Vaccine allocation policy optimization and budget sharing mechanism
using Thompson sampling
- URL: http://arxiv.org/abs/2109.10004v1
- Date: Tue, 21 Sep 2021 07:22:03 GMT
- Title: Vaccine allocation policy optimization and budget sharing mechanism
using Thompson sampling
- Authors: David Rey, Ahmed W Hammad, Meead Saberi
- Abstract summary: We take the perspective of decision-making agents that aim to minimize the size of their susceptible populations.
We propose an optimization policy based on Thompson sampling to learn mean vaccine efficiency rates over time.
We show that under a fixed global vaccine allocation budget, most countries can reduce their national number of infections and deaths.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The optimal allocation of vaccines to population subgroups over time is a
challenging health care management problem. In the context of a pandemic, the
interaction between vaccination policies adopted by multiple agents and the
cooperation (or lack thereof) creates a complex environment that affects the
global transmission dynamics of the disease. In this study, we take the
perspective of decision-making agents that aim to minimize the size of their
susceptible populations and must allocate vaccine under limited supply. We
assume that vaccine efficiency rates are unknown to agents and we propose an
optimization policy based on Thompson sampling to learn mean vaccine efficiency
rates over time. Furthermore, we develop a budget-balanced resource sharing
mechanism to promote cooperation among agents. We apply the proposed framework
to the COVID-19 pandemic. We use a raster model of the world where agents
represent the main countries worldwide and interact in a global mobility
network to generate multiple problem instances. Our numerical results show that
the proposed vaccine allocation policy achieves a larger reduction in the
number of susceptible individuals, infections and deaths globally compared to a
population-based policy. In addition, we show that, under a fixed global
vaccine allocation budget, most countries can reduce their national number of
infections and deaths by sharing their budget with countries with which they
have a relatively high mobility exchange. The proposed framework can be used to
improve policy-making in health care management by national and global health
authorities.
Related papers
- Deep Reinforcement Learning for Efficient and Fair Allocation of Health Care Resources [47.57108369791273]
Scarcity of health care resources could result in the unavoidable consequence of rationing.
There is no universally accepted standard for health care resource allocation protocols.
We propose a transformer-based deep Q-network to integrate the disease progression of individual patients and the interaction effects among patients.
arXiv Detail & Related papers (2023-09-15T17:28:06Z) - Cooperating Graph Neural Networks with Deep Reinforcement Learning for
Vaccine Prioritization [0.0]
This study explores the vaccine prioritization strategy to reduce the overall burden of the pandemic when the supply is limited.
Existing methods conduct macro-level or simplified micro-level vaccine distribution by assuming the homogeneous behavior within subgroup populations.
We develop a novel deep reinforcement learning to seek the optimal vaccine allocation strategy for the high-degree spatial-temporal disease evolution system.
arXiv Detail & Related papers (2023-05-09T04:19:10Z) - Evaluating COVID-19 vaccine allocation policies using Bayesian $m$-top
exploration [53.122045119395594]
We present a novel technique for evaluating vaccine allocation strategies using a multi-armed bandit framework.
$m$-top exploration allows the algorithm to learn $m$ policies for which it expects the highest utility.
We consider the Belgian COVID-19 epidemic using the individual-based model STRIDE, where we learn a set of vaccination policies.
arXiv Detail & Related papers (2023-01-30T12:22:30Z) - 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) - Evaluating vaccine allocation strategies using simulation-assisted
causal modelling [7.9656669215132005]
Early on during a pandemic, vaccine availability is limited, requiring prioritisation of different population groups.
We develop a model to retrospectively evaluate age-dependent counterfactual vaccine allocation strategies against the COVID-19 pandemic.
We compare Israel's implemented vaccine allocation strategy in 2021 to counterfactual strategies such as no prioritisation, prioritisation of younger age groups or a strict risk-ranked approach.
arXiv Detail & Related papers (2022-12-14T14:24:17Z) - 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) - Optimal non-pharmaceutical intervention policy for Covid-19 epidemic via
neuroevolution algorithm [0.0]
Policies aimed at disrupting the viral transmission cycle and preventing the healthcare system from being overwhelmed exact an economic toll.
We developed a intervention policy model that comprised the relative human, economic and healthcare costs of non-pharmaceutical epidemic intervention.
A proposed model finds the minimum required reduction in contact rates to maintain the burden on the healthcare system below the maximum capacity.
arXiv Detail & Related papers (2021-10-23T16:26:50Z) - 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) - A Deep Q-learning/genetic Algorithms Based Novel Methodology For
Optimizing Covid-19 Pandemic Government Actions [63.669642197519934]
We use the SEIR epidemiological model to represent the evolution of the virus COVID-19 over time in the population.
The sequences of actions (confinement, self-isolation, two-meter distance or not taking restrictions) are evaluated according to a reward system.
We prove that our methodology is a valid tool to discover actions governments can take to reduce the negative effects of a pandemic in both senses.
arXiv Detail & Related papers (2020-05-15T17:17:45Z) - Digital Ariadne: Citizen Empowerment for Epidemic Control [55.41644538483948]
The COVID-19 crisis represents the most dangerous threat to public health since the H1N1 pandemic of 1918.
Technology-assisted location and contact tracing, if broadly adopted, may help limit the spread of infectious diseases.
We present a tool, called 'diAry' or 'digital Ariadne', based on voluntary location and Bluetooth tracking on personal devices.
arXiv Detail & Related papers (2020-04-16T15:53:42Z)
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.