The Role of Vaccine Status Homophily in the COVID-19 Pandemic: A Cross-Sectional Survey with Modeling
Are, E. B.; Card, K. G.; Colijn, C.
Show abstract
BackgroundVaccine homophily describes non-heterogeneous vaccine uptake within contact networks. This study was performed to determine observable patterns of vaccine homophily, associations between vaccine homophily, self-reported vaccination, COVID-19 prevention behaviours, contact network size, and self-reported COVID-19, as well as the impact of vaccine homophily on disease transmission within and between vaccination groups under conditions of high and low vaccine efficacy. MethodsResidents of British Columbia, Canada, aged [≥]16 years, were recruited via online advertisements between February and March 2022, and provided information about vaccination status, perceived vaccination status of household and non-household contacts, compliance with COVID-19 prevention guidelines, and history of COVID-19. A deterministic mathematical model was used to assess transmission dynamics between vaccine status groups under conditions of high and low vaccine efficacy. ResultsVaccine homophily was observed among the 1304 respondents, but was lower among those with fewer doses (p<0.0001). Unvaccinated individuals had larger contact networks (p<0.0001), were more likely to report prior COVID-19 (p<0.0001), and reported lower compliance with COVID-19 prevention guidelines (p<0.0001). Mathematical modelling showed that vaccine homophily plays a considerable role in epidemic growth under conditions of high and low vaccine efficacy. Further, vaccine homophily contributes to a high force of infection among unvaccinated individuals under conditions of high vaccine efficacy, as well as elevated force of infection from unvaccinated to vaccinated individuals under conditions of low vaccine efficacy. InterpretationThe uneven uptake of COVID-19 vaccines and the nature of the contact network in the population play important roles in shaping COVID-19 transmission dynamics.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Fall 2021 Resurgence and COVID-19 Seroprevalence in Canada Modelling waning and boosting COVID-19 immunity in Canada A Canadian Immunization Research Network Study 95%
- Epidemiological differences in the impact of COVID-19 vaccination in the United States and China 95%
- Modeling the impact of COVID-19 vaccination in Lebanon: A call to speed-up vaccine roll out 94%
Similar papers in this journal
- Projecting the impact of a two-dose COVID-19 vaccination campaign in Ontario, Canada 96%
- Predicting immune protection against outcomes of infectious disease from population-level effectiveness data with application to COVID-19 95%
- Projected COVID-19 epidemic in the United States in the context of the effectiveness of a potential vaccine and implications for social distancing and face mask use 94%
Similar papers in this journal
- Beyond the new normal: assessing the feasibility of vaccine-based elimination of SARS-CoV-2 95%
- The impact of COVID-19 vaccination campaigns accounting for antibody-dependent enhancement 95%
- The Impact of Vaccination to Control COVID-19 Burden in the United States: A Simulation Modeling Approach 95%
Similar papers in this journal
- Mathematical modeling of vaccination rollout and NPIs lifting on COVID-19 transmission with VOC: a case study in Toronto, Canada 94%
- Association between vaccination rates and COVID-19 health outcomes in the United States: a population-level statistical analysis 94%
- How can the public health impact of vaccination be estimated? 93%
Similar papers in this journal
- Modelling the impact of hybrid immunity on future COVID-19 epidemic waves 94%
- Effectiveness of the BNT162b2 (Pfizer-BioNTech) and the ChAdOx1 nCoV-19 (Oxford-AstraZeneca) vaccines for reducing susceptibility to infection with the Delta variant (B.1.617.2) of SARS-CoV-2 94%
- Comparing alternative cholera vaccination strategies in Maela refugee camp using a transmission model 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.