197-31: Biosurveillance and Outbreak Detection Using the ARIMA and LOGISTIC Procedures

نویسندگان

  • Ernest S. Shtatland
  • Ken Kleinman
  • Emily M. Cain
چکیده

The main objective of this paper is to show potential usefulness of the combination of autoregressive integrated moving average (ARIMA) models and logistic regression with automatic model selection (see our work presented at SUGI’28 and SUGI’29.) Timeseries analysis with ARIMA provides only one perspective of the information in the surveillance data (i.e. the number of patients as a function of time). The information about the geographical location of the patients provides a second perspective. We would like to combine both perspectives.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Another Look at Low-Order Autoregressive Models in Early Detection of Epidemic Outbreaks and Explosive Behaviors in Economic and Financial Time Series

In our SUGI 2006 presentation, we suggested using low-order autoregressive models, AR(1) and AR(2), in biosurveillance and outbreak detection (PROC ARIMA, SAS/ETS). Our suggestion was based on empirical data. In the NESUG 2007 paper, we proposed strong theoretical grounds for this. Here we provide further development of our approach. Based on a classic susceptibleinfectious-recovered (SIR) mode...

متن کامل

Estimating the joint disease outbreak-detection time when an automated biosurveillance system is augmenting traditional clinical case finding

The goals of automated biosurveillance systems are to detect disease outbreaks early, while exhibiting few false positives. Evaluation measures currently exist to estimate the expected detection time of biosurveillance systems. Researchers also have developed models that estimate clinician detection of cases of outbreak diseases, which is a process known as clinical case finding. However, littl...

متن کامل

Quantifying the determinants of outbreak detection performance through simulation and machine learning

OBJECTIVE To develop a probabilistic model for discovering and quantifying determinants of outbreak detection and to use the model to predict detection performance for new outbreaks. MATERIALS AND METHODS We used an existing software platform to simulate waterborne disease outbreaks of varying duration and magnitude. The simulated data were overlaid on real data from visits to emergency depar...

متن کامل

Statistical Approach to Biosurveillance in Crisis: What is Next?

Motivated by the threat of bioterrorism, biosurveillance / syndromic surveillance systems are now in crisis: with the original purpose of early detection, and more than 10 years in existence, no health department has reported using them for this purpose. This has led to a shift away from only early detection of bioterrorist attacks. The goal has been expanded in two directions: firstly, to incl...

متن کامل

Biosurveillance and Outbreak Detection

Faced with the very real threat of bioterrorism, the critical need for early detection of an outbreak has shortened the time frame for major enhancements to our public health infrastructure. The early detection of covert biological attacks requires real time data streams revealing of the health of the population, as well as novel methods to detect abnormalities.

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2006