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With more than 1.7 million COVID-19 deaths, identifying effective measures toprevent COVID-19 is a top priority. We developed a mathematical model tosimulate the COVID-19 pandemic with digital contact tracing and testingstrategies. The model uses a real-world social network generated from ahigh-resolution contact data set of 180 students. This model incorporatesinfectivity variations, test sensitivities, incubation period, and asymptomaticcases. We present a method to extend the weighted temporal social network andpresent simulations on a network of 5000 students. The purpose of this work isto investigate optimal quarantine rules and testing strategies with digitalcontact tracing. The results show that the traditional strategy of quarantiningdirect contacts reduces infections by less than 20% without sufficient testing.Periodic testing every 2 weeks without contact tracing reduces infections byless than 3%. A variety of strategies are discussed including testing secondand third degree contacts and the pre-exposure notification system, which actsas a social radar warning users how far they are from COVID-19. The mosteffective strategy discussed in this work was combined the pre-exposurenotification system with testing second and third degree contacts. Thisstrategy reduces infections by 18.3% when 30% of the population uses the app,45.2% when 50% of the population uses the app, 72.1% when 70% of the populationuses the app, and 86.8% when 95% of the population uses the app. Whensimulating the model on an extended network of 5000 students, the results aresimilar with the contact tracing app reducing infections by up to 79%.

Modeling of Network Based Digital Contact Tracing and Testing Strategies for the COVID-19 Pandemic