A Hybrid Epidemic Model to Explore Stochasticity in COVID-19 Dynamics

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• The COVID-19 pandemic's dynamic nature necessitated adaptive public health responses.
• Social distancing and mask mandates were widely adopted globally.
• Understanding transmission by subpopulations, like silent spreaders, is key for targeted strategies.

Purpose of the Study:

• To model COVID-19 transmission dynamics, differentiating between silent and symptomatic spreaders.
• To estimate transmission and death rates in British Columbia (BC) using real-time data.
• To assess the contribution of silent spreaders to early transmission and the impact of interventions.

Main Methods:

• Developed a novel COVID-19 model incorporating silent (SilS) and symptomatic (SymS) spreaders.
• Fitted the model to confirmed cases and deaths in BC to estimate epidemiological parameters.
• Constructed a hybrid stochastic model combining discrete processes and stochastic differential equations.

Main Results:

• Silent spreaders played a notable role in BC's initial COVID-19 wave.
• Public health interventions impacted transmission rates for both SilS and symptomatic spreaders.
• Variability in transmission rates influenced outbreak probability and severity in different scenarios.

Conclusions:

• The study highlights the importance of accounting for silent spreaders in pandemic modeling.
• Effective public health strategies must consider transmission dynamics influenced by both detected and undetected cases.
• Demographic and environmental variability significantly shape disease outbreak trajectories.