Network structure and rapid HIV transmission among people who inject drugs: A simulation-based analysis.

Network structure and rapid HIV transmission among people who inject drugs: A simulation-based analysis.
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注射吸毒人群的网络结构与艾滋病病毒快速传播:一项基于模拟的分析

DOI:
10.1016/j.epidem.2020.100426
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发表时间:
2021-03
期刊:
影响因子:
3.8
通讯作者:
Goedel WC
Goedel WC
中科院分区:
医学2区
文献类型:
--
作者:
Singleton AL;Marshall BDL;Bessey S;Harrison MT;Galvani AP;Yedinak JL;Jacka BP;Goodreau SM;Goedel WC

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由于注射吸毒者中的艾滋病毒发病率在北美药物过量流行病不断升级的背景下增长,调查网络结构如何影响艾滋病毒快速传播的脆弱性对于预防疫情是必要的。我们比较了2015年印第安纳州农村疫情中观察到的接触者追踪网络的特征,以及由基于代理的网络模型生成的1000个网络,其中个体数量(n = 420)和它们之间的联系(n = 913)大致相同。我们将初始HIV感染引入模拟网络,并比较随后的流行病行为(例如,5年内累计感染艾滋病毒)。该模型能够产生具有基本上可比较的特征和艾滋病毒感染事件总数的网络。虽然该模型无法产生具有可比内聚性的网络(观察到的网络的传递性值与模拟网络的平均值相差35.7个标准差),但模拟网络的结构变异性允许研究其对HIV传播的潜在促进作用。这些研究结果强调,需要继续发展注射网络模拟研究,同时收集经验数据,以进一步研究网络特征如何在这次和未来的疫情中发挥作用。
As HIV incidence among people who inject drugs grows in the context of an escalating drug overdose epidemic in North America, investigating how network structure may affect vulnerability to rapid HIV transmission is necessary for preventing outbreaks. We compared the characteristics of the observed contact tracing network from the 2015 outbreak in rural Indiana with 1000 networks generated by an agent-based network model with approximately the same number of individuals (n = 420) and ties between them (n = 913). We introduced an initial HIV infection into the simulated networks and compared the subsequent epidemic behavior (e.g., cumulative HIV infections over 5 years). The model was able to produce networks with largely comparable characteristics and total numbers of incident HIV infections. Although the model was unable to produce networks with comparable cohesiveness (where the observed network had a transitivity value 35.7 standard deviations from the mean of the simulated networks), the structural variability of the simulated networks allowed for investigation into their potential facilitation of HIV transmission. These findings emphasize the need for continued development of injection network simulation studies in tandem with empirical data collection to further investigate how network characteristics played a role in this and future outbreaks.
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