The objective of this work is to generate probabilistic predictions of the spread of COVID-19 starting from observed data. The probabilistic forecasts are generated using an ensemble method inspired by probabilistic weather prediction systems operational today. Each ensemble member is defined by a logistic model: more specifically, each forecast is generated using logistic curves, determined by stochastically perturbing parameters.
Two are the main conclusions of this work. The first conclusion regards the ensemble method: results show that this method could provide valuable information on the probability of future scenario. The second conclusion regards the logistic model used to generate each single forecast: results indicate that the logistic model is too simple to be able to simulate the complex dynamic of the spread of COVID-19.
These conclusions indicate that, to generate more accurate and reliable probabilistic forecasts of the spread of diseases such as COVID-19, ensemble methods could be used, but each member of the ensemble of forecasts should be generated using a realistic model.
Keywords: COVID-19, probabilistic prediction, ensemble methods, uncertainty estimation