Intervention Strategies for Epidemics: Does Ignoring Time Delay Lead to Incorrect Predictions?
Adrienna Bingham, Leah B. Shaw

TL;DR
This paper examines how ignoring time delays in epidemic models can lead to inaccurate predictions, proposing a multi-compartment model to compare exponential and delta distributions for better realism in intervention analysis.
Contribution
It introduces a multi-infected compartment model that interpolates between exponential and delta distributions to assess their impact on epidemic dynamics under intervention strategies.
Findings
Placement of control measures affects model accuracy.
Length of time delay influences prediction reliability.
Simpler models may be insufficient for realistic delay scenarios.
Abstract
Our paper investigates distributions of exposed and infectious time periods in an epidemic model and how applying a disease control strategy affects the model's accuracy. While ordinary differential equations are widely used for their simplicity, they incorporate an exponential distribution for time spent exposed or infectious. This allows for a high probability of unrealistically short exposed and infectious time periods. We propose that caution must be taken when applying intervention methods to basic models in order to avoid inaccurate predictions. Delay differential equations, which use a delta distribution for exposed and infectious periods, can provide better realism but are more difficult to use and analyze. We introduce a multi-infected compartment model to interpolate between an ODE model with exponential distributions and a DDE model with delta distributions in order to…
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Taxonomy
TopicsMathematical and Theoretical Epidemiology and Ecology Models · COVID-19 epidemiological studies · Evolution and Genetic Dynamics
