Intervention strategies with 2D cellular automata for testing SARS-CoV-2 and reopening the economy

Abstract

During the period of time between a new disease outbreaks and its vaccine is deployed, the health and the economic systems have to find a testing strategy for reopening activities. In particular, asymptomatic individuals, who transmit locally the COVID-19 indoors, have to be identified and isolated. We proposed a 2D cellular automaton based on the SI epidemic model for selecting the most desirable testing frequency and identifying the best fitting size of random trails on local urban environments to diagnose SARS-CoV-2 and isolate infected people. We used the complex systems approach to face the challenge of a large-scale test strategy based on urban interventions, starting with first responders and essential workers. We used the case of Mexico to exemplify a credible and intelligent intervention that reduces the virus transmission and detects economic and health costs. Findings suggest that controlling and stopping the virus transmission in a short period of time are possible if the frequency of testing is daily and the percentage of random samples to be tested is at least 90%. This combination of model parameters represents the least expensive intervention compared to others. Therefore, the key for a national testing-isolating strategy is local interventions.