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Background During the 2009 H1N1 influenza epidemic, policy makers debated over whether, when, and how long to close schools. While closing schools could have reduced influenza transmission thereby preventing cases, deaths, and health care costs, it may also have incurred substantial costs from increased childcare needs and lost productivity by teachers and other school employees. Methods A combination of agent-based and Monte Carlo economic simulation modeling was used to determine the cost-benefit of closing schools (vs. not closing schools) for different durations (range: 1 to 8 weeks) and symptomatic case incidence triggers (range: 1 to 30) for the state of Pennsylvania during the 2009 H1N1 epidemic. Different scenarios varied the basic reproductive rate (R 0 ) from 1.2, 1.6, to 2.0 and used case-hospitalization and case-fatality rates from the 2009 epidemic. Additional analyses determined the cost per influenza case averted of implementing school closure. Results For all scenarios explored, closing schools resulted in substantially higher net costs than not closing schools. For R 0 = 1.2, 1.6, and 2.0 epidemics, closing schools for 8 weeks would have resulted in median net costs of $21.0 billion (95% Range: $8.0 - $45.3 billion). The median cost per influenza case averted would have been $14,185 ($5,423 - $30,565) for R 0 = 1.2, $25,253 ($9,501 - $53,461) for R 0 = 1.6, and $23,483 ($8,870 - $50,926) for R 0 = 2.0. Conclusions Our study suggests that closing schools during the 2009 H1N1 epidemic could have resulted in substantial costs to society as the potential costs of lost productivity and childcare could have far outweighed the cost savings in preventing influenza cases.

Pandemic flu: talking tactics Numerical models of the epidemiology of a potential flu pandemic show there is no single magic bullet which can control the outbreak, but that a combination of approaches could reduce transmission and save many lives. Border restrictions are unlikely to have much effect and travel restrictions within one country would make very little difference to the spread of a pandemic within that country. The models predict that a pandemic in the United Kingdom would peak within two to three months of the first case, and be over within 4 months. It also shows that vaccines need to be available within two months of the start of a pandemic to have a big effect in reducing infection rates. That means that vaccines would need to be stockpiled in advance to be effective. 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 1 . Mathematical models are powerful tools for exploring this complex landscape of intervention strategies and quantifying the potential costs and benefits of different options 2 , 3 , 4 , 5 . Here we use a large-scale epidemic simulation 6 to examine intervention options should initial containment 6 , 7 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.

In the fall 2009, the University of Pittsburgh Models of Infectious Disease Agent Study (MIDAS) team employed an agent-based computer simulation model (ABM) of the greater Washington, DC, metropolitan region to assist the Office of the Assistant Secretary of Public Preparedness and Response, Department of Health and Human Services, to address several key questions regarding vaccine allocation during the 2009 H1N1 influenza pandemic, including comparing a vaccinating children (i.e., highest transmitters)—first policy versus the Advisory Committee on Immunization Practices (ACIP)—recommended vaccinating at-risk individuals-first policy. Our study supported adherence to the ACIP (instead of a children-first policy) prioritization recommendations for the H1N1 influenza vaccine when vaccine is in limited supply and that within the ACIP groups, children should receive highest priority.

When influenza vaccines are in short supply, allocating vaccines equitably among different jurisdictions can be challenging. But justice is not the only reason to ensure that poorer counties have the same access to influenza vaccines as do wealthier ones. Using a detailed computer simulation model of the Washington, D.C., metropolitan region, we found that limiting or delaying vaccination of residents of poorer counties could raise the total number of influenza infections and the number of new infections per day at the peak of an epidemic throughout the region-even in the wealthier counties that had received more timely and abundant vaccine access. Among other underlying reasons, poorer counties tend to have high-density populations and more children and other higher-risk people per household, resulting in more interactions and both increased transmission of influenza and greater risk for worse influenza outcomes. Thus, policy makers across the country, in poor and wealthy areas alike, have an incentive to ensure that poorer residents have equal access to vaccines. [PUBLICATION ABSTRACT]

