Scenario simulation to predict the behavior of COVID-19 in Perú
Keywords:COVID-19, scenario prediction, Delta variant, physical-mathematical modeling
COVID-19 has been a multi-dimensional challenge for humanity, even more so for decision-makers responsible for acting in an accurate and timely manner to confront it. In Peru, with a current favorable trend of the Pandemic, the spread of the Delta variant is imminent, hence they require predictive information that makes it possible to make early decisions to mitigate its effects. Consequently, the objective was established to simulate scenarios applying physical-mathematical modeling, which predict the behavior of COVID-19 in Peru and facilitate decision-making. As methods and techniques were applied: analysis-synthesis, documentary review and physical-mathematical modeling using tools and functions of the MATLAB software. The following are recognized as results: determination of the behavior of the main variables of COVID-19 in Peru; physical-mathematical model based on the classic SIR with new compartments related to vaccination and those exposed, as well as its adjustment to the data from Peru; simulation of scenarios, including the Delta variant, for deceased, accumulated infected, unvaccinated infected and vaccinated infected. It was concluded that: The model conceived for the simulation of COVID-19 evolution scenarios, demonstrated its ability to predict the behavior of the most important variables that determine said evolution in Peru; another wave of infections must occur and cumulative figures between 2.9 and 3.36 million infected and between 215 and 255 thousand deaths must be reached; The main mitigation strategies should be aimed at guaranteeing social distancing and isolation, as well as increasing the vaccination regimen.
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Copyright (c) 2021 Héctor Eduardo Sánchez Vargas, Luis Alberto Taramona Ruiz, Amyrsa Salgado Rodríguez, Maribel Huatuco Lozano, Fernando Castillo Picón
This work is licensed under a Creative Commons Attribution 4.0 International License.