Park Sang Woo, Bolker Benjamin M
Department of Mathematics & Statistics, McMaster University, Hamilton, Ontario, Canada.
Department of Biology, McMaster University, Hamilton, Ontario, Canada.
PLoS Comput Biol. 2017 Mar 31;13(3):e1005453. doi: 10.1371/journal.pcbi.1005453. eCollection 2017 Mar.
Early in an epidemic, high densities of susceptible hosts select for relatively high parasite virulence; later in the epidemic, lower susceptible densities select for lower virulence. Thus over the course of a typical epidemic the average virulence of parasite strains increases initially, peaks partway through the epidemic, then declines again. However, precise quantitative outcomes, such as the peak virulence reached and its timing, may depend sensitively on epidemiological details. Fraser et al. proposed a model for the eco-evolutionary dynamics of HIV that incorporates the tradeoffs between transmission and virulence (mediated by set-point viral load, SPVL) and their heritability between hosts. Their model used implicit equations to capture the effects of partnership dynamics that are at the core of epidemics of sexually transmitted diseases. Our models combine HIV virulence tradeoffs with a range of contact models, explicitly modeling partnership formation and dissolution and allowing for individuals to transmit disease outside of partnerships. We assess summary statistics such as the peak virulence (corresponding to the maximum value of population mean log10 SPVL achieved throughout the epidemic) across models for a range of parameters applicable to the HIV epidemic in sub-Saharan Africa. Although virulence trajectories are broadly similar across models, the timing and magnitude of the virulence peak vary considerably. Previously developed implicit models predicted lower virulence and slower progression at the peak (a maximum of 3.5 log10 SPVL) compared both to more realistic models and to simple random-mixing models with no partnership structure at all (both with a maximum of ≈ 4.7 log10 SPVL). In this range of models, the simplest random-mixing structure best approximates the most realistic model; this surprising outcome occurs because the dominance of extra-pair contact in the realistic model swamps the effects of partnership structure.
在疫情初期，大量易感宿主会选择具有相对高毒力的寄生虫；而在疫情后期，较低的易感宿主密度会选择低毒力的寄生虫。因此，在典型疫情过程中，寄生虫菌株的平均毒力最初会增加，在疫情中期达到峰值，然后再次下降。然而，精确的定量结果，如达到的峰值毒力及其出现时间，可能会敏感地依赖于流行病学细节。弗雷泽等人提出了一个艾滋病病毒生态进化动力学模型，该模型纳入了传播与毒力之间的权衡（由设定点病毒载量，即SPVL介导）以及它们在宿主之间的遗传性。他们的模型使用隐式方程来捕捉伙伴关系动态的影响，而伙伴关系动态是性传播疾病流行的核心。我们的模型将艾滋病病毒毒力权衡与一系列接触模型相结合，明确模拟伙伴关系的形成和解散，并允许个体在非伙伴关系中传播疾病。我们针对撒哈拉以南非洲艾滋病疫情适用的一系列参数，评估各模型中的汇总统计数据，如峰值毒力（对应于疫情期间种群平均log10 SPVL的最大值）。尽管各模型的毒力轨迹大致相似，但毒力峰值的时间和幅度差异很大。与更现实的模型以及完全没有伙伴关系结构的简单随机混合模型相比（两者的最大值均约为4.7 log10 SPVL），先前开发的隐式模型预测在峰值时毒力较低且进展较慢（最大值为3.5 log10 SPVL）。在这个模型范围内，最简单的随机混合结构最接近最现实的模型；出现这一惊人结果的原因是，现实模型中配偶外交配接触占主导地位，掩盖了伙伴关系结构的影响。
