Universal Tool for Vaccine Scheduling: Applications for Children and Adults

• Published in: Interfaces (Providence) 2011-09, Vol.41 (5), p.436-454
• Main Authors: Smalley, Hannah K., Keskinocak, Pinar, Engineer, Faramroze G., Pickering, Larry K.
• Format: Article
• Language: English
• Subjects: Administered dose; Adolescents; Adults; Age; Algorithms; applications; Canada; Children; Children & youth; Decision making; Decision making models; Decision support systems; Disease control; Disease prevention; Dosage; Dosage and administration; Dynamic programming; Equipment and supplies; Experiments; Health care; Immunization; Immunization schedules; information systems; Management; Management science; Medical personnel; Methods; Operations research; Recommendations; Schedules; Scheduling; Scheduling (Management); Scheduling algorithms; Studies; Teenagers; Tetanus; U.S.A; User interface; Vaccination; Vaccines
• ISSN: 0092-2102; 2644-0865; 1526-551X; 2644-0873
• DOI: 10.1287/inte.1110.0583

To improve coverage against vaccine-preventable diseases for children and adults, and to aid caretakers and providers in making appropriate and timely vaccination decisions, Georgia Institute of Technology collaborated with the Centers for Disease Control and Prevention to develop decision support tools for creating optimized catch-up immunization schedules for four target groups: children through age 6, adolescents ages 7 through 18, adults ages 19 and over in the United States, and children and adolescents through age 19 in Canada. Our solution to the catch-up scheduling problem for each targeted group determines the best coverage schedule for each individual given his (her) vaccination history and age. If an individual misses one or more doses of a recommended vaccine, a health-care professional is typically responsible for generating a feasible catch-up schedule that optimizes the person's coverage against vaccine-preventable diseases, a task that is often challenging and time consuming. Inappropriate schedules could prevent some individuals from being vaccinated in a timely manner, potentially increasing their risk of contracting a disease. Each decision support tool uses a dynamic programming algorithm to construct recommended immunization schedules in an optimized manner. These tools simplify the tedious process of manually constructing immunization schedules, expedite the process, and eliminate errors.