Age-Stratified COVID-19 Spread Analysis and Vaccination: A Multitype Random Network Approach

Xianhao Chen, University of Florida
Guangyu Zhu, University of Florida
Lan Zhang, Michigan Technological University
Yuguang Fang, University of Florida
Linke Guo, Clemson University
Xinguang Chen, University of Florida


The risk of severe illness and mortality from COVID-19 significantly increases with age. As a result, age-stratified modeling for COVID-19 dynamics is the key to study how to reduce hospitalizations and mortality from COVID-19. By taking advantage of network theory, we develop an age-stratified epidemic model for COVID-19 in complex contact networks. Specifically, we present an extension of standard SEIR (susceptible-exposed-infectious-removed) compartmental model, called age-stratified SEAHIR (susceptible-exposed-asymptomatic-hospitalized-infectious-removed) model, to capture the spread of COVID-19 over multitype random networks with general degree distributions. We derive several key epidemiological metrics and then propose an age-stratified vaccination strategy to decrease the mortality and hospitalizations. Through extensive study, we discover that the outcome of vaccination prioritization depends on the reproduction number R 0 . Specifically, the elderly should be prioritized only when R 0 is relatively high. If ongoing intervention policies, such as universal masking, could suppress R 0 at a relatively low level, prioritizing the high-transmission age group (i.e., adults aged 20-39) is most effective to reduce both mortality and hospitalizations. These conclusions provide useful recommendations for age-based vaccination prioritization for COVID-19.