A Framework for Data-Driven Solutions with COVID-19 Illustrations

Mwitondi, Kassim; Said, Raed A.

doi:10.5334/dsj-2021-036

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…In order to identify and understand some of these disease patterns, researchers have demonstrated the association of COVID-19 cases with crowding and NPI implementation [ 8 , 9 , 33 , 34 , 35 ]. In this sense, COVID-19’s spread is regarded as a multifaceted sociopolitical problem, for which exploratory analysis and data comparison based on urban mobility and sociodemographic indicators can provide useful epidemiological information [ 36 , 37 , 38 , 39 , 40 ].…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Patterns in Araraquara, Brazil: A Multimodal Analysis

Aragão

Silva

Distante

et al. 2023

IJERPH

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The epidemiology of COVID-19 presented major shifts during the pandemic period. Factors such as the most common symptoms and severity of infection, the circulation of different variants, the preparedness of health services, and control efforts based on pharmaceutical and non-pharmaceutical interventions played important roles in the disease incidence. The constant evolution and changes require the continuous mapping and assessing of epidemiological features based on time-series forecasting. Nonetheless, it is necessary to identify the events, patterns, and actions that were potential factors that affected daily COVID-19 cases. In this work, we analyzed several databases, including information on social mobility, epidemiological reports, and mass population testing, to identify patterns of reported cases and events that may indicate changes in COVID-19 behavior in the city of Araraquara, Brazil. In our analysis, we used a mathematical approach with the fast Fourier transform (FFT) to map possible events and machine learning model approaches such as Seasonal Auto-regressive Integrated Moving Average (ARIMA) and neural networks (NNs) for data interpretation and temporal prospecting. Our results showed a root-mean-square error (RMSE) of about 5 (more precisely, a 4.55 error over 71 cases for 20 March 2021 and a 5.57 error over 106 cases for 3 June 2021). These results demonstrated that FFT is a useful tool for supporting the development of the best prevention and control measures for COVID-19.

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Section: Introductionmentioning

confidence: 99%

COVID-19 Patterns in Araraquara, Brazil: A Multimodal Analysis

Aragão

Silva

Distante

et al. 2023

IJERPH

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“…Epidemiology, the aspect of research focusing on disease modelling is date intensive. Research epidemiologists in different research groups played a key role in developing different data driven model for COVID-19 [1][2][3] and monkeypox [4,5]. The requirement of accessing highly accurate data useful for disease modelling is beneficial, but not without having challenges.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of Group for Epidemiology Architectural Approach

Mathebula

2023

Ann. Data. Sci.

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Epidemiology, the aspect of research focusing on disease modelling is date intensive. Research epidemiologists in different research groups played a key role in developing different data driven model for COVID-19 and monkeypox. The requirement of accessing highly accurate data useful for disease modelling is beneficial but not without having challenges. Currently, the task of data acquisition is executed by select individuals in different research groups. This approach experiences the drawbacks associated with getting permission to access the desired data and inflexibility to change data acquisition goals due to dynamic epidemiological research objectives. The presented research addresses these challenges and proposes the design and use of dynamic intelligent crawlers for acquiring epidemiological data related to a given goal. In addition, the research aims to quantify how the use of computing entities enhances the process of data acquisition in epidemiological related studies. This is done by formulating and investigating the metrics of the data acquisition efficiency and the data analytics efficiency. The use of human assisted crawlers in the global information networks is found to enhance data acquisition efficiency (DAqE) and data analytics efficiency (DAnE). The use of human assisted crawlers in a hybrid configuration outperforms the case where manual research group member efforts are expended enhancing the DAqE and DAnE by up to 35% and 99% on average, respectively.

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The Role of Data-Driven Solutions for SMEs in Responding to COVID-19

Almeida

Wasim

2022

Int. J. Innovation Technol. Management

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This study aims to explore the role of small and medium-sized enterprises (SMEs) in developing data-driven solutions to address the direct and indirect challenges posed by COVID-19. A sample of six case studies of SMEs from the UK and Portugal were selected to explore in-depth the experience of these companies in proposing innovative solutions in the pandemic context. The findings reveal that the pandemic caused amplifying effects on the digitalization of organizations and the emergence of data-driven solutions. However, the development of a data-driven approach involves not only technologies but also the digitalization of processes and highly skilled human resources. The pandemic was also a catalyst for the emergence of collaborative initiatives that have enabled the development of solutions involving diverse players from science, business, and civilian society. This study offers innovative contributions by focusing exclusively on companies developing data-driven solutions supported by technologies such as the internet of things (IoT), big data, and artificial intelligence.

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A Framework for Data-Driven Solutions with COVID-19 Illustrations

Cited by 3 publications

References 29 publications

COVID-19 Patterns in Araraquara, Brazil: A Multimodal Analysis

COVID-19 Patterns in Araraquara, Brazil: A Multimodal Analysis

Quantitative Analysis of Group for Epidemiology Architectural Approach

The Role of Data-Driven Solutions for SMEs in Responding to COVID-19

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