Using Monte Carlo simulation to assess the value of combination vaccines for pediatric immunization
Pages 1421 - 1428
Abstract
Research by vaccine manufacturers has resulted in the development of new vaccines that protect against a number of diseases. This has created a dilemma for how to introduce such new vaccines into an already crowded Recommended Childhood Immunization Schedule and prompted the development of vaccine products that combine several individual vaccines into a single injection. Such combination vaccines permit new vaccines to be inserted into the immunization schedule without exposing children to an unacceptable number of injections during a single clinic visit. This paper describes a Monte Carlo simulation with an integer programming model to assess and quantify the distributions around inclusion prices which reflect the economic premium of these new combinations. Each new vaccine competed against existing vaccines for six childhood diseases (hepatitis B, diphtheria, tetanus, pertussis, Haemophilus influenzae type b, and polio) at their March 2000 Federal contract discount prices.
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- Using Monte Carlo simulation to assess the value of combination vaccines for pediatric immunization
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Published In
December 2001
1595 pages
ISBN:078037309X
- Conference Chair:
- Matt Rohrer,
- Program Chair:
- Deb Medeiros,
- Publications Chair:
- Mark Grabau
Sponsors
- IIE: Institute of Industrial Engineers
- INFORMS/CS: Institute for Operations Research and the Management Sciences/College on Simulation
- ASA: American Statistical Association
- ACM: Association for Computing Machinery
- SIGSIM: ACM Special Interest Group on Simulation and Modeling
- IEEE/CS: Institute of Electrical and Electronics Engineers/Computer Society
- NIST: National Institute of Standards and Technology
- IEEE/SMCS: Institute of Electrical and Electronics Engineers/Systems, Man, and Cybernetics Society
- SCS: The Society for Computer Simulation International
Publisher
IEEE Computer Society
United States
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Published: 09 December 2001
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WSC '01 Paper Acceptance Rate 111 of 155 submissions, 72%;
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