks of infectious disease can pose a significant threat to human health. Thus, detecting and characterizing outbreaks quickly and accurately remains an important problem. This paper describes a Bayesian framework that links clinical diagnosis of individuals in a population to epidemiological modeling of disease outbreaks in the population. Computer-based diagnosis of individuals who seek healthcare is used to guide the search for epidemiological models of population disease that explain the pattern of diagnoses well. We applied this framework to develop a system that detects influenza outbreaks from emergency department (ED) reports. The system diagnoses influenza in individuals probabilistically from evidence in ED reports that are extracted using natural language processing. These diagnoses guide the search for epidemiological models of influenza that explain the pattern of diagnoses well. Those epidemiological models with a high posterior probability determine the most likely outbreaks of specific diseases; the models are also used to characterize properties of an outbreak, such as its expected peak day and estimated size. We evaluated the method using both simulated data and data from a real influenza outbreak. The results provide support that the approach can detect and characterize outbreaks early and well enough to be valuable. We describe several extensions to the approach that appear promising.

[1]  Frank Diederich,et al.  Mathematical Epidemiology Of Infectious Diseases Model Building Analysis And Interpretation , 2016 .

[2]  Po-Huang Chiang,et al.  Probabilistic Daily ILI Syndromic Surveillance with a Spatio-Temporal Bayesian Hierarchical Model , 2010, PloS one.

[3]  Steven Babin,et al.  An integrated approach for fusion of environmental and human health data for disease surveillance , 2011, Statistics in medicine.

[4]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[5]  R. Serfling.  Methods for current statistical analysis of excess pneumonia-influenza deaths. , 1963, Public health reports.

[6]  Colleen A Bradley,et al.  BioSense: implementation of a National Early Event Detection and Situational Awareness System. , 2005, MMWR supplements.

[7]  Jialan Que,et al.  Probabilistic Case Detection for Disease Surveillance Using Data in Electronic Medical Records , 2011, Online Journal of Public Health Informatics.

[8]  Albert N. Shiryaev,et al.  Optimal Stopping Rules , 2011, International Encyclopedia of Statistical Science.

[9]  P. Sebastiani,et al.  A Bayesian dynamic model for influenza surveillance , 2006, Statistics in medicine.

[10]  Daniel Zeng,et al.  A comparative study of spatio-temporal hotspot analysis techniques in security informatics , 2004, Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749).

[11]  Richard White,et al.  An Introduction to Infectious Disease Modelling , 2010 .

[12]  Daniel Zeng,et al.  Prospective spatio-temporal data analysis for security informatics , 2005, Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005..

[13]  Michael M. Wagner,et al.  Handbook of biosurveillance , 2006 .

[14]  Alʹbert Nikolaevich Shiri︠a︡ev,et al.  Optimal stopping rules , 1977 .

[15]  Garrick L. Wallstrom,et al.  A Bayesian Network for Outbreak Detection and Prediction , 2006, AAAI.

[16]  R. Platt,et al.  A generalized linear mixed models approach for detecting incident clusters of disease in small areas, with an application to biological terrorism. , 2004, American journal of epidemiology.

[17]  M. Kulldorff.  Spatial Scan Statistics: Models, Calculations, and Applications , 1999 .

[18]  E. S. Page.  CONTINUOUS INSPECTION SCHEMES , 1954 .

[19]  F. Carrat,et al.  Monitoring epidemiologic surveillance data using hidden Markov models. , 1999, Statistics in medicine.

[20]  Bert Veenendaal,et al.  Disease surveillance using a hidden Markov model , 2009, BMC Medical Informatics Decis. Mak..

[21]  M. Kulldor,et al.  Prospective time-periodic geographical disease surveillance using a scan statistic , 2001 .

[22]  A. Cook,et al.  Real-Time Epidemic Monitoring and Forecasting of H1N1-2009 Using Influenza-Like Illness from General Practice and Family Doctor Clinics in Singapore , 2010, PloS one.

[23]  Andrew W. Moore,et al.  A Bayesian Spatial Scan Statistic , 2005, NIPS.

[24]  Emilia Vynnycky,et al.  Comprar An Introduction to Infectious Disease Modelling | Emilia Vynnycky | 9780198565765 | Oxford University Press , 2010 .

[25]  A S Neubauer,et al.  The EWMA control chart: properties and comparison with other quality-control procedures by computer simulation. , 1997, Clinical chemistry.

[26]  Adnan Darwiche,et al.  Modeling and Reasoning with Bayesian Networks , 2009 .

[27]  Jun Zhang,et al.  Detection of Outbreaks from Time Series Data Using Wavelet Transform , 2003, AMIA.

[28]  S B Thacker,et al.  A Bayesian Approach to the Detection of Aberrations in Public Health Surveillance Data , 1993, Epidemiology.

[29]  M. Kulldorff,et al.  A Space–Time Permutation Scan Statistic for Disease Outbreak Detection , 2005, PLoS medicine.

[30]  Howard S. Burkom,et al.  Methods Paper: Bayesian Information Fusion Networks for Biosurveillance Applications , 2009, J. Am. Medical Informatics Assoc..

[31]  Jeremy Ginsberg,et al.  Detecting influenza epidemics using search engine query data , 2009, Nature.

[32]  S. Hammer.  INTRODUCTION TO INFECTIOUS DISEASE , 2004 .

[33]  Kristina B Metzger,et al.  How many illnesses does one emergency department visit represent? Using a population-based telephone survey to estimate the syndromic multiplier. , 2004, MMWR supplements.

[34]  Fu-Chiang Tsui,et al.  Spatial and Temporal Algorithm Evaluation for Detecting Over-The-Counter Thermometer Sale Increases during 2009 H1N1 Pandemic , 2012, Online journal of public health informatics.

[35]  D. Cummings,et al.  Strategies for mitigating an influenza pandemic , 2006, Nature.

[36]  M. Abdelguerfi,et al.  Introduction 1.2 Parallel Database Systems 1.2.1 Computation Model 2 1.2 Parallel Database Systems Introduction Select * from Employee, Department Where (employee.dept_no @bullet Department.dept_no) and (employee.position = "manager") (a) Sql Request 1.2.2 Engineering Model , 2022 .

[37]  Gregory F. Cooper,et al.  A multivariate Bayesian scan statistic for early event detection and characterization , 2010, Machine Learning.

[38]  Gregory F. Cooper,et al.  A method for estimating from thermometer sales the incidence of diseases that are symptomatically similar to influenza , 2013, J. Biomed. Informatics.

[39]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[40]  Paola Sebastiani,et al.  Automated Detection of Influenza Epidemics with Hidden Markov Models , 2003, IDA.

[41]  F F Nobre,et al.  Dynamic linear model and SARIMA: a comparison of their forecasting performance in epidemiology , 2001, Statistics in medicine.

[42]  Branko Ristic,et al.  Predicting an epidemic based on syndromic surveillance , 2010, 2010 13th International Conference on Information Fusion.

[43]  Michael Wagner,et al.  Probabilistic, Decision-theoretic Disease Surveillance and Control , 2011, Online journal of public health informatics.

[44]  A. Owen,et al.  Safe and Effective Importance Sampling , 2000 .

[45]  이찬영,et al.  Recursive Least Squares 방식의 이송계모델 파라미터 식별 , 2016 .

[46]  N. Ferguson,et al.  Time lines of infection and disease in human influenza: a review of volunteer challenge studies. , 2008, American journal of epidemiology.

[47]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.