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

Justin Junsay, Aaron Lebumfacil, Ivan Tarun, William Yu

Abstract

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.

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Paper Citation


in Harvard Style

Junsay J., Lebumfacil A., Tarun I. and Yu W. (2021). Activity based Traffic Indicator System for Monitoring the COVID-19 Pandemic. In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-509-8, pages 183-191. DOI: 10.5220/0010399201830191


in Bibtex Style

@conference{iceis21,
author={Justin Junsay and Aaron Lebumfacil and Ivan Tarun and William Yu},
title={Activity based Traffic Indicator System for Monitoring the COVID-19 Pandemic},
booktitle={Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2021},
pages={183-191},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010399201830191},
isbn={978-989-758-509-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Activity based Traffic Indicator System for Monitoring the COVID-19 Pandemic
SN - 978-989-758-509-8
AU - Junsay J.
AU - Lebumfacil A.
AU - Tarun I.
AU - Yu W.
PY - 2021
SP - 183
EP - 191
DO - 10.5220/0010399201830191