Resumen

HASDEU, Santiago; LAMFRE, Laura; CARO, Patricia  y  HORNE, Federico. Narrative review: predictive models on the evolution of COVID-19 pandemic. Rev. argent. salud pública [online]. 2020, vol.12, suppl.1, pp.3-3.  Epub 01-Oct-2020. ISSN 1852-8724.

Mathematical modeling has been used for over a hundred years to assess the impact of public health intervention strategies and suggest the optimal course of action in the fight against emerging infectious diseases. The appearance of the new SARS-CoV-2 virus poses a great challenge for health planners and decision-makers, who must allocate finite resources, reorganize care systems, and make decisions in a context of great uncertainty. Many health systems incorporate information provided by predictive models in their decision-making process to face the COVID-19 pandemic. This makes it necessary to review the evolution of the different types of existing models, their characteristics, limitations and link with decision-making in Argentina and other countries. In order to fulfill this objective, a bibliographic search was carried out on the published models about the evolution of the pandemic. The number of related projects submitted for scholarships from the Ministry of Science, Technology and Innovation was analyzed. Different types of published models were identified, classified and described, such as deterministic and stochastic, different compartmentalized models, threshold theory and main characteristics of the models were described as the basic reproductive number ( R 0). The importance of the assumptions of each model and the approach to uncertainty were analyzed. Its main limitations and its link with decision-making in provinces and regions were discussed.

Palabras clave : Statistical models; Theoretical models; Coronavirus infections.

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