Buceta Javier, Johnson Kaylynn
Department of Chemical and Biomolecular Engineering, Lehigh University, Bethlehem, PA, 18015, United States of America.
Bioengineering Program, Lehigh University, Bethlehem, PA, 18015, United States of America.
PLoS One. 2017 Jun 12;12(6):e0179559. doi: 10.1371/journal.pone.0179559. eCollection 2017.
Understanding Ebola necessarily requires the characterization of the ecology of its main enzootic reservoir, i.e. bats, and its interplay with seasonal and enviroclimatic factors. Here we present a SIR compartmental model where we implement a bidirectional coupling between the available resources and the dynamics of the bat population in order to understand their migration patterns. Our compartmental modeling approach and simulations include transport terms to account for bats mobility and spatiotemporal climate variability. We hypothesize that environmental pressure is the main driving force for bats' migration and our results reveal the appearance of sustained migratory waves of Ebola virus infected bats coupled to resources availability. Ultimately, our study can be relevant to predict hot spots of Ebola outbreaks in space and time and suggest conservation policies to mitigate the risk of spillovers.
了解埃博拉病毒必然需要对其主要自然疫源宿主(即蝙蝠)的生态特征及其与季节和环境气候因素的相互作用进行描述。在此,我们提出一种SIR compartmental模型,在该模型中,我们实现了可用资源与蝙蝠种群动态之间的双向耦合,以便了解它们的迁徙模式。我们的compartmental建模方法和模拟包括运输项,以考虑蝙蝠的移动性和时空气候变异性。我们假设环境压力是蝙蝠迁徙的主要驱动力,我们的结果揭示了受埃博拉病毒感染的蝙蝠持续迁徙浪潮的出现与资源可用性相关。最终,我们的研究可能有助于预测埃博拉疫情在空间和时间上的热点区域,并提出保护政策以降低病毒溢出的风险。