Spinned Poisson distribution with health management application.

Health Care Manag Sci

School of Health Administration, Texas State University, San Marcos, TX 78666, USA.

Published: December 2011

AI Article Synopsis

  • The study examines data collection challenges during infectious disease outbreaks, highlighting how quick removal of infected individuals complicates accurate incidence rate estimation.
  • The traditional Poisson distribution is found unsuitable for this context, prompting the development of a new probability distribution called the spinned Poisson distribution.
  • The properties of the spinned Poisson distribution are explored and demonstrated using historical data on smallpox incidence in Abakaliki, Nigeria.

Article Abstract

Consider a data collection setup during a spread of an infectious disease. Examples include severe acute respiratory syndrome (SARS) or influenza A virus H3N2. The health management in such scenarios quickly removes the infected cases before the data collection is completed. The estimate of the incidence rate or the chance for anyone to be immune requires using a correct probability distribution. The usual Poisson distribution is inappropriate because of the impact of removing infected cases on the incidence rate as well as observing a new case. An appropriate new probability distribution is derived and is named as spinned Poisson distribution. The statistical properties of the spinned Poisson distribution are developed and illustrated using infectious smallpox incidence in Abakaliki, Nigeria.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7087638PMC
http://dx.doi.org/10.1007/s10729-011-9157-8DOI Listing

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