Deciphering dynamics of recent COVID-19 outbreak in India: An age-structured modeling

Infectious disease transmission dynamics are particularly sensitive to social contact patterns, and the precautions people take to limit disease transmission. It depends on the age distribution of the community. Thus, knowing the age$-$specific prevalence and incidence of infectious diseases is critical for predicting future disease burden and the efficacy of interventions like immunization. This study uses an SEIR age$-$structured multi-group epidemic model to understand how social contact affects disease control. We created location$-$specific social contact matrices in the community to see how social mixing has affected illness spread. We estimated the basic reproduction number $(R_0)$ of the system and plotted its global behavior in terms of $R_0$. Optimal control for the problem has also been established quantitatively. The proposed model's transmission rate for India from September 1, 2020, to December 31, 2020, has also been estimated. We replicated the lifting of non-pharmaceutical therapies by allowing participants to return to work in phases and studied the impact of this. Our findings imply that identifying symptomatic sick people aged $20-49$ can help lower the number of infected people when schools are closed. When some schools are partially open, awareness of symptomatic infected persons helps reduce cases. The simulation results also suggest that limiting contact at school and other meeting areas could significantly lower the number of instances. Using the least square approach, it was discovered that the time$-$dependent transmission rate is more realistic than the constant spread rate for COVID$-19$ in India. To reduce the COVID$-19$ in burden, we found that gradually loosening control methods could flatten and lower other peaks. Our findings may help health policymakers decide on timely age$-$based immunization distribution strategies and hence control the disease.