Modelado computacional para evaluar estrategias de rastreo y prevención de contagios.

SOLDANO, GERMAN JOSE; FRAIRE, JUAN; QUIROGA, RODRIGO; FINOCHIETTO, JORGE

Congreso; Congreso anual de la Sociedad Argentina de Infectología 2020; 2020

Sociedad Argentina de Infectología

A plethora of measures are being combined in the attempt to reduce SARS-CoV-2 virus spread which arguably remains uncontrolled, especially in the northern hemisphere, as of November 2020.Among these, contact tracing (CT) is already enabling authorities to react and isolate individuals at risk of being infected (thereby preventing further contagion), once a close contact with a diagnosed COVID-19 case is manually or digitally identified via phonecalls, house visits or smartphone apps.After a careful analysis of the infection detection rate and time delay dependency of CT, we introduce the concept of community-based contact prevention (CP).While tracing contacts anonymously by relying on available users´ digital assets, CP derives localized warnings that could modulate social behaviour and thus prevent contagion.We model both CT and CP dynamics in SERIA, a highly detailed agent-based simulation platform embracing realistic population-dependant statistical distributions.Our results provide evidence that, for varying adoption rates of the supporting digital technology, CP is an appealing alternative and complement to CT, particularly in scenarios where infection detection rate is low (high percentage of asymptomatic infections and/or diagnostic difficulties).This work calls attention to some limitations of CT and also suggests CP as a new, potent, and plausible tool for reducing the propagation of respiratory viruses while relying on available technology, respecting data privacy, and most importantly, promoting community-based awareness and social responsibility. The combination of both strategies is a promising strategy to minimize the mortality of the COVID-19 pandemic.