When and why direct transmission models can be used for environmentally persistent pathogens.

Area of Science:

• Epidemiology and Mathematical Modeling
• Disease Ecology
• Infectious Disease Dynamics

Background:

• Susceptible-Infected-Removed (SIR) models are foundational in epidemiology for studying infectious diseases.
• Direct Transmission Models (DTM) simplify disease spread as discrete events, suitable for many pathogens.
• Environmental Transmission Models (ETM) are needed for diseases with indirect spread via environmental pathogens.

Purpose of the Study:

• To compare the stochastic susceptible-exposed-infected-removed (SEIR) DTM with the susceptible-exposed-infected-removed-pathogen (SEIR-P) ETM.
• To investigate the conditions under which SEIR DTM dynamics approximate SEIR-P ETM dynamics.
• To assess the robustness of the SEIR model to indirect transmission using graphical posterior predictive checks (GPPC).

Main Methods:

• Theoretical analysis showing SEIR as a timescale separation limit of SEIR-P.
• Graphical posterior predictive checks (GPPC) to validate SEIR against simulated SEIR-P data.
• Case study using white spot disease (WSD) in shrimp, estimating SEIR-P parameters from experimental data.

Main Results:

• SEIR DTM dynamics resemble SEIR-P ETM dynamics when the pathogen's environmental timescale is short relative to the host population's timescale.
• SEIR models demonstrate robustness to deviations from direct transmission in many scenarios.
• Simulations of white spot disease management showed SEIR and SEIR-P outputs diverge significantly with more frequent (6-hourly) removal of diseased shrimp compared to less frequent (24-hourly) removals.

Conclusions:

• The SEIR model serves as a valid approximation for SEIR-P models under specific timescale conditions, particularly when pathogen environmental persistence is limited.
• The frequency of intervention (e.g., removal of infected individuals) can critically influence the divergence between DTM and ETM predictions.
• Understanding the pathogen's environmental timescale is crucial for selecting appropriate epidemiological models and accurately predicting disease outbreak dynamics.