Periodic forcing in a three-level cellular automata model for a vector-transmitted disease

Area of Science:

• Epidemiology
• Computational Biology
• Mathematical Modeling

Background:

• Vector-borne diseases like dengue exhibit complex spatiotemporal dynamics sensitive to seasonal variations.
• Existing mean-field models often fail to capture the intricate fluctuations and patterns observed in real-world epidemics.

Purpose of the Study:

• To develop and analyze a periodically forced two-dimensional cellular automata model for simulating dengue epidemics.
• To investigate the influence of seasonality, mobility, and vector control on disease transmission dynamics.
• To compare model predictions with actual dengue epidemic data from Brazil.

Main Methods:

• A three-population coupled cellular automata model (human, adult vector, immature vector) was employed.
• The model incorporates external seasonality forcing, population mobility, and vector control interventions.
• Model parameters were calibrated using reported data, and simulations were validated against time-series data from Brazilian cities.

Main Results:

• The cellular automata model successfully reproduced complex spatiotemporal patterns and fluctuations not captured by mean-field approaches.
• Model simulations aligned with actual dengue epidemic time series, demonstrating robustness across different locations.
• The study revealed distinct long-term epidemic evolution in the absence of external periodic forcing.

Conclusions:

• The developed cellular automata model provides a robust framework for understanding and predicting dengue epidemic dynamics.
• Local rainfall modulation is crucial for accurate epidemic forecasting when incorporated into the model's forcing term.
• The interplay between epidemic thresholds and vector control strategies, influenced by human mobility, significantly impacts disease spread.