ArticleSustainable cities and society2021
A two-step vaccination technique to limit COVID-19 spread using mobile data.
Article in Sustainable cities and society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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
10 citing papers in PubMed.
- Optimizing vaccine site locations while considering travel inconvenience and public health outcomes.Health care management science · 2026Article
- Optimization Modeling for Pandemic Vaccine Supply Chain Management: A Review and Future Research Opportunities.Naval research logistics · 2024Article
- Estimation and sensitivity analysis of a COVID-19 model considering the use of face mask and vaccination.Scientific reports · 2023Article
- Review
- Simulating a Hockey Hub COVID-19 Mass Vaccination Facility.Healthcare (Basel, Switzerland) · 2022Article
- On male urination and related environmental disease transmission in restrooms: From the perspectives of fluid dynamics.Sustainable cities and society · 2022Article
- Spatiotemporal patterns of the COVID-19 control measures impact on industrial production in Wuhan using time-series earth observation data.Sustainable cities and society · 2021Article
- Seesaw scenarios of lockdown for COVID-19 pandemic: Simulation and failure analysis.Sustainable cities and society · 2021Article
- Digital contact tracing, community uptake, and proximity awareness technology to fight COVID-19: a systematic review.Sustainable cities and society · 2021Article
- Optimization in the Context of COVID-19 Prediction and Control: A Literature Review.IEEE access : practical innovations, open solutions · 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Vaccination is one of the most effective methods to prevent the spread of infectious diseases, but due to limitations in vaccines' availability, especially when faced with a new disease such as COVID-19, not all individuals in the community can be vaccinated. A limited number of candidates should be selected when the supply of vaccines is limited. In this paper, a method is introduced to prioritize the individuals for vaccination in order to achieve the best results in preventing the spread of COVID-19. We divide this problem into two steps: vaccine allocation and targeted vaccination. In vaccine allocation, vaccines are allocated among different population. An algorithm is proposed by defining the maximization of the total immunity among populations as an optimization problem. The aim of the targeted vaccination step is to select the individuals in each population that when vaccinated, create the greatest reduction in the transmission paths of the disease. The contact tracing data for this step is obtained from wireless communication networks and is modeled using graph theory. A metric is presented for selection of the candidates, based on centrality metrics. Simulations indicate that a 30% drop in infection rate could be achieved compared to random vaccination.
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.