Consequences of Long-Distance Dispersal for Epidemic Spread: Patterns, Scaling, and Mitigation.
Consequences of Long-Distance Dispersal for Epidemic Spread: Patterns, Scaling, and Mitigation.
复制标题
长距离传播对流行病传播的后果：模式、规模和缓解。
作者：
Paul M. Severns;K. E. Sackett;D. Farber;C. Mundt
由远距离传播的病原体引起的流行病导致植物和动物（包括人类）的一些最具爆炸性和难以控制的疾病。然而，影响疾病传播的因素，特别是在疫情爆发的早期阶段，还没有得到很好的理解。我们目前的缩放关系，潜在的广泛相关性，从超过15年的现场和在硅片上的小麦条锈病传播的单焦点研究。这些关系的出现是由于占了更大比例的肥尾疾病梯度，可能经常被低估的疾病传播研究。在自然界中，轻峰型扩散梯度（尖峰厚尾）相对常见，可以用幂律函数表示。幂律尺度不变性属性生成在多个空间尺度上重复的模式，表明在第一代疾病爆发和随后的流行病传播期间疾病水平之间的重要和可预测的缩放关系。实验小麦条锈病爆发和疾病传播模拟支持幂律性质的理论缩放关系，并建议相对简单的缩放近似可能是有用的预测疾病的传播所造成的远距离分散的病原体。我们的研究结果表明，当缺乏实际的传播/疾病的数据，指数= 2的逆幂律可以提供一个合理的近似模型疾病传播。此外，我们的实验和模拟强烈表明，早期控制治疗与小的空间范围可能是更有效地抑制由远距离分散的病原体引起的爆发比延迟治疗的更大的面积。我们详细的缩放关系和疾病控制的相关后果可能广泛适用于植物和动物病原体的非指数约束，厚尾扩散梯度。
Epidemics caused by long-distance dispersed pathogens result in some of the most explosive and difficult to control diseases of both plants and animals (including humans). Yet the factors influencing disease spread, especially in the early stages of the outbreak, are not well-understood. We present scaling relationships, of potentially widespread relevance, that were developed from more than 15 years of field and in silico single focus studies of wheat stripe rust spread. These relationships emerged as a consequence of accounting for a greater proportion of the fat-tailed disease gradient that may be frequently underestimated in disease spread studies. Leptokurtic dispersal gradients (highly peaked and fat-tailed) are relatively common in nature and they can be represented by power law functions. Power law scale invariance properties generate patterns that repeat over multiple spatial scales, suggesting important and predictable scaling relationships between disease levels during the first generation of disease outbreaks and subsequent epidemic spread. Experimental wheat stripe rust outbreaks and disease spread simulations support theoretical scaling relationships from power law properties and suggest that relatively straightforward scaling approximations may be useful for projecting the spread of disease caused by long-distance dispersed pathogens. Our results suggest that, when actual dispersal/disease data are lacking, an inverse power law with exponent = 2 may provide a reasonable approximation for modeling disease spread. Furthermore, our experiments and simulations strongly suggest that early control treatments with small spatial extent are likely to be more effective at suppressing an outbreak caused by a long-distance dispersed pathogen than would delayed treatment of a larger area. The scaling relationships we detail and the associated consequences for disease control may be broadly applicable to plant and animal pathogens characterized by non-exponentially bound, fat-tailed dispersal gradients.