A data-based analysis, modelling and forecasting approach was conducted for the COVID-19 outbreak in Hubei, China, using publicly available epidemiological data from January 11 to February 10, 2020. The study estimated key epidemiological parameters, including the basic reproduction number $ R_0 $, case fatality ratio, and case recovery ratio, using a Susceptible-Infectious-Recovered-Dead (SIDR) model. The results showed that $ R_0 $ was estimated to be approximately 2.6 based on confirmed cases and 2 under a scenario where the number of infected and recovered cases was scaled up by 20 and 40 times, respectively. The case fatality ratio was estimated to be around 0.15% in the total population under the scaled scenario. The study also forecasted the outbreak evolution up to February 29, predicting a cumulative number of infected cases ranging from 45,000 to 760,000, with a median of 180,000. The number of deaths was projected to reach up to 34,000, with a median of 9,000. The analysis highlighted the importance of considering the potential underreporting of asymptomatic and mild cases, which could significantly affect the accuracy of the forecasts. The study also noted that the case fatality ratio decreased from January 26, likely due to effective control measures in Hubei. The results suggest that the outbreak may slow down by the end of February, particularly under the scaled scenario. The study underscores the need for accurate data and the challenges in predicting the spread of the virus due to the uncertainty in official data and the complexity of the disease dynamics.A data-based analysis, modelling and forecasting approach was conducted for the COVID-19 outbreak in Hubei, China, using publicly available epidemiological data from January 11 to February 10, 2020. The study estimated key epidemiological parameters, including the basic reproduction number $ R_0 $, case fatality ratio, and case recovery ratio, using a Susceptible-Infectious-Recovered-Dead (SIDR) model. The results showed that $ R_0 $ was estimated to be approximately 2.6 based on confirmed cases and 2 under a scenario where the number of infected and recovered cases was scaled up by 20 and 40 times, respectively. The case fatality ratio was estimated to be around 0.15% in the total population under the scaled scenario. The study also forecasted the outbreak evolution up to February 29, predicting a cumulative number of infected cases ranging from 45,000 to 760,000, with a median of 180,000. The number of deaths was projected to reach up to 34,000, with a median of 9,000. The analysis highlighted the importance of considering the potential underreporting of asymptomatic and mild cases, which could significantly affect the accuracy of the forecasts. The study also noted that the case fatality ratio decreased from January 26, likely due to effective control measures in Hubei. The results suggest that the outbreak may slow down by the end of February, particularly under the scaled scenario. The study underscores the need for accurate data and the challenges in predicting the spread of the virus due to the uncertainty in official data and the complexity of the disease dynamics.