Comparison Prediction Models Using Time Series in COVID-19 Infection in Mexico

The COVID-19 pandemic was the first health crisis that involved the entire world in this century. The statistical data revealed a lack of organization and control in sanitary measures, containment, and mitigation policies, as well as a lack of planning and coordination in the use of medical supplies. This situation led to the development of prediction models that provided predictive information about the pandemic evolution. In this paper, time series of accumulated cases of infection were generated through official data provided by the Ministry of Health of the Government of Mexico. Six deterministic and stochastic predictive models were applied to this information with the purpose of comparing its efficiency to predictive cases of infection of COVID-19. These models were applied to data from two cities in Mexico, Colima and the State of Mexico. The study concludes that the ARIMA and ANN MLP models adapt better to the data that are generated daily, therefore, they have an improved prediction capacity.