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.

中文翻译：

---

按年龄分层的 COVID-19 传播分析和疫苗接种：多类型随机网络方法


COVID-19 导致重症和死亡的风险随着年龄的增长而显着增加。因此，针对 COVID-19 动态的年龄分层建模是研究如何减少因 COVID-19 导致的住院率和死亡率的关键。通过利用网络理论，我们在复杂的接触网络中开发了一种按年龄分层的 COVID-19 流行病模型。具体来说，我们提出了标准 SEIR（易感暴露-感染-去除）隔室模型的扩展，称为年龄分层 SEAHIR（易感-暴露-无症状-住院-感染-去除）模型，以捕获 COVID-19 的传播情况。具有一般度分布的多类型随机网络。我们得出了几个关键的流行病学指标，然后提出了按年龄分层的疫苗接种策略，以降低死亡率和住院率。通过广泛的研究，我们发现疫苗接种优先顺序的结果取决于再生数 $R_0$ 。具体来说，只有当$R_0$相对较高时才应优先考虑老年人。如果持续的干预政策（例如普遍掩盖）可以将$R_0$抑制在相对较低的水平，那么优先考虑高传播年龄组（即20-39岁的成年人）对于降低死亡率和住院率最有效。这些结论为基于年龄的 COVID-19 疫苗接种优先顺序提供了有用的建议。