A Drive-through Simulation Tool for Mass Vaccination during COVID-19 Pandemic

Asgary, Ali; Najafabadi, Mahdi M.; Karsseboom, Richard; Wu, Jianhong

doi:10.3390/healthcare8040469

Cited by 60 publications

(60 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The artificial intelligence (AI) discussed here was developed based on a big dataset derived from an existing drive-through mass vaccination simulation created by the authors [ 32 ]. In this section, we briefly explain the drive-through simulation tool and then provide some details about our machine learning model.…”

Section: Methodsmentioning

confidence: 99%

Artificial Intelligence Model of Drive-Through Vaccination Simulation

Asgary

Valtchev

Chen

et al. 2020

IJERPH

Self Cite

View full text Add to dashboard Cite

Planning for mass vaccination against SARS-Cov-2 is ongoing in many countries considering that vaccine will be available for the general public in the near future. Rapid mass vaccination while a pandemic is ongoing requires the use of traditional and new temporary vaccination clinics. Use of drive-through has been suggested as one of the possible effective temporary mass vaccinations among other methods. In this study, we present a machine learning model that has been developed based on a big dataset derived from 125K runs of a drive-through mass vaccination simulation tool. The results show that the model is able to reasonably well predict the key outputs of the simulation tool. Therefore, the model has been turned to an online application that can help mass vaccination planners to assess the outcomes of different types of drive-through mass vaccination facilities much faster.

show abstract

Section: Methodsmentioning

confidence: 99%

Artificial Intelligence Model of Drive-Through Vaccination Simulation

Asgary

Valtchev

Chen

et al. 2020

IJERPH

Self Cite

View full text Add to dashboard Cite

show abstract

“…Both the beneficiary and the doctor must acknowledge the administration on the chain (lines 11-22) by signing the associated blockchain transaction. When both signatures are registered, the vaccine lot size is decremented and the association between the beneficiary Hash (PI) and the vaccine lot is registered (lines [19][20].…”

Section: Vaccine Administration and Side Effects Reportingmentioning

confidence: 99%

Blockchain Platform For COVID-19 Vaccine Supply Management

Antal

Cioara

Antal

et al. 2021

IEEE Open J. Comput. Soc.

105

View full text Add to dashboard Cite

In the context of COVID-19 pandemic, the rapid roll-out of a vaccine and the implementation of a worldwide immunization campaign is critical, but its success will depend on the availability of an operational and transparent distribution chain that can be audited by all relevant stakeholders. In this paper, we discuss how blockchain technology can help in several aspects of COVID-19 vaccination scheme. We present a system in which blockchain technology is used to guaranty data integrity and immutability of beneficiary registration for vaccination, avoiding identity thefts and impersonations. Smart contracts are defined to monitor and track the proper vaccine distribution conditions against the safe handling rules defined by vaccine producers enabling the awareness of all network peers. For vaccine administration, a transparent and tamper-proof solution for side effects self-reporting is provided considering beneficiary and administrated vaccine association. A prototype was implemented using the Ethereum test network, Ropsten, considering the COVID-19 vaccine distribution conditions. The results obtained for each onchain operation can be checked and validated on the Etherscan. In terms of throughput and scalability, the proposed blockchain system shows promising results while the estimated cost in terms of gas for vaccination scenario based on real data remains within reasonable limits.

show abstract

“…Various levels of pre-existing immunity were also assumed, ranging from 5% to 20%, depending on the region. In the article [36], the authors use an agent-based model to study the optimal arrangement of drive-through vaccination stations. In the article [37], a dynamic contact network was constructed in order to study the optimal choice of 5/50 vaccination strategy under a partial or complete lockdown or without any non-pharmaceutical interventions active at all.…”

Section: State Of the Artmentioning

confidence: 99%

Estimating the impact of interventions against COVID-19: from lockdown to vaccination

Thompson

Wattam²

2021

Preprint

View full text Add to dashboard Cite

Coronavirus disease 2019 (COVID-19) is an infectious disease of humans caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since the first case was identified in China in December 2019 the disease has spread worldwide, leading to an ongoing pandemic. In this article, we present a detailed agent-based model of COVID-19 in Luxembourg, and use it to estimate the impact, on cases and deaths, of interventions including testing, contact tracing, lockdown, curfew and vaccination. Our model is based on collation, with agents performing activities and moving between locations accordingly. The model is highly heterogeneous, featuring spatial clustering, over 2000 behavioural types and a 10 minute time resolution. The model is validated against COVID-19 clinical monitoring data collected in Luxembourg in 2020. Our model predicts far fewer cases and deaths than the equivalent equation-based SEIR model. In particular, with $R_0 = 2.45$, the SEIR model infects 87% of the resident population while our agent-based model results, on average, in only around 23% of the resident population infected. Our simulations suggest that testing and contract tracing reduce cases substantially, but are much less effective at reducing deaths. Lockdowns appear very effective although costly, while the impact of an 11pm-6am curfew is relatively small. When vaccinating against a future outbreak, our results suggest that herd immunity can be achieved at relatively low levels, with substantial levels of protection achieved with only 30% of the population immune. When vaccinating in midst of an outbreak, the challenge is more difficult. In this context, we investigate the impact of vaccine efficacy, capacity, hesitancy and strategy. We conclude that, short of a permanent lockdown, vaccination is by far the most effective way to suppress and ultimately control the spread of COVID-19.

show abstract

A Drive-through Simulation Tool for Mass Vaccination during COVID-19 Pandemic

Cited by 60 publications

References 33 publications

Artificial Intelligence Model of Drive-Through Vaccination Simulation

Artificial Intelligence Model of Drive-Through Vaccination Simulation

Blockchain Platform For COVID-19 Vaccine Supply Management

Estimating the impact of interventions against COVID-19: from lockdown to vaccination

Contact Info

Product

Resources

About