Integrate-and-fire model of disease transmission.

Area of Science:

• Epidemiology
• Network Science
• Computational Biology

Background:

• Traditional epidemiological models often lack memory of past interactions or infections.
• Understanding disease transmission dynamics is crucial for public health interventions.

Purpose of the Study:

• To develop and validate a novel susceptible-infected-susceptible (SIS) epidemiological model incorporating memory.
• To assess the model's accuracy in replicating real-world disease data (COVID-19, influenza).
• To evaluate the impact of public health interventions on disease dynamics within the model.

Main Methods:

• Utilized integrate-and-fire nodes on a network to create a memory-enhanced SIS model.
• Modeled infectious matter accumulation and agent 'firing' (infection) based on thresholds.
• Incorporated immunity as an increased infection threshold post-recovery.
• Simulated single-strain and multi-strain dynamics on power-law networks.

Main Results:

• The single-strain model accurately predicted England's COVID-19 case data (RMSE 0.014%).
• The multi-strain model closely matched Canadian influenza A and B data (RMSEs 0.002% and 0.0012%).
• Interventions like quarantining and social gathering restrictions reduced peak infections but created secondary peaks upon removal.

Conclusions:

• The memory-enhanced epidemiological model effectively captures real-world disease transmission patterns.
• Interventions primarily reshape disease curves, influencing timing and peak height rather than total population infection.
• The model's memory component is key to its predictive accuracy and understanding intervention effects.