EVALUATING THE EFFECTIVENESS OF CONTACT TRACING USING NETWORK THEORY AND GRAPH ANALYTICS

What if epidemics could be controlled not just through human tracing teams, but via mathematical network models that predict who matters most in an outbreak? This study evaluates the effectiveness of contact tracing using network theory and graph analytics in Ghana from 2020 to 2024, focusing on how node metrics, edge attributes, and algorithmic models influence tracing outcomes such as contact identification speed, cluster containment, and exposure accuracy. Based on 105 region-month secondary observations, the study employed Pearson correlation and multiple regression analyses to examine relationships between tracing components and outcomes. Results showed that algorithmic models like Page Rank yielded up to 34% faster tracing, edge attributes improved exposure prediction by 38%, and central nodes accounted for 34% of secondary infections. The regression model, however, explained only 3.3% of outcome variance (R² = 0.033), with Digital and Social Constraints having the strongest positive correlation (r = 0.110). Despite limited statistical strength, practical impacts included a 58% containment success rate and over 85% exposure notification accuracy in regions using graph-enhanced tracing. The study concludes that network-informed tracing significantly boosts epidemic control when combined with high participation and digital trust. It recommends national adoption of real-time graph dashboards, digital inclusion initiatives, and adaptive algorithmic tracing for scalable outbreak response.