Hassman Rowan L, McCabe Iona M H, Smith Kaia M, Allen Linda J S
Department of Mathematics, Bates College, Lewiston, ME, 04240, USA.
Department of Mathematics, University of California, Santa Barbara, CA, 93106, USA.
Bull Math Biol. 2024 Dec 15;87(1):14. doi: 10.1007/s11538-024-01396-9.
Avian influenza virus type A causes an infectious disease that circulates among wild bird populations and regularly spills over into domesticated animals, such as poultry and swine. As the virus replicates in these intermediate hosts, mutations occur, increasing the likelihood of emergence of a new variant with greater transmission to humans and a potential threat to public health. Prior models for spread of avian influenza have included some combinations of the following components: multi-host populations, spillover into humans, environmental transmission, seasonality, and migration. We develop an ordinary differential equation (ODE) model for spread of a low pathogenic avian influenza virus that combines all of these factors, and we translate this into a stochastic continuous-time Markov chain model. Linearization of the ODE near the disease-free solution leads to the basic reproduction number , a threshold for disease extinction in both the ODE and Markov chain. The linearized Markov chain leads to a branching process approximation which provides an estimate for probability of disease extinction, i.e., probability no major disease outbreak in the multi-host system. The probability of disease extinction depends on the time and the population into which infection is introduced and reflects the seasonality inherent in the system. Some of the most sensitive parameters to model outcomes include wild bird recovery and environmental transmission. We find that migratory wild birds can drive infection numbers in other populations even when transmission parameters for those populations are low, and that environmental transmission can be a significant driver of infections.
甲型禽流感病毒引发一种在野生鸟类群体中传播的传染病,并经常传播到家养动物,如家禽和猪身上。随着病毒在这些中间宿主中复制,会发生突变,增加出现新变种的可能性,这种新变种更易传播给人类,对公众健康构成潜在威胁。先前的禽流感传播模型包含以下部分的一些组合:多宿主种群、传播给人类、环境传播、季节性和迁徙。我们针对低致病性禽流感病毒的传播开发了一个常微分方程(ODE)模型,该模型综合了所有这些因素,并将其转化为一个随机连续时间马尔可夫链模型。在无病解附近对ODE进行线性化处理可得出基本再生数,它是ODE和马尔可夫链中疾病灭绝的阈值。线性化的马尔可夫链会导出一个分支过程近似,该近似为疾病灭绝概率提供了估计,即在多宿主系统中不发生重大疾病爆发的概率。疾病灭绝概率取决于感染引入的时间和种群,并反映了系统固有的季节性。对模型结果最敏感的一些参数包括野生鸟类恢复情况和环境传播。我们发现,即使其他种群的传播参数较低,迁徙的野生鸟类也能推动其他种群中的感染数量增加,而且环境传播可能是感染的一个重要驱动因素。