An agent-based transmission model of COVID-19 for re-opening policy design. (September 2022)

• Record Type: Journal Article
• Authors: Rodríguez, Alma; Cuevas, Erik; Zaldivar, Daniel; Morales-Castañeda, Bernardo; Sarkar, Ram; Houssein, Essam H.
• Is Part Of: Computers in biology and medicine. Volume 148(2022)
• Publication Date: 2022-09
• Subjects: COVID-19 -- SARS-CoV-2 -- Transmission model -- Agent-based model -- Contagion risk; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.105847
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: The global pandemic caused by the coronavirus (COVID-19) disease has collapsed the worldwide economy. Elements such as non-obligatory vaccination, new strain variants and lack of discipline to follow social distancing measures suggest the possibility that COVID-19 may continue to exist, exhibiting the behavior of a seasonal disease. As the socio-economic crisis has become unsustainable, all countries are planning strategies to gradually restart their economic and social activities. Initially, several containment measures have been adopted involving social distancing, infection detection tests, and ventilation systems. Despite the implementation of such policies, there exists a lack of evaluation of their performance to reduce the contagion index. This means there are no appropriate indicators to decide which intervention or set of interventions present the most effective result. Under these conditions, the development of models that provide useful information in the design and evaluation of containment measures and re-opening policies is of prime concern. In this paper, a novel approach to model the transmission process of COVID-19 in closed environments is proposed. The proposed model can simulate the effects that result from the complex interaction among individuals when they follow a particular containment measure or re-opening policy. With the proposed model, different hypothetical re-opening policies, that are otherwise impossible to analyze in real conditions, can be tested. Computer experiments demonstrate that the proposed model provides suitable information and realistic predictions, which are appropriate for designing strategies that allow a safe return to economic activities. Highlights: An approach to model the transmission process of COVID-19 in closed environments is proposed. With the proposed model, different hypothetical re-opening policies can be tested. Different from all other models, our approach considers asymptomatic or inoculated members. Under Its realistic assumptions, the model allows obtaining accurate results.