Development of strategies for mitigating the severity of a new influenza pandemic is now a top global public health priority. Influenza prevention and containment strategies can be considered under the broad categories of antiviral, vaccine and non-pharmaceutical (case isolation, household quarantine, school or workplace closure, restrictions on travel) measures. Mathematical models are powerful tools for exploring this complex landscape of intervention strategies and quantifying the potential costs and benefits of different options. Here we use a large-scale epidemic simulation to examine intervention options should initial containment of a novel influenza outbreak fail, using Great Britain and the United States as examples. We find that border restrictions and/or internal travel restrictions are unlikely to delay spread by more than 2-3 weeks unless more than 99% effective. School closure during the peak of a pandemic can reduce peak attack rates by up to 40%, but has little impact on overall attack rates, whereas case isolation or household quarantine could have a significant impact, if feasible. Treatment of clinical cases can reduce transmission, but only if antivirals are given within a day of symptoms starting. Given enough drugs for 50% of the population, household-based prophylaxis coupled with reactive school closure could reduce clinical attack rates by 40-50%. More widespread prophylaxis would be even more logistically challenging but might reduce attack rates by over 75%. Vaccine stockpiled in advance of a pandemic could significantly reduce attack rates even if of low efficacy. Estimates of policy effectiveness will change if the characteristics of a future pandemic strain differ substantially from those seen in past pandemics.

Planning a response to an outbreak of a pandemic strain of influenza is a high public health priority. Three research groups using different individual-based, stochastic simulation models have examined the consequences of intervention strategies chosen in consultation with U.S. public health workers. The first goal is to simulate the effectiveness of a set of potentially feasible intervention strategies. Combinations called targeted layered containment (TLC) of influenza antiviral treatment and prophylaxis and nonpharmaceutical interventions of quarantine, isolation, school closure, community social distancing, and workplace social distancing are considered. The second goal is to examine the robustness of the results to model assumptions. The comparisons focus on a pandemic outbreak in a population similar to that of Chicago, with Ϸ8.6 million people. The simulations suggest that at the expected transmissibility of a pandemic strain, timely implementation of a combination of targeted household antiviral prophylaxis, and social distancing measures could substantially lower the illness attack rate before a highly efficacious vaccine could become available. Timely initiation of measures and school closure play important roles. Because of the current lack of data on which to base such models, further field research is recommended to learn more about the sources of transmission and the effectiveness of social distancing measures in reducing influenza transmission.influenza antiviral agents ͉ mitigation ͉ prophylaxis ͉ social distancing ͉ transmission

Recent studies have reported on the utility of audio computer-assisted self-interviewing (ACASI) in surveys of human immunodeficiency virus (HIV) risk behaviors that involve a single assessment. This paper reports the results of a test of ACASI within a longitudinal study of HIV risk behavior and infection. Study participants (gay men (n = 1,974) and injection drug users (n = 903)) were randomly assigned to either ACASI or interviewer-administered assessment at their second follow-up visit 12 months after baseline. Significantly more of the sexually active gay men assessed via ACASI reported having sexual partners who were HIV antibody positive (odds ratio = 1.36, 95% confidence interval: 1.08, 1.72), and a higher proportion reported unprotected receptive anal intercourse. Among injection drug users (IDUs), our hypothesis was partially supported. Significantly more IDUs assessed via ACASI reported using a needle after another person without cleaning it (odds ratio = 2.40, 95% confidence interval: 1.34, 4.30). ACASI-assessed IDUs reported similar rates of needle sharing and needle exchange use but a lower frequency of injection. Participants reported few problems using ACASI, and it was well accepted among members of both risk groups. Sixty percent of the participants felt that the ACASI elicited more honest responses than did interviewer-administered questionnaires. Together, these data are consistent with prior research findings and suggest that ACASI can enhance the quality of behavioral assessment and provide an acceptable method for collecting self-reports of HIV risk behavior in longitudinal studies and clinical trials of prevention interventions.

Responding to potentially sensitive questions should not be seen as merely "providing data," but rather as an activity with complex motivations. These motivations can include maintaining social respect, obtaining social support, and altruism. Ideally, procedures for collecting self-report data would maximize altruistic motivation while accommodating the other motives.

Background: Mathematical and computational models provide valuable tools that help public health planners to evaluate competing health interventions, especially for novel circumstances that cannot be examined through observational or controlled studies, such as pandemic influenza. The spread of diseases like influenza depends on the mixing patterns within the population, and these mixing patterns depend in part on local factors including the spatial distribution and age structure of the population, the distribution of size and composition of households, employment status and commuting patterns of adults, and the size and age structure of schools. Finally, public health planners must take into account the health behavior patterns of the population, patterns that often vary according to socioeconomic factors such as race, household income, and education levels.