FASSSTrace: Embedding Micro and Macro Social Network Analysis in Modeling Contact Tracing during the Early Stages of the Pandemic
Contact-tracing is part and parcel of interventions in reducing the rate of transmission of the disease. After identifying the rate of transmission, the next important step is to determine the spread of the disease using network analysis. Using manual or digital methods, active tracing requires testing of suspect cases to identify and isolate positive cases from identified close contacts. Passive tracing allows citizens to report symptoms to designated local health authorities. Most contact tracing efforts implemented by the local government units to stem the transmission of COVID-19 follow standard manual contract tracing procedures. However, with the rapid increase in the speed of the outbreak, traditional contact tracing approaches are not sufficient to contain the spread of the disease. FASSSTrace is designed to make contact tracing more efficient by developing a contact tracing model for digital platforms. Specifically, the platform provides a social network model of confirmed (C) and suspect (S) cases and visualizes the transmission dynamics with the inclusion of suspect cases. The method allows for the construction of a model reflecting the contact network of the confirmed cases as recorded in the official disease surveillance tool which produces a contact network determining superspreaders from key individuals and locations. The contacts network uses a two-mode network incorporating geographical locations as nodes to bridge the unlinked confirmed cases. Analysis involves micro-level network measures to determine key individuals and locations and macro-level network measures to study the patterns in transmission dynamics of the disease.
Pangan, Z. S., & Estuar, M. R. J. E. (2021). FASSSTrace: Embedding micro and macro social network analysis in modeling contact tracing during the early stages of the pandemic. 2021 5th International Conference on Medical and Health Informatics, 44–49. https://doi.org/https://doi.org/10.1145/3472813.3472822