A Pandemic Forecasting Framework: An Application of Risk Analysis

Dizioli, Allan; Radzikowski, Aneta; Greenwood, Daniel J.H.

doi:10.5089/9781513594842.001

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…Since the beginning of the pandemic, four variants have been classified as VOC by the World Health Organization [16] When the prevalence of one (or more) of these variants become relevant, the epidemiological models used to track the pandemic evolution should take them into account. Several modeling contributions to describe the effect of variants have been proposed in the literature in the past few months [12,17,18,19].…”

Section: Introductionmentioning

confidence: 99%

Modelling the COVID-19 epidemic and the vaccination campaign in Italy by the SUIHTER model

Parolini¹,

Dedè²,

Ardenghi³

et al. 2021

Preprint

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Several epidemiological models have been proposed to study the evolution of COVID-19 pandemic. In this paper, we propose an extension of the SUIHTER model, first introduced in [Parolini et al, Proc R. Soc. A., 2021] to analyse the COVID-19 spreading in Italy, which accounts for the vaccination campaign and the presence of new variants when they become dominant. In particular, the specific features of the variants (e.g. their increased transmission rate) and vaccines (e.g. their efficacy to prevent transmission, hospitalization and death) are modeled, based on clinical evidence. The new model is validated comparing its near-future forecast capabilities with other epidemiological models and exploring different scenario analyses.

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Section: Introductionmentioning

confidence: 99%

Modelling the COVID-19 epidemic and the vaccination campaign in Italy by the SUIHTER model

Parolini¹,

Dedè²,

Ardenghi³

et al. 2021

Preprint

View full text Add to dashboard Cite

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Applications of AI and IoT in COVID-19 Vaccine and Its Impact on Social Life

Almars

Gad

Atlam

2021

Studies in Computational Intelligence

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COVID-19 illness has been recognized as an International Public Health Emergency (PHEIC). According to the World Health Organization (WHO), more than 200 countries worldwide have been impacted by the COVID-19. More than 4.7 M people in the globe have lost their lives due to the coronavirus pandemic. Governments and hospitals have taken several actions and measures to prevent the further spread of infectious diseases, protect all people, and minimize illness and death rates. Artificial Intelligence (AI) and the Internet of Things (IoT) have great potential to create prevailing tools for battling COVID-19. Several technologies such as deep learning, machine learning, natural language processing, and the Internet of Things have been used to properly address healthcare concerns, including diagnosis, drug and vaccine development, vaccine distribution, sentiment analysis, and fake news identification regarding COVID-19 reviews. The primary goal of this chapter is to focus on the potential of Artificial Intelligence (AI) and the Internet of Things (IoT). This chapter aims to provide a comprehensive overview of different approaches of AI and IoT related to combating COVID-19. In addition, the chapter discusses several actions and measurements that governments have used to prevent the spread of COVID-19. This chapter covers all methodologies applied to forecast the acceptance and the demand of the COVID-19 vaccine.

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