American Journal of Theoretical and Applied Statistics
Volume 5, Issue 5, September 2016, Pages: 326-333
Received: Sep. 9, 2016;
Accepted: Sep. 21, 2016;
Published: Oct. 10, 2016
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Vincent Moshi Ouma, Applied Statistics, Department of Statistics and Actuarial Sciences, College of Pure and Applied Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Samuel Musili Mwalili, Department of Statistics and Actuarial Sciences, College of Pure and Applied Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Anthony Wanjoya Kiberia, Department of Statistics and Actuarial Sciences, College of Pure and Applied Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Traditionally, statistical models provide a general basis for analysis of infectious disease count data with its unique characteristics such as low disease counts, underreporting, reporting delays, seasonality, past outbreaks and lack of a number of susceptible. Through this approach, statistical models have provided a popular means of estimating safety performance of various health elements. Predictions relating to infectious disease outbreaks by use of statistical models have been based on Poisson modeling framework and Negative Binomial (NB) modeling framework in the case of overdispersion within the count data. Recent studies have proved that the Poisson- Inverse Gaussian (PIG) model can be used to analyze count data that is highly overdispersed which cannot be effectively analyzed by the traditional Negative Binomial model. A PIG model with fixed/varying dispersion parameters is fitted to two infectious disease datasets and its performance in terms of goodness-of-fit and future outbreak predictions of infectious disease is compared to that of the traditional NB model.
Vincent Moshi Ouma,
Samuel Musili Mwalili,
Anthony Wanjoya Kiberia,
Poisson Inverse Gaussian (PIG) Model for Infectious Disease Count Data, American Journal of Theoretical and Applied Statistics.
Vol. 5, No. 5,
2016, pp. 326-333.
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