Brucellosis is one of the most significant zoonotic diseases worldwide, posing a serious threat to public health security and the economic development of livestock husbandry. The disease exhibits distinct periodic outbreak patterns driven by the interplay of seasonal sheep reproduction, environmental survival cycle of Brucella, and human production and business activities, thereby challenging the efficacy of conventional prevention strategies. In response to these challenges, we constructs a periodic multi-patch dynamical model integrating sheep, human, and Brucella in the environment. Sharp threshold dynamics are established via the basic reproduction number <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$\mathscr {R}_0$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> </math></EquationSource> </InlineEquation>: the disease-free equilibrium proves globally asymptotically stable when <InlineEquation ID="IEq2"> <EquationSource Format="TEX">$\mathscr {R}_0 <1$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> <mo><</mo> <mn>1</mn> </mrow> </math></EquationSource> </InlineEquation>, whereas an endemic periodic solution exist when <InlineEquation ID="IEq3"> <EquationSource Format="TEX">$\mathscr {R}_0 > 1$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> <mo>></mo> <mn>1</mn> </mrow> </math></EquationSource> </InlineEquation>. Model parameters are calibrated against Ningxia surveillance data (2016–2020) using nonlinear least squares and Markov chain Monte Carlo methods, demonstrating strong concordance with observed epidemiological trends. Furthermore, an adaptive control strategy synthesizing targeted culling and environmental disinfection is designed to track desired infection suppression trajectories. Numerical simulations reveal that patch-wise synchronization of interventions is necessary for disease elimination, whereas isolated control within single patches fails to eradicate transmission. This study furnishes a quantitative decision-making framework for coordinated, precision-based brucellosis governance in spatially heterogeneous, seasonally forced systems.

Dynamics and adaptive control of a periodic multi-patch brucellosis transmission model

期刊论文
发表日期：

2026年5月13日

查看全文

Ting Kang,
Wenyang Yang,
Boqiang Cao,
Qingyun Wang

摘要

Brucellosis is one of the most significant zoonotic diseases worldwide, posing a serious threat to public health security and the economic development of livestock husbandry. The disease exhibits distinct periodic outbreak patterns driven by the interplay of seasonal sheep reproduction, environmental survival cycle of Brucella, and human production and business activities, thereby challenging the efficacy of conventional prevention strategies. In response to these challenges, we constructs a periodic multi-patch dynamical model integrating sheep, human, and Brucella in the environment. Sharp threshold dynamics are established via the basic reproduction number $\mathscr {R}_0$ $R_{0}$ : the disease-free equilibrium proves globally asymptotically stable when $\mathscr {R}_0 <1$ $R_{0} < 1$ , whereas an endemic periodic solution exist when $\mathscr {R}_0 > 1$ $R_{0} > 1$ . Model parameters are calibrated against Ningxia surveillance data (2016–2020) using nonlinear least squares and Markov chain Monte Carlo methods, demonstrating strong concordance with observed epidemiological trends. Furthermore, an adaptive control strategy synthesizing targeted culling and environmental disinfection is designed to track desired infection suppression trajectories. Numerical simulations reveal that patch-wise synchronization of interventions is necessary for disease elimination, whereas isolated control within single patches fails to eradicate transmission. This study furnishes a quantitative decision-making framework for coordinated, precision-based brucellosis governance in spatially heterogeneous, seasonally forced systems.