Data Driven Models for Contact Tracing Prediction: A Systematic Review of COVID-19

Saravanan Muthaiyah, Thein Oak Kyaw Zaw, Kalaiarasi Sonai Muthu Anbananthen, Byeonghwa Park, Myung Joon Kim


The primary objective of this research is to identify commonly used data-driven decision-making techniques for contact tracing with regards to Covid-19. The virus spread quickly at an alarming level that caused the global health community to rely on multiple methods for tracking the transmission and spread of the disease through systematic contact tracing. Predictive analytics and data-driven decision-making were critical in determining its prevalence and incidence. Articles were accessed from primarily four sources, i.e., Web of Science, Scopus, Emerald, and the Institute of Electrical and Electronics Engineers (IEEE). Retrieved articles were then analyzed in a stepwise manner by applying Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISM) that guided the authors on eligibility for inclusion. PRISM results were then evaluated and summarized for a total of 845 articles, but only 38 of them were selected as eligible. Logistic regression and SIR models ranked first (11.36%) for supervised learning. 90% of the articles indicated supervised learning methods that were useful for prediction. The most common specialty in healthcare specialties was infectious illness (36%). This was followed closely by epidemiology (35%). Tools such as Python and SPSS (Statistical Package for Social Sciences) were also popular, resulting in 25% and 16.67%, respectively.


Doi: 10.28991/ESJ-2023-SPER-02

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Coronavirus; Data Driven Decision Making; Prediction; Pandemic; Covid-19; SARS-CoV-2.


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DOI: 10.28991/ESJ-2023-SPER-02


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