Discrete-Time Modeling of COVID-19 Propagation in Argentina with Explicit Delays.

We present a new deterministic discrete-time compartmental model of COVID-19 that explicitly takes into account relevant delays related to the stages of the disease, its diagnosis and report system, allowing to represent the presence of imported cases. In addition to developing the model equations, we describe an automatic parameter fitting mechanism using official data on the spread of the virus in Argentina. The result consistently reflects the behavior of the disease with respect to characteristic times: latency, infectious period, report of cases (confirmed and dead), and allows for detecting automatically changes in the reproductive number and in the mortality factor. We also analyse the model's prediction capability and present simulation results assuming different future scenarios. We discuss usage of the model in a closed-loop control scheme, where the explicit presence of delays plays a key role in projecting more realistic dynamics than that of classic continuous-time models.