Social networks and forecasting the spread of HIV infection
Social networks and forecasting the spread of HIV infection
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DOI：
10.1097/00126334-200210010-00013
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发表时间：
2002-10-01
影响因子：
3.6
通讯作者：
Yang, SJ
中科院分区：
文献类型：
--
作者：
Bell, DC;Montoya, ID;Yang, SJ
这项研究是利用网络数据预测艾滋病毒在美国大城市传播的初步努力。数据是从德克萨斯州休斯敦低收入地区的吸毒者和社会人口统计学上匹配的非吸毒者的样本中收集的。两个基于样本的艾滋病毒流行模型和两个社会学模型与三个已发表的生物学模型相结合，以预测艾滋病毒血清阳性率的增长。这些预测预测，休斯敦市中心低收入居民的艾滋病毒年复合增长率在2.4%到16.5%之间。这些结果表明，预测对所使用的社会学模型的性质最为敏感。随机混合模型显示，与经验性网络数据相比，20年内预测的血清阳性率大约高估了三倍。因此，收集额外的社交网络数据可能是进行更准确预测的最重要要求。
This study is an initial effort to use network data to forecast the spread of HIV in a large U.S. city. Data were collected from a sample of drug users and sociodemographically matched nonusers in low-income areas of Houston, Texas. Two sample-based HIV prevalence models and two sociological models were combined with three published biological models to yield forecasts of the growth of HIV seroprevalence. The forecasts predict a compounded annual growth in HIV of between 2.4% and 16.5% among low-income residents of Houston's inner city. These results suggest that forecasts are most sensitive to the nature of the sociological model used. A random mixing model showed about a threefold overestimate of 20-year projected seroprevalence compared with the empiric network data. Thus, the collection of additional social network data is probably the most important requirement for more accurate projections.