In this paper we expand classic SEIRmodel for COVID-19 to include demographics, morespecifically heterogeneous mix of age groups. In the mostbasic SEIR-based model, the disease transmission rate,recovering rate and death rate are considered to behomogeneous across the age groups. However, such abasic model fails to give us deeper insight to the diseasetransmission, for people from different age groups mayshow significantly different infection and recovery trendsover time. Simulating SEIR for different age groups isdifficult because first, different social interactions withinthe age group and between the age groups can both affecthow fast the virus is spread for an age group. Secondly,certain age groups are more likely to contract the virus orhave a harder time to recover from the infection because ofweaker body immune system. Lastly, government quaran-tine policies can often impact age groups differently, whichcould dramatically change transmission rate for a periodof time. With these concerns in mind, we are bringingin sets of parameters expanding on age groups instead ofusing one fixed rate for transition between stages. Our finalmodel is promising compared to the basic SEIR model

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