Rational spatio-temporal strategies for controlling a Chagas disease vector in urban environments

Area of Science:

• Vector-borne disease control
• Computational epidemiology
• Urban entomology

Background:

• Controlling communicable diseases in urban settings requires rational intervention design.
• The Chagas disease vector, Triatoma infestans, often returns after insecticide effectiveness diminishes.
• Current control strategies face challenges in complex urban environments.

Purpose of the Study:

• To adapt a genetic algorithm for optimizing spatio-temporal insecticide campaigns against Triatoma infestans in urban areas.
• To develop a more effective strategy for preventing vector resurgence compared to current methods.

Main Methods:

• Adaptation of a genetic algorithm, originally for the travelling salesman problem, to design insecticide campaigns.
• Analysis of spatio-temporal intervention strategies using simplified models.
• Comparison of the novel strategy's effectiveness against current sequential application methods.

Main Results:

• The adapted genetic algorithm identified a strategy reducing expected vector return by 34-fold.
• Strategy effectiveness is sensitive to insecticide duration and campaign delays, showing chaotic fluctuations.
• The optimized strategy significantly outperforms current sequential application to contiguous communities.

Conclusions:

• A novel spatio-temporal strategy, designed with a genetic algorithm, substantially improves control of the urban Chagas disease vector Triatoma infestans.
• This approach provides a detailed procedure for enhancing vector control and offers generalizable guidelines for other disease interventions in complex environments.