Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing

Agbehadji, Israel Edem; Awuzie, Bankole; Ngowi, A.B.; Millham, Richard

doi:10.3390/ijerph17155330

Cited by 192 publications

(138 citation statements)

References 79 publications

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“…This might be attributed to the lack of a vast amount of historical data to train the AI models, which results in developing AI forecasting models that rely on noisy data and social media data. This severely affects the performance and accuracy of the forecasting model because of different data formats, lack of data standardization and interoperability, and missing values which is often inaccuracy and unreliable (Agbehadji, Bankole, Alfred, & Richard, 2020; Elliot, Fanwell, & Kinsley, 2018). The current literature, depicted in Table 2, shows that China is the leading pack in implementing AI technologies in fighting COVID‐19 pandemic.…”

Section: Discussionmentioning

confidence: 99%

A critical review of emerging technologies for tackling COVID ‐19 pandemic

Mbunge

Akinnuwesi

Fashoto

et al. 2020

Human Behav and Emerg Tech

154

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COVID‐19 pandemic affects people in various ways and continues to spread globally. Researches are ongoing to develop vaccines and traditional methods of Medicine and Biology have been applied in diagnosis and treatment. Though there are success stories of recovered cases as of November 10, 2020, there are no approved treatments and vaccines for COVID‐19. As the pandemic continues to spread, current measures rely on prevention, surveillance, and containment. In light of this, emerging technologies for tackling COVID‐19 become inevitable. Emerging technologies including geospatial technology, artificial intelligence (AI), big data, telemedicine, blockchain, 5G technology, smart applications, Internet of Medical Things (IoMT), robotics, and additive manufacturing are substantially important for COVID‐19 detecting, monitoring, diagnosing, screening, surveillance, mapping, tracking, and creating awareness. Therefore, this study aimed at providing a comprehensive review of these technologies for tackling COVID‐19 with emphasis on the features, challenges, and country of domiciliation. Our results show that performance of the emerging technologies is not yet stable due to nonavailability of enough COVID‐19 dataset, inconsistency in some of the dataset available, nonaggregation of the dataset due to contrasting data format, missing data, and noise. Moreover, the security and privacy of people's health information is not totally guaranteed. Thus, further research is required to strengthen the current technologies and there is a strong need for the emergence of a robust computationally intelligent model for early differential diagnosis of COVID‐19.

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Section: Discussionmentioning

confidence: 99%

A critical review of emerging technologies for tackling COVID ‐19 pandemic

Mbunge

Akinnuwesi

Fashoto

et al. 2020

Human Behav and Emerg Tech

154

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“…3 Overall, the current results of this living systematic review are in line with the literature about the validity of predictive models regarding the detection and prognosis of COVID-19. 4,5 While appreciating the scientific race of COVID-19 predictive papers in medicine, developing more rigorous models using the previously published studies should be of more concern.…”

Section: Summary Review/covid-19mentioning

confidence: 99%

Can medical practitioners rely on prediction models for COVID-19? A systematic review

Shamsoddin¹

2020

Evid Based Dent

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Aim This systematic review sought to assess and scrutinise the validity and practicality of published and preprint reports of prediction models for the diagnosis of coronavirus disease 2019 (COVID-19) in patients with suspected infection, for prognosis of patients with COVID-19, and for identifying individuals in the general population at increased risk of infection with COVID-19 or being hospitalised with the illness. Data sources A systematic, online search was conducted in PubMed and Embase. In order to do so, the authors used Ovid as the host platform for these two databases and also investigated bioRxiv, medRxiv and arXiv as repositories for the preprints of studies. A public living systematic review list of COVID-19-related studies was used as the baseline searching platform (Institute of Social and Preventive Medicine's repository for living evidence on COVID-19). Study selection Studies which developed or validated a multivariable prediction model related to COVID-19 patients' data (individual level data) were included. The authors did not put any restrictions on the models included in their study regarding the model setting, prediction horizon or outcomes. Data extraction and synthesis Checklists of critical appraisal and data extraction for systematic reviews of prediction modelling studies (CHARMS) and prediction model risk of bias assessment tool (PROBAST) were used to guide developing of a standardised data extraction form. Each model's predictive performance was extracted by using any summaries of discrimination and calibration. All these steps were done according to the aspects of the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) and preferred reporting items for systematic reviews and meta-analyses (PRISMA). Results One hundred and forty-five prediction models (107 studies) were selected for data extraction and critical appraisal. The most common predictors of diagnosis and prognosis of COVID-19 were age, body temperature, lymphocyte count and lung imaging characteristics. Influenza-like symptoms and neutrophil count were regularly predictive in diagnostic models, while comorbidities, sex, C-reactive protein and creatinine were common prognostic items. C-indices (a measure of discrimination for models) ranged from 0.73 to 0.81 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.68 to 0.99 in the prognostic models. All the included studies were reported to have high risks of bias. Conclusions Overall, this study did not recommend applying any of the predictive models in clinical practice yet. High risk of bias, reporting problems and (probably) optimistic reported performances are all among the reasons for the previous conclusion. Prompt actions regarding accurate data sharing and international collaborations are required to achieve more rigorous ...

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“…Unfortunately, the contact-tracing method in Spain is different from other countries, where policy-makers have considered using digital contact-tracing as a COVID-19 containment strategy to improve the traditional contact-tracing efficiency. Similarly, big data platforms have also been applied for tracing contacts as other studies show [ 1 , 2 ].…”

Section: Introductionmentioning

confidence: 99%

Facing a Second Wave from a Regional View: Spatial Patterns of COVID-19 as a Key Determinant for Public Health and Geoprevention Plans

Guerra

Salcines

Cantarero

2020

IJERPH

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Several studies on spatial patterns of COVID-19 show huge differences depending on the country or region under study, although there is some agreement that socioeconomic factors affect these phenomena. The aim of this paper is to increase the knowledge of the socio-spatial behavior of coronavirus and implementing a geospatial methodology and digital system called SITAR (Fast Action Territorial Information System, by its Spanish acronym). We analyze as a study case a region of Spain called Cantabria, geocoding a daily series of microdata coronavirus records provided by the health authorities (Government of Cantabria—Spain) with the permission of Medicines Ethics Committee from Cantabria (CEIm, June 2020). Geocoding allows us to provide a new point layer based on the microdata table that includes cases with a positive result in a COVID-19 test. Regarding general methodology, our research is based on Geographical Information Technologies using Environmental Systems Research Institute (ESRI) Technologies. This tool is a global reference for spatial COVID-19 research, probably due to the world-renowned COVID-19 dashboard implemented by the Johns Hopkins University team. In our analysis, we found that the spatial distribution of COVID-19 in urban locations presents a not random distribution with clustered patterns and density matters in the spread of the COVID-19 pandemic. As a result, large metropolitan areas or districts with a higher number of persons tightly linked together through economic, social, and commuting relationships are the most vulnerable to pandemic outbreaks, particularly in our case study. Furthermore, public health and geoprevention plans should avoid the idea of economic or territorial stigmatizations. We hold the idea that SITAR in particular and Geographic Information Technologies in general contribute to strategic spatial information and relevant results with a necessary multi-scalar perspective to control the pandemic.

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Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing

Cited by 192 publications

References 79 publications

A critical review of emerging technologies for tackling COVID ‐19 pandemic

A critical review of emerging technologies for tackling COVID ‐19 pandemic

Can medical practitioners rely on prediction models for COVID-19? A systematic review

Facing a Second Wave from a Regional View: Spatial Patterns of COVID-19 as a Key Determinant for Public Health and Geoprevention Plans

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