Wise in Vaccine Allocation
- URL: http://arxiv.org/abs/2306.07223v1
- Date: Mon, 12 Jun 2023 16:30:53 GMT
- Title: Wise in Vaccine Allocation
- Authors: Baiqiao Yin, Jiaqing Yuan, Weichen Lv, Jiehui Huang, Guian Fang
- Abstract summary: The paper uses machine learning and mathematical modeling to predict future vaccine distribution and solve the problem of allocating vaccines to different types of hospitals.
They created a model and allocate vaccines to central and community hospitals and health centers in Hangzhou Gongshu District and Harbin Daoli District based on the model.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The paper uses machine learning and mathematical modeling to predict future
vaccine distribution and solve the problem of allocating vaccines to different
types of hospitals. They collected data and analyzed it, finding factors such
as nearby residents, transportation, and medical personnel that impact
distribution. They used the results to create a model and allocate vaccines to
central and community hospitals and health centers in Hangzhou Gongshu District
and Harbin Daoli District based on the model. They provide an explanation for
the vaccine distribution based on their model and conclusions.
Related papers
- Modeling the amplification of epidemic spread by misinformed populations [41.31724592098777]
We employ an epidemic model that incorporates a large, mobility-informed physical contact network as well as the distribution of misinformed individuals across counties derived from social media data.
We present a worst-case scenario in which a heavily misinformed population would result in an additional 14% of the U.S. population becoming infected over the course of the COVID-19 epidemic.
arXiv Detail & Related papers (2024-02-17T18:01:43Z) - Agent-Based Model: Simulating a Virus Expansion Based on the Acceptance
of Containment Measures [65.62256987706128]
Compartmental epidemiological models categorize individuals based on their disease status.
We propose an ABM architecture that combines an adapted SEIRD model with a decision-making model for citizens.
We illustrate the designed model by examining the progression of SARS-CoV-2 infections in A Coruna, Spain.
arXiv Detail & Related papers (2023-07-28T08:01:05Z) - Accurate Measures of Vaccination and Concerns of Vaccine Holdouts from
Web Search Logs [31.231365080959247]
We show how large-scale search engine logs and machine learning can be leveraged to fill gaps in vaccine data.
We develop a vaccine intent classifier that can accurately detect when a user is seeking the COVID-19 vaccine on search.
We use our classifier to identify two groups, vaccine early adopters and vaccine holdouts.
arXiv Detail & Related papers (2023-06-12T23:19:55Z) - Dense Feature Memory Augmented Transformers for COVID-19 Vaccination
Search Classification [60.49594822215981]
This paper presents a classification model for detecting COVID-19 vaccination related search queries.
We propose a novel approach of considering dense features as memory tokens that the model can attend to.
We show that this new modeling approach enables a significant improvement to the Vaccine Search Insights (VSI) task.
arXiv Detail & Related papers (2022-12-16T13:57:41Z) - Doctors vs. Nurses: Understanding the Great Divide in Vaccine Hesitancy
among Healthcare Workers [64.1526243118151]
We find that doctors are overall more positive toward the COVID-19 vaccines.
Doctors are more concerned with the effectiveness of the vaccines over newer variants.
Nurses pay more attention to the potential side effects on children.
arXiv Detail & Related papers (2022-09-11T14:22:16Z) - VacciNet: Towards a Smart Framework for Learning the Distribution Chain
Optimization of Vaccines for a Pandemic [0.0]
We put forward a novel framework leveraging Supervised Learning and Reinforcement Learning (RL) which we call VacciNet.
RL is capable of learning to predict the demand of vaccination in a state of a country as well as suggest optimal vaccine allocation in the state for minimum cost of procurement and supply.
arXiv Detail & Related papers (2022-08-01T19:37:33Z) - Applying Machine Learning and AI Explanations to Analyze Vaccine
Hesitancy [0.0]
The paper quantifies the impact of race, poverty, politics, and age on vaccination rates in U.S. counties.
It is apparent that the influence of impact factors is not universally the same across different geographies.
arXiv Detail & Related papers (2022-01-07T22:50:17Z) - Validating Optimal COVID-19 Vaccine Distribution Models [7.227440688079006]
We propose a clustering-based solution to select optimal distribution centers and a Constraint Satisfaction Problem framework to optimally distribute the vaccines.
We demonstrate the efficiency of the proposed models using real-world data obtained from the district of Chennai, India.
arXiv Detail & Related papers (2021-02-03T21:54:47Z) - Predicting seasonal influenza using supermarket retail records [59.18952050885709]
We consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets.
We make use of the Support Vector Regression (SVR) model to produce the predictions of seasonal flu incidence.
arXiv Detail & Related papers (2020-12-08T16:30:43Z) - Falling into the Echo Chamber: the Italian Vaccination Debate on Twitter [65.7192861893042]
We examine the extent to which the vaccination debate on Twitter is conductive to potential outreach to the vaccination hesitant.
We discover that the vaccination skeptics, as well as the advocates, reside in their own distinct "echo chambers"
At the center of these echo chambers we find the ardent supporters, for which we build highly accurate network- and content-based classifiers.
arXiv Detail & Related papers (2020-03-26T13:55:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.