Narrative review: predictive models on the evolution of COVID-19 pandemic

Abstract

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 (R0). 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.