Agent-based Modeling of COVID-19 Infection Rate vis-à-vis the Philippine Government Community Quarantine and Face Covering Measures

• Main Authors: Agustin, Alfonso D, Ferrer, Justine C, Bolingot, Harold Jay M, Celestre, John Donnie I, Oppus, Carlos M, Monje, Jose Claro N
• Format: text
• Published: Archīum Ateneo 2021
• Subjects: COVID-19; limiting; pandemics; contacts; government; predictive models; agent-based modeling; COVID-19 pandemic; face shields; face masks; government measures; NetLogo; Electrical and Computer Engineering; Public Health
• Online Access: https://archium.ateneo.edu/ecce-faculty-pubs/110; https://ieeexplore.ieee.org/document/9664664
• Institution: Ateneo De Manila University

The COVID-19 pandemic has uprooted the normal lives and routines of Filipinos. Dubbed as the ‘new normal,’ the wearing of face masks and face shields and adherence to social distancing and the community quarantines have been enforced by the Philippine government to all its citizens. The primary concern is to slow down the spread of SARS-CoV-2 virus absent of widespread and available pharmaceutical interventions such as vaccines and therapies, in the hopes of keeping the overall healthcare system functional. In this study, the researchers use NetLogo, an agent-based modeling software to emulate such government measures on a closed population of agents and observe the effects on the spread of COVID-19. Running different simulation scenarios, the researchers find that the NetLogo model is able to predict long- term trends imposed by these government restrictions. The results show that through increasing the number of people using face coverings and restricting interactions through enforcement of community quarantines, the rise of infections can be decreased substantially. The results of our model are consistent with the Philippine government’s collated pandemic data on the effect of the enforcement of restrictions to the ongoing outbreak during certain time periods in the year 2020.