Social networks and forecasting the spread of HIV infection.
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.
Bell
David C
DC
Affiliated Systems Corporation, Houston, Texas 77027-6022, USA. dbell@affiliatedsystems.com
Montoya
Isaac D
ID
Atkinson
John S
JS
Yang
Su-Jau
SJ
D000328:Adult; D044383:Black People; D005260:Female; D005544:Forecasting; D015658:HIV Infections / Q000453:epidemiology* / Q000523:psychology / Q000635:transmission; D006801:Humans; D008297:Male; D008875:Middle Aged; D008960:Models, Psychological; D011203:Poverty; D015995:Prevalence; D012494:Sampling Studies; D012944:Social Support; D011795:Surveys and Questionnaires; D013781:Texas / Q000453:epidemiology; D014505:Urban Population; D001741:Black or African American