Abstract
Background: Epidemiological sciences have been evolving at an exponential rate paralleled only by the comparable growth within the discipline of data science. Digital epidemiological studies are playing a vital role in medical science analytics for the past few decades. To date, there are no published attempts at deploying the use of real-time analytics in connection with the disciplines of Dentistry or Medicine. Aims and Objectives: We deployed a real-time statistical analysis in connection with topics in Dental Anatomy and Dental Pathology represented by the maxillary sinus, posterior maxillary teeth, related oral pathology. The purpose is to infer the digital epidemiology based on a continuous stream of raw data retrieved from Google Trends database. Materials and Methods: Statistical analysis was carried out via Microsoft Excel 2016 and SPSS version 24. Google Trends database was used to retrieve data for digital epidemiology. Real-time analysis and the statistical inference were based on encoding a programming script using Python high-level programming language. A systematic review of the literature was carried out via PubMed-NCBI, the Cochrane Library, and Elsevier databases. Results: The comprehensive review of the literature, based on specific keywords search, yielded 491813 published studies. These were distributed as 488884 (PubMed-NCBI), 1611 (the Cochrane Library), and 1318 (Elsevier). However, there was no single study attempting real-time analytics. Nevertheless, we succeeded in achieving an automated real-time stream of data accompanied by a statistical inference based on data extrapolated from Google Trends. Conclusion: Real-time analytics are of considerable impact when implemented in biological and life sciences as they will tremendously reduce the required resources for research. Predictive analytics, based on artificial neural networks and machine learning algorithms, can be the next step to be deployed in continuation of the real-time systems to prognosticate changes in the temporal trends and the digital epidemiology of phenomena of interest.
Highlights
IntroductionIt can be implemented based on data from online resources of the surface web including trends mas.ccsenet.org
Digital epidemiology is an emerging discipline of public health and epidemiological sciences, and it has been evolving rapidly over the past few decades (Adawi et al, 2016; Mittelstadt, Benzler, Engelmann, Prainsack, &Vayena, 2018)
Real-time analytics are of considerable impact when implemented in biological and life sciences as they will tremendously reduce the required resources for research
Summary
It can be implemented based on data from online resources of the surface web including trends mas.ccsenet.org. Google Trends database perfectly fits this purpose as it contains an automated and up-to-date collection of data based on queries of users of the web from all over the world, including millions if not billions of users (Adawi et al, 2016; Cervellin, Comelli, & Lippi, 2017). Epidemiologist never attempted to carry out real-time or predictive analytics within the context of digital epidemiology and in connection with the discipline of dentistry or medicine (Carneiro & Mylonakis, 2009; Choi & Varian, 2012; Nuti et al, 2014).
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