O'Dea Eamon B, Wilke Claus O
Section of Integrative Biology, The University of Texas at Austin, Austin, TX 78712, USA.
Interdiscip Perspect Infect Dis. 2011;2011:238743. doi: 10.1155/2011/238743. Epub 2010 Dec 1.
The inference of population dynamics from molecular sequence data is becoming an important new method for the surveillance of infectious diseases. Here, we examine how heterogeneity in contact shapes the genealogies of parasitic agents. Using extensive simulations, we find that contact heterogeneity can have a strong effect on how the structure of genealogies reflects epidemiologically relevant quantities such as the proportion of a population that is infected. Comparing the simulations to BEAST reconstructions, we also find that contact heterogeneity can increase the number of sequence isolates required to estimate these quantities over the course of an epidemic. Our results suggest that data about contact-network structure will be required in addition to sequence data for accurate estimation of a parasitic agent's genealogy. We conclude that network models will be important for progress in this area.
从分子序列数据推断种群动态正成为传染病监测的一种重要新方法。在此，我们研究接触的异质性如何塑造寄生虫病原体的谱系。通过广泛的模拟，我们发现接触异质性会对谱系结构反映诸如感染人群比例等流行病学相关数量的方式产生强烈影响。将模拟结果与贝叶斯进化分析采样树（BEAST）重建结果进行比较，我们还发现接触异质性会增加在疫情过程中估计这些数量所需的序列分离株数量。我们的结果表明，除了序列数据外，还需要有关接触网络结构的数据，才能准确估计寄生虫病原体的谱系。我们得出结论，网络模型对于该领域的进展将至关重要。
