@article{arrar_mathematical_2021,

title = {Mathematical and Computational Initiatives from the University of Buenos Aires to Contribute to Decision-Making in the Context of COVID-19 in Argentina},

author = {Mehrnoosh Arrar and Laouen Mayal Louan Belloli and Ana Maria Bianco and Leonardo Boechi and Rodrigo Castro and Guillermo Alfredo Duran and Roberto Argentino Etchenique and Natalia Brenda Fernández and Luciana Ferrer and Diego David Garbervetsky and Rodrigo Goldsmit and Carolina Grillo Vidal and Juan Kamienkowski and Pablo Laciana and Esteban Lanzarotti and Mario Lozano and Rodrigo Maidana and Mariano Mendiluce and Sol Minoldo and Ezequiel Pecker-Marcosig and Leonardo Pepino and Armando Ezequiel Puerta and Rodrigo Quiroga and Guillermo Solovey and Marina Valdora and Mariano Zapatero},

doi = {10.52712/sciencereviews.v2i2.38},

year  = {2021},

date = {2021-01-01},

note = {Publisher: Centro de Estudios sobre Ciencia, Desarrollo y Educación Superior},

keywords = {COVID 19, Decision making},

pubstate = {published},

tppubtype = {article}

}

@article{bergonzi_discrete-time_2021,

title = {Discrete-Time Modeling of COVID-19 Propagation in Argentina with Explicit Delays},

author = {Mariana Bergonzi and Ezequiel Pecker-Marcosig and Ernesto Kofman and Rodrigo Castro},

doi = {10.1109/MCSE.2020.3040700},

issn = {1521-9615},

year  = {2021},

date = {2021-01-01},

journal = {Computing in Science & Engineering},

volume = {23},

number = {01},

pages = {35–45},

abstract = {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.},

note = {Publisher: IEEE Computer Society},

keywords = {Atmospheric Modeling, Automatic Parameter, Automatically Changes, Characteristic Times, Closed Loop Systems, Continuous Time Models, Continuous Time Systems, COVID 19, Delays, Differential Equations, Discrete Time Compartmental Model, Discrete Time Modeling, Discrete Time Systems, Diseases, Explicit Delays, Explicit Presence, Imported Cases, Infectious Period, Physiological Models, Sociology, Stochastic Processes, Viruses Medical},

pubstate = {published},

tppubtype = {article}

}