Vaccine Simulation Calculator
The Vaccine Simulation Calculator uses a model-informed approach to evaluate and compare the impact of five age-stratified vaccine prioritization strategies on cumulative incidence, mortality and years of life lost. The SEIR model used here considers variation in the performance of the vaccine, disease dynamics, and population. This Calculator is a supplement to the paper Model-informed COVID-19 vaccine prioritization strategies by age and serostatus by Kate Bubar, Kyle Reinholt, Stephen Kissler, Marc Lipsitch, Sarah Cobey, Yonatan Grad, and Daniel Larremore. It allows users to see how different parameter values change the impact of different prioritization strategies.
Each point on the graphs below compares one year of simulation with vaccine rollout to one year of simulation without vaccine rollout to calculate a reduction in deaths, infections, and years of life lost. For a particular vaccine supply (horizontal axis) and rollout parameters (menu options), the curve that is on top represents the best of the five plotted strategies. Black dots show the point at which the prioritized groups are 70% vaccinated, after which vaccines are given without prioritization.
Parameters
Simulation Results
To report a bug, please send screenshots to kyle@colorado.edu and daniel.larremore@colorado.edu.
Legend
| Under 20 | |
| Adults 20-49 | |
| Adults 20+ | |
| Adults 60+ | |
| All Ages | |
| Vaccinate All | |
| Vacc. Only Seronegatives |
Technical Details
Full details of the model and methods are described in the paper Model-informed COVID-19 vaccine prioritization strategies by age and serostatus (Bubar et al., 2021). These simulations assume the vaccine does not protect against infection. (Exploration of vaccines with infection-blocking effects can be found in the manuscript and accompanying code). When targeting only seronegatives, we assume a serological test with specificity = 99% and sensitivity = 70%, to account for seroreversion.
Country - changing the country changes the age-specific contact structure (Prem et al.) and age demographic data (UNWPP).
Vaccine Efficacy - changing the efficacy changes the ability of the vaccine to protect against disease.
Cumulative Incidence at t0 - changing the cumulative incidence at t0 changes the proportion of the population that has previously been infected and is assumed to be immune to reinfection.
Transmission Blocking - changing the percent transmission blocking changes the ability of the vaccine to block transmission of the virus.
Rollout Speed - changing the rollout speed affects the fraction of the population that receives the vaccine each day.
Initial R - changing the reproductive number R affects the growth rate of the epidemic. R is calculated prior to the inclusion of cumulative incidence.
Acknowledgements
This web tool was created with the help of the BioFrontiers Institute IT department, BIT, at the University of Colorado at Boulder. BIT supports in-house scientific computing, compute infrastructure, and the development of open-source interactive tools to support our science. More information can be found at https://bficores.colorado.edu/biofrontiers-it.
The development of this calculator was supported through the MIDAS Coordination Center (MIDASNI2020-2) by a grant from the National Institute of General Medical Science (3U24GM132013-02S2). The research was also supported by that grant, as well as by the SeroNet program of the National Cancer Institute (1U01CA261277-01), and by the Morris-Singer Fund for the Center for Communicable Disease Dynamics at the Harvard T.H. Chan School of Public Health.
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