Activity based Traffic Indicator System for Monitoring the COVID-19 Pandemic

• Main Authors: Junsay, Justin, Lebumfacil, Aaron Joaquin, Tarun, Ivan George, Yu, William Emmanuel S
• Format: text
• Published: Archīum Ateneo 2021
• Subjects: Big Data; Data Science; Decision Support System; Pandemic Management; Computer Sciences; Databases and Information Systems
• Online Access: https://archium.ateneo.edu/discs-faculty-pubs/211; https://archium.ateneo.edu/cgi/viewcontent.cgi?article=1210&context=discs-faculty-pubs
• Institution: Ateneo De Manila University

This study describes an activity based traffic indicator system to provide information for COVID-19 pandemic management. The activity based traffic indicator system does this by utilizing a social probability model based on the birthday paradox to determine the exposure risk, the probability of meeting someone infected (PoMSI). COVID-19 data, particularly the 7-day moving average of the daily growth rate of cases (7-DMA of DGR) and cumulative confirmed cases of next week covering a period from April to September 2020, were then used to test PoMSI using Pearson correlation to verify whether it can be used as a factor for the indicator. While there is no correlation for the 7-DMA of DGR, PoMSI is strongly correlated (0.671 to 0.996) with the cumulative confirmed cases and it can be said that as the cases continuously rise, the probability of meeting someone COVID positive will also be higher. This shows that indicator not only shows the current exposure risk of certain activities but it also has a predictive nature since it correlates to cumulative confirmed cases of next week and can be used to anticipate the values of confirmed cumulative cases. This information can then be used for pandemic management.