Comparison of predictive methods of the COVID-19 pandemic evolution

Abstract

[en] The appearance of the COVID-19 pandemic has urged governments from around the world to resort to the scientific community in order to predict how the spread of the virus would evolve. In Catalonia, the need to compare the two most used models arose to decide on whether restrictions should be applied or not. The Cata- lan government has mainly used the Gompertz model to predict the evolution of the infected population but some scientists have shown concern for this model as compared to the more traditional deterministic compartmental models. This created the need to compare the effectiveness of these two models and determine which of them is more useful for predicting future pandemics. In this project, we will present and analyse both types of models. In the case of compartmental models we will summarise only the three most basic types, SIR,SEIR and SEIRS. We will determine which of these models is most effective in predicting the spread of the virus by retrospectively comparing the predicted values and the real ones obtained. We will use data from the database provided by the Catalan government, Dades Obertes, and train our models with data from 30 days in order to predict two weeks in advance. Our results have shown that SEIR is the best of the initially proposed methods but is clearly affected by noise in our data, especially after restrictions were applied. A clear improvement was shown by averaging the values from the Gompertz model together with the values of the SEIR model. We clearly obtained the best possible model from combining both of them as it is smoother than predicting with SEIR and yields better results than the Gompertz model. In future work, we plan to include more complex compartmental models, such as the ones including geographical and transportation factors, given the necessary data. In addition, a larger average of models can be used, in which more than 2 models is applied, for an even more precise prediction of the evolution of pandemics.