Many published influenza models treat each simulation day as a weekday and do not distinguish weekend days. Consequently, the weekend effect on influenza transmission has not been fully explored. To assess whether distinguishing between weekday and weekend transmissions in simulation models of flu-like infectious disease models matters, this study uses an agent-based model of the Chicago, Illinois metropolitan area. Our study assesses whether including weekend effects is offset by increases in weekend contact patterns and if implementing 3-day weekends dampens disease transmission enough to warrant its use as a containment strategy. Results indicate that ignoring weekend behaviors without incorporating increases in community-based non-school contacts (i.e., compensatory behaviors) causes the peak case incidence day to occur 7 days earlier and can reduce the peak attack rate by as much as 60 %. These results are sensitive to the proportion of symptomatic cases that are assumed to remain at home until they recover. The 3-day weekend intervention has interesting possibilities, but the benefits may only be effective for mild epidemics. However, a 3-day weekend for schools would be less detrimental to the educational process than sustained permanent closing because student and teacher contact is maintained throughout the epidemic period. Also, a 4-day school and work week may be more easily accommodated by many types of schools and businesses. On the other hand, an additional day per week of school closure could result in substantial societal costs, with lost productivity and child care costs outweighing the savings of preventing influenza cases.

Well-conducted telephone surveys provide an economical means of estimating the prevalence of sexual and reproductive behaviors in a population. There is, however, a nontrivial potential for bias since respondents must report sensitive information to a human interviewer. The National STD and Behavior Measurement Experiment (NSBME) evaluates a new survey technology-telephone audio computer-assisted self-interviewing (T-ACASI)-that eliminates this requirement. The NSBME embedded a randomized experiment in a survey of probability samples of 1,543 U.S. and 744 Baltimore adults ages 18 to 45. Compared with NSBME respondents interviewed by human interviewers, respondents interviewed by T-ACASI were 1.5 to 1.6 times more likely to report same-gender sexual attraction, experience, and genital contact. The impact of T-ACASI was more pronounced (odds ratio = 2.5) for residents of locales that have historically been less tolerant of same-gender sexual behaviors and for respondents in households with children (odds ratio = 3.0).

Well-conducted telephone surveys provide an economical means of estimating the prevalence of sexual and reproductive behaviors in a population. There is, however, a nontrivial potential for bias since respondents must report sensitive information to a human interviewer. The National STD and Behavior Measurement Experiment (NSBME) evaluates a new survey technology—telephone audio computer-assisted self-interviewing (T-ACASI)—that eliminates this requirement. The NSBME embedded a randomized experiment in a survey of probability samples of 1,543 U.S. and 744 Baltimore adults ages 18 to 45. Compared with NSBME respondents interviewed by human interviewers, respondents interviewed by T-ACASI were 1.5 to 1.6 times more likely to report same-gender sexual attraction, experience, and genital contact. The impact of T-ACASI was more pronounced (odds ratio = 2.5) for residents of locales that have historically been less tolerant of same-gender sexual behaviors and for respondents in households with children (odds ratio = 3.0).

Information from 12 studies is combined to estimate the AIDS incubation distribution with greater precision than is possible from a single study. The analysis uses a hierarchy of parametric models based on a four-parameter generalized F distribution. This general model contains four standard two-parameter distributions as special cases. The cases are the Weibull, gamma, log-logistic, lognormal distributions. These four special cases subsume three distinct asymptotic hazard behaviors. As time increases beyond the median of approximately 10 years, the hazard can increase to infinity (Weibull), can plateau at some constant level (gamma), or can decrease to zero (log-logistic and lognormal). The Weibull, gamma and 'log-logistic distributions' which represent the three distinct asymptotic hazard behaviors, all fit the data as well as the generalized F distribution at the 25 percent significance level. Hence, we conclude that incubation data is still too limited to ascertain the specific hazard assumption that should be utilized in studies of the AIDS epidemic. Accordingly, efforts to model the AIDS epidemic (e.g., back-calculation approaches) should allow the incubation distribution to take several forms to adequately represent HIV estimation uncertainty. It is recommended that, at a minimum, the specific Weibull, gamma and log-logistic distributions estimated in this meta-analysis should all be used in modeling the AIDS epidemic, to reflect this uncertainty.