Age-Stratified COVID-19 Spread Analysis and Vaccination: A Multitype Random Network Approach.
IEEE Trans Netw Sci Eng
; 8(2): 1862-1872, 2021 Apr 01.
Article
in English
| MEDLINE | ID: covidwho-1373763
ABSTRACT
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 [Formula see text]. Specifically, the elderly should be prioritized only when [Formula see text] is relatively high. If ongoing intervention policies, such as universal masking, could suppress [Formula see text] 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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Prognostic study
/
Randomized controlled trials
Topics:
Vaccines
Language:
English
Journal:
IEEE Trans Netw Sci Eng
Year:
2021
Document Type:
Article
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