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

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. © 2023 by the authors. Licensee ESJ, Italy.

A Novel Approach on Covid-19 Contact Tracing – Utilization of Low Calibrated Transmission Power & Signal Captures in BLE

Covid-19 pandemic has compelled countries to conduct contact tracing vigorously in order to curb the highly infectious virus from further spread. In this context, Bluetooth Low Energy (BLE) has been broadly used, utilizing Received Signal Strength Indicator (RSSI) for Close Contact Identification (CCI). However, many of the available solutions are not able to adhere to the guidelines provided by Centers for Disease Control (CDC) and Prevention which are: (1) Distance requirement of within 6-feet (~2 meters) and (2) Minimum 15-minutes duration for CCI. In providing some closure to the gap, we proposed a novel approach of utilizing: (1) Low calibrated transmission power (Tx) and (2) Number of signal captures. Our proposed approach is to lowly calibrate Tx so that when distance is at 2 meters between users, number signal capture gets lower as the chipset’s smallest RSSI sensitivity value has been reached. In this paper, complete experimentation for Proof of Concept (POC) and Pilot test conducted are demonstrated. Results obtained shows that the accuracy for POC utilizing signal captures for 2±0.3 m distance is at: (1) 71.43% for 5 users and (2) 70.69% for 9 users. While so, accuracy for the Pilot test when considering CCI on individual case-basis is at 95% for 5 users. © 2022 by the authors. Licensee ESJ, Italy.