EconPapers    
Economics at your fingertips  
 

Non-parametric Back-projection of HIV Positive Tests using Multinomial and Poisson Settings

P. H. Chau and Paul Yip

Journal of Applied Statistics, 2004, vol. 31, issue 5, 553-564

Abstract: Back-projection is a commonly used method in reconstructing HIV incidence. Instead of using AIDS incidence data in back-projection, this paper uses HIV positive tests data. Both multinomial and Poisson settings are used. The two settings give similar results when a parametric form or step function is assumed for the infection curve. However, this may not be true when the HIV infection in each year is characterized by a different parameter. This paper attempts to use simulation studies to compare these two settings by constructing various scenarios for the infection curve. Results show that both methods give approximately the same estimates of the number of HIV infections in the past, whilst the estimates for HIV infections in the recent past differ a lot. The multinomial setting always gives a levelling-off pattern for the recent past, while the Poisson setting is more sensitive to the change in the shape of the HIV infection curve. Nonetheless, the multinomial setting gives a relatively narrower point-wise probability interval. When the size of the epidemic is large, the narrow probability interval may be under-estimating the true underlying variation.

Keywords: Back-calculation; Back-projection; Diagnoses; Hiv/AIDS; Hong Kong; Incidence; Multinomial; Poisson; Simulation (search for similar items in EconPapers)
Date: 2004
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/02664760410001681792 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:31:y:2004:i:5:p:553-564

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664760410001681792

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-20
Handle: RePEc:taf:japsta:v:31:y:2004:i:5:p:553-564