Mathematical modelling is a sophisticated approach to study the behaviours of a system under different conditions in an ideal case. For infectious disease simulation, modelling allows birth, migration, and death events, as well as imperfect immunity to be present in mathematical forms. In modern notation, these model can be represented as compartmental model.

Conventional epidemiological study can normally tell what has happened in the past and can guide the events in the future. Modelling is of more immediate value in prediction of future events even if it is not so certain about the past or the reliability because not every detailed data can be obtained. The Royal Society Committee on infectious diseases concluded that “Quantitative modeling is one of the essential tools both for developing strategies in preparation for an outbreak and for predicting and evaluating the effectiveness of control policies during an outbreak”. The impacts of different interventions can be predicted in the model in a timely manner, rather than depending on whenever the large set of data is available. For example, Fraser et al. (see https://www.pnas.org/content/101/16/6146) have used simple mathematical approaches, without using actual data to answer important policy questions to determine the likely success of public health measures in controlling outbreaks. Now Health policy makers face with a number of potential intervention policies regarding the risk of community spread.

Conventional epidemiological study can normally tell what has happened in the past and can guide the events in the future. Modelling is of more immediate value in prediction of future events even if it is not so certain about the past or the reliability because not every detailed data can be obtained. The Royal Society Committee on infectious diseases concluded that “Quantitative modeling is one of the essential tools both for developing strategies in preparation for an outbreak and for predicting and evaluating the effectiveness of control policies during an outbreak”. The impacts of different interventions can be predicted in the model in a timely manner, rather than depending on whenever the large set of data is available. For example, Fraser et al. (see https://www.pnas.org/content/101/16/6146) have used simple mathematical approaches, without using actual data to answer important policy questions to determine the likely success of public health measures in controlling outbreaks. Now Health policy makers face with a number of potential intervention policies regarding the risk of community spread.

The model as we used to simulate Covid-19 transmission is a classical Susceptible-Exposed-Infectious-Recovered (SEIR) epidemic model. The parameter values will be estimated using Markov chain Monte Carlo (MCMC) algorithms under Bayesian framework. This is a generally accepted approach to predict infectious disease dynamics and to answer many public health questions. This approach is same or similar to the approaches used by the leading institutes (MRC Centre for Global Infectious Disease Analysis at Imperial College London, London School of Hygiene and Tropical Medicine, and others), which does not require massive datasets to predict future events. Infectious disease modelling generally make best attempts to predict the future based on incorporating many biological or control mechanisms or certain assumptions when data there is no available data.

Modelling approach can be used to infer dynamics of a system in complex scenarios such as the mechanism of pathogens' evolution, vaccination impacts on disease transmission, cancer cells evolution and personalized treatment.