VacSIM: Learning effective strategies for COVID-19 vaccine distribution using reinforcement learning

Awasthi, Raghav; Guliani, Keerat Kaur; Khan, Saif Ahmad; Vashishtha, Aniket; Gill, Mehrab Singh; Bhatt, Arshita; Nagori, Aditya; Gupta, Aniket; Kumaraguru, Ponnurangam; Sethi, Tavpritesh

doi:10.1016/j.ibmed.2022.100060

Cited by 13 publications

(8 citation statements)

References 14 publications

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“…AI and machine learning can be used to analyze large datasets, such as genomic data, to identify patterns and trends relevant to the understanding and treatment of infectious diseases [ 9 , 10 , 11 , 12 ]. For example, machine learning algorithms have been utilized to identify potential drug targets for SARS-CoV-2, which causes COVID-19 [ 13 , 14 ].…”

Section: An Opinionmentioning

confidence: 99%

The Epidemiology of Infectious Diseases Meets AI: A Match Made in Heaven

et al. 2023

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Section: An Opinionmentioning

confidence: 99%

The Epidemiology of Infectious Diseases Meets AI: A Match Made in Heaven

et al. 2023

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“…In addition to the aforementioned, the inequalities present in the immunization process have contributed to the harm to human health and postponed the pandemic end[ 91 ]. Research demonstrated that the cost of vaccines against the COVID-19 impeded the access and the immunization process of some countries which suffered from the economic impact of the pandemic, and the adaptation of their health systems to attend to the population with the disease[ 92 ].…”

Section: Restriction To Health Services Accessibilitymentioning

confidence: 99%

Syndemic aspects between COVID-19 pandemic and social inequalities

Apolonio¹,

Júnior²,

Cuzzuol³

et al. 2022

WJM

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Although the coronavirus disease 2019 (COVID-19) pandemic has reached all over the world population, it has demonstrated a heterogeneous impact on different populations. The most vulnerable communities which coexist daily with the social inequalities like low access to hygiene and personal protection products, crowded residences, and higher levels of chronic diseases have a higher risk of contact and the spread of infection, beyond unfavorable clinical outcomes. The elevation of the risk of infection exposure can be related to gender due to the presence of a larger contingent of women in essential services, as well as frontline and cleaning professionals who regardless of gender have the greatest exposure to the virus. Such exposures can contribute to the development of fear of contaminating themselves or their family members associated also with the work stress, both of which are related to the emergence of mental disturbances in these populations. Furthermore, conditions of unsanitary living and low socioeconomic status, populations at war, pre-existing social barriers, and ethnicity have contributed to more impact of the pandemic both in the exposure to the virus and access to health services, COVID-19 management, and management of other pathologies. At the same time, factors such as the closing of non-essential services, the loss of jobs, and the increase in household spending aggravated the social vulnerabilities and impacted the family economy. Lastly, the COVID-19 pandemic contributed still more to the impact on women's health since it propitiated a favorable environment for increasing domestic violence rates, through the segregation of women from social life, and increasing the time of the victims with their aggressors.

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“…Although not yet implemented, Awasthi et al (2020) has built a pipeline called VacSIM for optimizing COVID-19 vaccine distribution using Deep Reinforcement Learning models and contextual bandit machine learning framework. Although the application is primarily for the United States and the PVI does not taken to account vaccinated population, it demonstrates that a predictive model based on machine learning and statistical models is capable of tracking vaccine booster doses and recommending an ideal vaccination scheme.…”

Section: Proposalmentioning

confidence: 99%

A feasibility study proposal of the predictive model to enable the prediction of population susceptibility to COVID-19 by analysis of vaccine utilization for advising deployment of a booster dose

Jittprasong¹

2022

Preprint

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With the present highly infectious dominant SARS-CoV-2 strain of B1.1.529 or Omicron spreading around the globe, there is concern that the COVID-19 pandemic will not end soon and that it will be a race against time until a more contagious and virulent variant emerges. One of the most promising approaches for preventing virus propagation is to maintain continuous high vaccination efficacy among the population, thereby strengthening the population protective effect and preventing the majority of infection in the vaccinated population, as is known to occur with the Omicron variant frequently. Countries must structure vaccination programs in accordance with their populations' susceptibility to infection, optimizing vaccination efforts by delivering vaccines progressively enough to protect the majority of the population. We present a feasibility study proposal for maintaining optimal continuous vaccination by assessing the susceptible population, the decline of vaccine efficacy in the population, and advising booster dosage deployment to maintain the population's protective efficacy through the use of a predictive model. Numerous studies have been conducted in the direction of analyzing vaccine utilization; however, very little study has been conducted to substantiate the optimal deployment of booster dosage vaccination with the help of a predictive model based on machine learning algorithms.

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VacSIM: Learning effective strategies for COVID-19 vaccine distribution using reinforcement learning

Cited by 13 publications

References 14 publications

The Epidemiology of Infectious Diseases Meets AI: A Match Made in Heaven

The Epidemiology of Infectious Diseases Meets AI: A Match Made in Heaven

Syndemic aspects between COVID-19 pandemic and social inequalities

A feasibility study proposal of the predictive model to enable the prediction of population susceptibility to COVID-19 by analysis of vaccine utilization for advising deployment of a booster dose

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