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Abstract:

Generating multivariate Poisson random variables is essential in many applications, such as multi echelon supply chain systems, multi-item / multi-period pricing models, accident monitoring systems, etc. Current simulation methods suffer from limitations ranging from computational complexity to restrictions on the structure of the correlation matrix, and therefore are rarely used in management science. Instead, multivariate Poisson data are commonly approximated by either univariate Poisson or multivariate Normal data. However, these approximations are often not adequate in practice. In this paper, we propose a conceptually appealing correction for NORTA (NORmal To Anything) for generating multivariate Poisson data with a flexible correlation structure and rates. NORTA is based on simulating data from a multivariate Normal distribution and converting it to an arbitrary continuous distribution with a specific correlation matrix. We show that our method is both highly accurate and computationally efficient. We also show the managerial advantages of generating multivariate Poisson data over univariate Poisson or multivariate Normal data.

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Abstract:

We describe a wavelet-based automated algorithm for
detecting disease outbreaks in temporal syndromic data.
We describe the method, which improves upon the
Goldenberg et al. (2002) algorithm and its implementation
on a diverse set of real syndromic data from multiple data
sources and multiple geographical locations. Our results
show a robust performance which is comparable to a few
recently suggested methods.

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URL:

Abstract:

While many methods have been proposed for detecting disease
outbreaks from pre-diagnostic data, their performance is usually not
well understood. We argue that most existing temporal detection methods
for biosurveillance can be characterized as a forecasting component
coupled with a monitoring/detection component. In this paper, we
describe the effect of forecast accuracy on detection performance. Quantifying this effect allows one to measure the benefits of improved
forecasting and determine when it is worth improving a forecast
method’s precision at the cost of robustness or simplicity. We quantify
the effect of forecast accuracy on detection metrics under different
scenarios and investigate the effect when standard assumptions are
violated. We illustrate our results by examining performance on
authentic biosurveillance data.