ArticlePLoS computational biology2019
Optimizing spatial allocation of seasonal influenza vaccine under temporal constraints.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Optimal resource allocation model for COVID-19: a systematic review and meta-analysis.BMC infectious diseases · 2024Pooled it
- Efficient reporting delay calibration in spatial metapopulation models for reconstructing cross-regional epidemic dynamics.Biosafety and health · 2026Article
- An agent-based model of the effects of limited vaccination on novel respiratory infections.Journal of clinical epidemiology · 2026Article
- Epidemiological Assessment and Optimization of School-Based Influenza Vaccination - Shenzhen City, Guangdong Province, China, 2023-2024.China CDC weekly · 2025Article
- Novel multi-cluster workflow system to support real-time HPC-enabled epidemic science: Investigating the impact of vaccine acceptance on COVID-19 spread.Journal of parallel and distributed computing · 2024Article
- Data-driven mechanistic framework with stratified immunity and effective transmissibility for COVID-19 scenario projections.Epidemics · 2024Article
- Strategies and Trends in COVID-19 Vaccination Delivery: What We Learn and What We May Use for the Future.Vaccines · 2023Article
- Racial residential segregation and COVID-19 vaccine uptake: an analysis of Georgia USA county-level data.BMC public health · 2023Article
- Optimizing two-dose vaccine resource allocation to combat a pandemic in the context of limited supply: The case of COVID-19.Frontiers in public health · 2023Article
- Identifying high-risk areas for dog-mediated rabies using Bayesian spatial regression.One health (Amsterdam, Netherlands) · 2022Article
- Optimal control of the spatial allocation of COVID-19 vaccines: Italy as a case study.PLoS computational biology · 2022Article
- A decision support system for prioritised COVID-19 two-dosage vaccination allocation and distribution.Transportation research. Part E, Logistics and transportation review · 2022Article
- Dynamic resource allocation for controlling pathogen spread on a large metapopulation network.Journal of the Royal Society, Interface · 2022Article
- Evaluating strategies for spatial allocation of vaccines based on risk and centrality.Journal of the Royal Society, Interface · 2022Article
- Optimizing Spatio-Temporal Allocation of the COVID-19 Vaccine Under Different Epidemiological Landscapes.Frontiers in public health · 2022Article
- Combinational Recommendation of Vaccinations, Mask-Wearing, and Home-Quarantine to Control Influenza in Megacities: An Agent-Based Modeling Study With Large-Scale Trajectory Data.Frontiers in public health · 2022Article
- COVID-19 related interdisciplinary methods: Preventing errors and detecting research opportunities.Methods (San Diego, Calif.) · 2021Review
- Operational Considerations in Global Health Modeling.Pathogens (Basel, Switzerland) · 2021Article
- Efficient and effective single-step screening of individual samples for SARS-CoV-2 RNA using multi-dimensional pooling and Bayesian inference.Journal of the Royal Society, Interface · 2021Article
- Identifying US County-level characteristics associated with high COVID-19 burden.BMC public health · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Prophylactic interventions such as vaccine allocation are some of the most effective public health policy planning tools. The supply of vaccines, however, is limited and an important challenge is to optimally allocate the vaccines to minimize epidemic impact. This resource allocation question (which we refer to as VaccIntDesign) has multiple dimensions: when, where, to whom, etc. Most of the existing literature in this topic deals with the latter (to whom), proposing policies that prioritize individuals by age and disease risk. However, since seasonal influenza spread has a typical spatial trend, and due to the temporal constraints enforced by the availability schedule, the when and where problems become equally, if not more, relevant. In this paper, we study the VaccIntDesign problem in the context of seasonal influenza spread in the United States. We develop a national scale metapopulation model for influenza that integrates both short and long distance human mobility, along with realistic data on vaccine uptake. We also design GreedyAlloc, a greedy algorithm for allocating the vaccine supply at the state level under temporal constraints and show that such a strategy improves over the current baseline of pro-rata allocation, and the improvement is more pronounced for higher vaccine efficacy and moderate flu season intensity. Further, the resulting strategy resembles a ring vaccination applied spatiallyacross the US.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.