Dataset Information

Stochastic optimization for vaccine and testing kit allocation for the COVID-19 pandemic.

ABSTRACT: We present a formal mathematical modeling framework for a multi-agent sequential decision problem during an epidemic. The problem is formulated as a collaboration between a vaccination agent and learning agent to allocate stockpiles of vaccines and tests to a set of zones under various types of uncertainty. The model is able to capture passive information processes and maintain beliefs over the uncertain state of the world. We designed a parameterized direct lookahead approximation which is robust and scalable under different scenarios, resource scarcity, and beliefs about the environment. We design a test allocation policy designed to capture the value of information and demonstrate that it outperforms other learning policies when there is an extreme shortage of resources (information is scarce). We simulate the model with two scenarios including a resource allocation problem to each state in the United States and another for the nursing homes in Nevada. The US example demonstrates the scalability of the model and the nursing home example demonstrates the robustness under extreme resource shortages.

SUBMITTER: Thul L

PROVIDER: S-EPMC8580866 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Stochastic optimization for vaccine and testing kit allocation for the COVID-19 pandemic.

Thul Lawrence L Powell Warren W

European journal of operational research 20211111 1

We present a formal mathematical modeling framework for a multi-agent sequential decision problem during an epidemic. The problem is formulated as a collaboration between a vaccination agent and learning agent to allocate stockpiles of vaccines and tests to a set of zones under various types of uncertainty. The model is able to capture passive information processes and maintain beliefs over the uncertain state of the world. We designed a parameterized direct lookahead approximation which is robu ...[more]

PMID: 34785854

Similar Datasets

Project description:BackgroundThis study presents a framework for determining the allocation and distribution of the limited amount of vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).MethodsAfter analyzing the pandemic strategies of the major organizations and countries and with a literature review conducted by a core panel, a modified Delphi survey was administered to 13 experts in the fields of vaccination, infectious disease, and public health in the Republic of Korea. The following topics were discussed: 1) identifying the objectives of the vaccination strategy, 2) identifying allocation criteria, and 3) establishing a step-by-step vaccination framework and prioritization strategy based on the allocation criteria. Two rounds of surveys were conducted for each topic, with a structured questionnaire provided via e-mail in the first round. After analyzing the responses, a meeting with the experts was held to obtain consensus on how to prioritize the population groups.ResultsThe first objective of the vaccination strategy was maintenance of the integrity of the healthcare system and critical infrastructure, followed by reduction of morbidity and mortality and reduction of community transmission. In the initial phase, older adult residents in care homes, high-risk health and social care workers, and personal support workers who work in direct contact with coronavirus disease 2019 (COVID-19) patients would be prioritized. Expansion of vaccine supply would allow immunization of older adults not included in phase 1, followed by healthcare workers not previously included and individuals with comorbidities. Further widespread vaccine supply would ensure availability to the extended adult age groups (50-64 years old), critical workers outside the health sector, residents who cannot socially distance, and, eventually, the remaining populations.ConclusionThis survey provides the much needed insight into the decision-making process for vaccine allocation at the national level. However, flexibility in adapting to strategies will be essential, as new information is constantly emerging.

Project description:BackgroundAnticipating an initial shortage of vaccines for COVID-19, the Centers for Disease Control (CDC) in the United States developed priority vaccine allocations for specific demographic groups in the population. This study evaluates the performance of the CDC vaccine allocation strategy with respect to multiple potentially competing vaccination goals (minimizing mortality, cases, infections, and years of life lost (YLL)), under the same framework as the CDC allocation: four priority vaccination groups and population demographics stratified by age, comorbidities, occupation and living condition (congested or non-congested).Methods and findingsWe developed a compartmental disease model that incorporates key elements of the current pandemic including age-varying susceptibility to infection, age-varying clinical fraction, an active case-count dependent social distancing level, and time-varying infectivity (accounting for the emergence of more infectious virus strains). The CDC allocation strategy is compared to all other possibly optimal allocations that stagger vaccine roll-out in up to four phases (17.5 million strategies). The CDC allocation strategy performed well in all vaccination goals but never optimally. Under the developed model, the CDC allocation deviated from the optimal allocations by small amounts, with 0.19% more deaths, 4.0% more cases, 4.07% more infections, and 0.97% higher YLL, than the respective optimal strategies. The CDC decision to not prioritize the vaccination of individuals under the age of 16 was optimal, as was the prioritization of health-care workers and other essential workers over non-essential workers. Finally, a higher prioritization of individuals with comorbidities in all age groups improved outcomes compared to the CDC allocation.ConclusionThe developed approach can be used to inform the design of future vaccine allocation strategies in the United States, or adapted for use by other countries seeking to optimize the effectiveness of their vaccine allocation strategies.

Project description:In the last two years, the coronavirus disease 19 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a scientific and social challenge worldwide. Vaccines have been the most effective intervention for reducing virus transmission and disease severity. However, virus genetic variants are still circulating among vaccinated individuals with different symptomatology disease cases. Understanding the protective or disease associated mechanisms in vaccinated individuals is relevant to advance in vaccine development and implementation. To address this objective, serum protein profiles were characterized by quantitative proteomics and data analysis algorithms in four cohorts of vaccinated individuals uninfected and SARS-CoV-2 infected with asymptomatic, nonsevere and severe disease symptomatology. The results showed that immunoglobulins were the most overrepresented proteins in infected cohorts when compared to PCR-negative individuals. The immunoglobulin profile varied between different infected cohorts and correlated with protective or disease associated capacity. Overrepresented immunoglobulins in PCR-positive individuals correlated with protective response against SARS-CoV-2, other viruses, and thrombosis in asymptomatic cases. In nonsevere cases, correlates of protection against SARS-CoV-2 and HBV together with risk of myasthenia gravis and allergy and autoantibodies were observed. Patients with severe symptoms presented risk for allergy, chronic idiopathic thrombocytopenic purpura, and autoantibodies. The analysis of underrepresented immunoglobulins in PCR-positive compared to PCR-negative individuals identified vaccine-induced protective epitopes in various coronavirus proteins including the Spike receptor-binding domain RBD. Non-immunoglobulin proteins were associated with COVID-19 symptoms and biological processes. These results evidence host-associated differences in response to vaccination and the possibility of improving vaccine efficacy against SARS-CoV-2.

Dataset Information

Stochastic optimization for vaccine and testing kit allocation for the COVID-19 pandemic.

Publications

Stochastic optimization for vaccine and testing kit allocation for the COVID-19 pandemic.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets