Novel Data-Based Model for Future Epidemiology

Mathebula, Dephney

doi:10.1109/icabcd54961.2022.9856356

Cited by 1 publication

(5 citation statements)

References 17 publications

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“…This is done for two cases i.e., Case 1 and Case 2. The scenario presented in Case is that which is presented in [22]. The scenario in Case 2 arises due to the proposed approach and incorporates a reduction of crawler persistence.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Existing work in [22] recognizes the capability of using web crawlers to improve the efficiency of acquisition and use of healthcare related data. The use of web crawlers enables health domain researchers to make use of digital tools in acquiring data to be used for future analysis.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The model presented in [22] examines the context of acquiring data to provide solutions to health care challenges without suitable closed form expressions. The acquisition of data to address this kind of challenge is the goal of the crawler architecture presented in [22]. The acquisition of data to meet this goal can be done using different approaches i.e., with crawlers only, humans only, or a combination of humans and crawlers.…”

Section: Introductionmentioning

confidence: 99%

“…These metrics should be examined in the context of their relations with the number of research groups (NRGs). The focus on the use of crawlers in the domain of health applications [12][13][14][15][16][17]22] has been their use in acquiring data. However, it is important to examine their performance benefits in different contexts.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%