Epidemic Models with Random Infectious Period
Riano, G.
Show abstract
In this paper, we present an extension to the classical SIR epidemic transmission model that uses any general probability distribution for the length of the infectious period. The classical SIR model implicitly requires an exponential distribution for the length of this period of time. We will show how a general distribution can be easily taken into account using the Transient Little Law and present numerical methods to solve the model in an efficient way. Our numerical experiments show that in the presence of a more realistic distribution, with lower variability than the exponential distribution, the size of peak of infected individuals on the graph will be higher and occur earlier. Conversely, a higher-variability distribution will lead to a lower peak that takes longer to dissipate. We also discuss some extensions to the basic model, to include variants like SEIRD and SIS. These findings should have profound and important consequences in the design of public policy.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Adding a reaction-restoration type transmission rate dynamic law to the basic SEIR COVID-19 model 96%
- Increasing efficacy of contact-tracing applications by user referrals and stricter quarantining 96%
- Countering the potential re-emergence of a deadly infectious disease - information warfare, identifying strategic threats, launching countermeasures 96%
Similar papers in this journal
- Effect of human behavior on the evolution of viral strains during an epidemic 95%
- Modeling and Global Sensitivity Analysis of Strategies to Mitigate Covid-19 Transmission on a Structured College Campus 95%
- Laplacian Dynamics and Kron Reduction in Species-Reaction Graphs of Chemical Reaction Networks 95%
Similar papers in this journal
- Model Based Estimation of the SARS-CoV-2 Immunization Level in Austria and Consequences for Herd Immunity Effects 95%
- Ranking the Effectiveness of Non-Pharmaceutical Interventions to Counter COVID-19 in UK Universities with Vaccinated Population 95%
- Interpreting epidemiological surveillance data: A modelling study from Pune City 95%
"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.