Forecasting COVID-19 Epidemic in Spain and Italy Using A Generalized Richards Model with Quantified Uncertainty

The Richards model and its generalized version are deterministic models that often implemented to fit forecast the cumulative number of infective cases in an epidemic outbreak. In this paper we employ a predict COVID-19 Spain Italy, based on available epidemiological data. To quantify uncertainty parameter estimation, use parametric bootstrapping approach construct 95% confidence interval estimation for model. Here assume time series data follow Poisson distribution. It is found each becomes narrow with increasing All all, predicts daily new reasonably well during calibration periods. However, fails produce good forecasts when amount used estimations not sufficient. Based our estimates, it early stages epidemic, both followed almost exponentially growth. peak Italy respectively 2 April 2020 28 March 2020. final sizes forecasted be at 293220 237010, respectively.