A data-driven dynamical system model for assessing targeted interventions against brucellosis in Tanzania

Area of Science:

• Veterinary Epidemiology
• Public Health Modeling
• Zoonotic Disease Control

Background:

• Brucellosis poses a significant public health and economic threat in Tanzania, especially where humans and livestock interact.
• Controlling brucellosis requires integrated strategies addressing human, animal, and environmental factors.

Purpose of the Study:

• To develop and evaluate a data-driven dynamical system model for assessing targeted brucellosis control interventions in Tanzania.
• To quantify the impact of public health education and animal management practices on disease transmission.

Main Methods:

• A dynamical system model integrating human, animal, and environmental factors was developed.
• Markov Chain Monte Carlo (MCMC) with Adaptive Metropolis algorithm was used for parameter estimation.
• A graph-theoretic approach calculated the effective reproduction number (R_e) and force of infection.

Main Results:

• Brucellosis transmission significantly declined, with the force of infection dropping from 0.99262 in 2021 to 0.13265 by May 2024.
• Annual brucellosis cases decreased from 173 in 2021 to 93 in 2024.
• Interventions like public health education and enhanced animal management (fencing, health checks, isolation, controlled breeding) reduced disease spread and contamination.

Conclusions:

• Sustained interventions effectively controlled brucellosis, maintaining R_e < 1.
• Continuous monitoring, expanded diagnostics, and region-specific strategies are crucial for long-term brucellosis control in Tanzania.