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Pediatric immunization is important for preventing potentially life-threatening diseases in children. Over time, the number of recommended pediatric vaccines has increased and is likely to increase further as new vaccines are developed. Given the different number of doses for available vaccines and various constraints (e.g., the appropriate age for each dose of a vaccine or the time between doses), it is challenging to develop a recommended vaccination schedule or a catch-up schedule when a child falls behind on one or more vaccinations. We developed an integer programming optimization model, enabled by Python programming and embedded into an Excel-based decision tool, to recommend childhood vaccination schedules or personalized catch-up schedules. The model recommends a vaccination schedule that balances the goal of being as close as possible to the clinically-indicated dosing schedules and the goal of minimizing clinic visits, and gives users the ability to trade off between these two goals. We illustrated the broad applicability of our proposed model with commonly-faced vaccine scheduling challenges in the United States. The illustrative computational case study confirms our model's ability to create personalized schedules based on each child's age and vaccination history, and to adjust appropriately when a new vaccine becomes available. The model presented in this paper fills the need for an easy-to-use tool to recommend vaccination schedules for de novo and catch-up purposes. It provides straightforward recommendations that can be easily used by physicians, is flexible to handle the requirements varying by region, and can be updated as new vaccines are approved for use. [ABSTRACT FROM AUTHOR]

Despite high vaccine coverage rates in children and efficacy of pneumococcal conjugate vaccines, invasive pneumococcal disease (IPD) episodes due to serotypes included in the vaccine following completion of the recommended course of immunisation (i.e. vaccine failure) have been reported. We used data gathered from a population-based enhanced passive surveillance for IPD in children under 18 years of age in Massachusetts and an ensemble model composed of three machine-learning algorithms to predict probability of 13-valent pneumococcal conjugated vaccine (PCV13) failure and to evaluate potential associated features including age, underlying comorbidity, clinical presentation, and vaccine schedule. Vaccine failure was defined as diagnosis of IPD due to vaccine serotype (VST), in a child who received age recommended doses recommended by Advisory Committee of Immunization Practices. During the 7-year study period, between April 01, 2010 and March 31, 2017, we identified 296 IPD cases. There were 107 (36%) IPD cases caused by VST, mostly serotype 19A (49, 17%), 7F (21, 7%), and 3 (18, 6%). Thirty-seven (34%) were in children who were completely vaccinated representing 13% of all IPD cases. Vaccine failure was more likely among children older than 60 months (predicted probability 0.40, observed prevalence 0.37, model prediction accuracy 79%), children presenting with pneumonia (predicted probability 0.27, observed prevalence 0.31, model accuracy 77%), and children with underlying comorbidity (predicted probability 0.24, observed prevalence 0.23, model accuracy 96%). Vaccine failure probability for those >60 months of age and had an underlying risk factor was 45% (observed prevalence 0.33, model accuracy 82%). The likelihood of vaccine failure was lowest among children who had completed 3 primary doses plus one booster dose PCV13 (predicted probability 0.14, observed prevalence 0.14, model prediction accuracy 100%). PCV13 vaccine failure is more frequent among older children with underlying comorbidity, and among those who present with pneumococcal pneumonia. Our study provides a preliminary framework to predict the patterns of vaccine failures and may contribute to decision-making processes to optimize PCV immunization schedules. [ABSTRACT FROM AUTHOR]

Objective: To understand the value of information on vaccine inventory levels during an influenza pandemic, we propose a simulation study to compare vaccine allocation strategies using: (i) only population information (pro-rata, or population-based, PB), (ii) both population and vaccine inventory information (population and inventory-based, PIB). Methods: We adapt an agent-based simulation model to predict the spread of the disease both geographically and temporally. We study PB and PIB when uptake rates vary geographically. The simulation study is done from 2015 to 2017, using population and commuting data from the state of Georgia from the United States census. Findings: Compared to PB under reasonable scenarios, PIB reduces the infection attack rate from 23.4% to 22.4%, decreases the amount of leftover inventory from 827 to 152 thousand, and maintains or increases the percentage of vaccinated population. Conclusions: Our results indicate the need for greater vaccine inventory visibility in public health supply chains, especially when supply is limited, and uptake rates vary geographically. Such visibility has a potential to decrease the number of infections, help identify locations with low uptake rates and to motivate public awareness efforts. [ABSTRACT FROM AUTHOR]