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Background: 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., Methods: 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., Results: 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., Conclusions: 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., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Yao-Hsuan Chen is an employee of MSD (UK) Limited, London, United Kingdom, and may own stock and/or hold stock options in the Company. This research collaboration was fully funded by the Company., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Objective: To assess the value of using SARS-CoV-2 specific antibody testing to prioritize the vaccination of susceptible individuals as part of a COVID-19 vaccine distribution plan when vaccine supply is limited., Methods: An extended susceptible-infected-recovered (SIR) compartmental model was used to simulate COVID-19 spread when considering diagnosis, isolation, and vaccination of a cohort of 1 million individuals. The scenarios modeled represented 4 pandemic severity scenarios and various times when the vaccine becomes available during the pandemic. Eligible individuals have a probability p of receiving antibody testing prior to vaccination (p = 0, 0.25, 0.5, 0.75, and 1). The vaccine was modeled as a single dose vaccine with 90% and 70% efficacy. The value of serology testing was evaluated by comparing the infection attack rate, peak infections, peak day, and deaths., Results: The use of antibody testing to prioritize the allocation of limited vaccines reduces infection attack rates and deaths. The size of the reduction depends on when the vaccine becomes available relative to the infection peak day. The largest percentage reduction in cases and deaths occurs when the vaccine is deployed before and close to the infection peak day. The reduction in the number of cases and deaths diminishes as vaccine deployment is delayed., Conclusions: Antibody testing as part of the vaccination plan is an effective method to maximize the benefit of a COVID-19 vaccine. Decision-makers need to consider relative timing between the infection peak day and when the vaccine becomes available., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dr. Yildirim reported being a member of the mRNA-1273 Study Group. Dr. Yildirim has received funding to her institution to conduct clinical research from BioFire, MedImmune, Regeneron, PaxVax, Pfizer, GSK, Merck, Novavax, Sanofi-Pasteur, and Micron. Dr. Keskinocak and Ms. Fujimoto do not have conflict of interest to disclose., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

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]