To improve decision-making strategies and prediction based on epidemiological data, so far biased by highly-variable criteria, algorithms using unbiased morbidity parameters, i.e. Intensive Care Units (ICU) and Ordinary Hospitalizations (OH), are proposed. ICU/OH acceleration and velocities are mathematically modeled using available and official data to derive two thresholds, alerting on 30 % ICU and 40 % OH of COVID-19 daily occupancy settled by the Italian Minister of Health, as a case of study. A predictive model is also proposed to estimate the daily occupancy of ICU and OH in hospitals for each region, using a Susceptible-Infected-Recovered-Death (SIRD) epidemic model to further extend occupancy prediction in each regional district. Computed data validated the proposed models in Italy after almost two years of pandemic, obtaining agreements with the Italian Presidential Decree regardless of the different regional trends of epidemic waves. Therefore, the decision-making algorithm and prediction model resulted valuable tools, retrospectively, to be tested prospectively in sustainable strategies to curb the impact of COVID-19, or of any other pandemic threats with any aggregate of data, on local healthcare systems.