Estimating Antibody Levels in Infected and Vaccinated Individuals
Author Information
Author(s): Prajakta Bedekar, Rayanne A. Luke, Anthony J. Kearsley
Primary Institution: Johns Hopkins University
Hypothesis
Can a time-inhomogeneous Markov chain model effectively estimate the prevalence of antibody responses in individuals who are either infected or vaccinated?
Conclusion
The study presents a new Markov chain model that accurately estimates the prevalence of antibody responses in populations affected by infections and vaccinations.
Supporting Evidence
- The model effectively estimates antibody levels using synthetic data.
- Results show that the new approach can handle complex interactions between infections and vaccinations.
- Prevalence estimates align closely with true values in various scenarios.
Takeaway
This study created a new way to understand how many people have antibodies after being infected or vaccinated, which helps us know how well the population is protected.
Methodology
The authors developed a time-inhomogeneous Markov chain model to estimate prevalence using synthetic data based on antibody measurements.
Potential Biases
Potential biases may arise from overlapping antibody responses in infected and vaccinated individuals.
Limitations
The model assumes no reinfection or revaccination, which may not reflect real-world scenarios.
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website