COVID-19, flattening the curve: recommendations towards control and managing a second wave

Fenemigho, Iziegbe; Ukponmwan, Ewaenosa; Nnakwue, Elizabeth C; Udoete, Iboro O; Asuzu, Chisom; Adaralegbe, Adeleye; Effiong, Utibe

doi:10.29392/001c.14151

Cited by 6 publications

(10 citation statements)

References 13 publications

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“…Recently, several efforts have been made to combine blockchain and AI technologies to build innovative solutions against the COVID-19 epidemic. For example, a research group in Spain leverages these two technologies to develop a blockchain and AI-based app that is able to predict the evolution of the COVID-19 pandemic [151] . Blockchain has been used to ensure that people comply with the social distancing rules by creating digital identities and licenses that keep track of the daily activities of a person.…”

Section: Blockchain and Ai Use Cases For Coronavirus Fightingmentioning

confidence: 99%

Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey

et al. 2021

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The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems to timely handle public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. Blockchain can combat pandemics by enabling early detection of outbreaks, ensuring the ordering of medical data, and ensuring reliable medical supply chain during the outbreak tracing. Moreover, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Motivated by these, in this paper we present an extensive survey on the use of blockchain and AI for combating COVID-19 epidemics. First, we introduce a new conceptual architecture which integrates blockchain and AI for fighting COVID-19 . Then, we survey the latest research efforts on the use of blockchain and AI for fighting COVID-19 in various applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. A case study is also provided using federated AI for COVID-19 detection. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronaviruslike epidemics.

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Section: Blockchain and Ai Use Cases For Coronavirus Fightingmentioning

confidence: 99%

Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey

et al. 2021

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“…Although different measures suit different regional specifics during a pandemic, countries with multiple standards have seen the most robust outcome[ 16 , 17 ].…”

Section: What Are the Measurement Goals In The Covid Pandemicmentioning

confidence: 99%

Pandemic control - do's and don'ts from a control theory perspective

Томов¹,

Miteva²,

Sekulovski³

et al. 2022

WJM

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Managing a pandemic is a difficult task. Pandemics are part of the dynamics of nonlinear systems with multiple different interactive features that co-adapt to each other (such as humans, animals, and pathogens). The target of controlling such a nonlinear system is best achieved using the control system theory developed in engineering and applied in systems biology. But is this theory and its principles actually used in controlling the current coronavirus disease-19 pandemic? We review the evidence for applying principles in different aspects of pandemic control related to different goals such as disease eradication, disease containment, and short- or long-term economic loss minimization. Successful policies implement multiple measures in concordance with control theory to achieve a robust response. In contrast, unsuccessful policies have numerous failures in different measures or focus only on a single measure (only testing, vaccines, etc. ). Successful approaches rely on predictions instead of reactions to compensate for the costs of time delay, on knowledge-based analysis instead of trial-and-error, to control complex nonlinear systems, and on risk assessment instead of waiting for more evidence. Iran is an example of the effects of delayed response due to waiting for evidence to arrive instead of a proper risk analytical approach. New Zealand, Australia, and China are examples of appropriate application of basic control theoretic principles and focusing on long-term adaptive strategies, updating measures with the evolution of the pandemic.

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“…These developments reduced caution regarding COVID-19 and led to alleviation of coronavirus-related containments. Therefore, the "flattening the curve" strategy with a main emphasis on limiting population mobility and contact tracing was replaced by less stringent measures and focused on the protection of vulnerable populations by encouraging vaccine uptake 7 , 8 .…”

Section: Introductionmentioning

confidence: 99%

A retrospective cohort study of incidence and risk factors for severe SARS-CoV-2 breakthrough infection among fully vaccinated people

Meister

Kolde

Fischer

et al. 2023

Sci Rep

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SARS-CoV-2 vaccination is currently the mainstay in combating the COVID-19 pandemic. However, there are still people among vaccinated individuals suffering from severe forms of the disease. We conducted a retrospective cohort study based on data from nationwide e-health databases. The study included 184,132 individuals who were SARS-CoV-2 infection-naive and had received at least a primary series of COVID-19 vaccination. The incidence of BTI (breakthrough infection) was 8.03 (95% CI [confidence interval] 7.95⎼8.13/10,000 person-days), and for severe COVID-19 it was 0.093 (95% CI 0.084⎼ 0.104/10,000 person-days). The protective effect of vaccination against severe COVID-19 remained constant for up to six months, and the booster dose offered an additional pronounced benefit (hospitalization aHR 0.32, 95% CI 0.19⎼0.54). The risk of severe COVID-19 was higher among those ≥ 50 years of age (aHR [adjusted hazard ratio] 2.06, 95% CI 1.25⎼3.42) and increased constantly with every decade of life. Male sex (aHR 1.32, 95% CI 1.16⎼1.45), CCI (The Charlson Comorbidity Index) score ≥ 1 (aHR 2.09, 95% CI 1.54⎼2.83), and a range of comorbidities were associated with an increased risk of COVID-19 hospitalization. There are identifiable subgroups of COVID-19-vaccinated individuals at high risk of hospitalization due to SARS-CoV-2 infection. This information is crucial to driving vaccination programs and planning treatment strategies.

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COVID-19, flattening the curve: recommendations towards control and managing a second wave

Cited by 6 publications

References 13 publications

Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey

Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey

Pandemic control - do's and don'ts from a control theory perspective

A retrospective cohort study of incidence and risk factors for severe SARS-CoV-2 breakthrough infection among fully vaccinated people

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