Six supervised classification methods (random forest, support vector machines, nearest neighbor with three decision rules, linear and flexible discriminant analysis) were used in identifying partial epidemic curves from six agent-based stochastic simulations of influenza epidemics. The accuracy of the methods was compared using a performance metric based on the McNemar test.

The findings showed that: (1) assumptions made by the methods regarding the structure of an epidemic curve influences their performance i.e. methods with fewer assumptions perform best, (2) the performance of most methods is consistent across different individual-based networks for Seattle, Los Angeles and New York and (3) combining classifiers using a weighting approach does not guarantee better prediction.

Using marginal models, we estimate association measures between HCV and HIV infections at individual level, i.e., odds ratios and correlation coefficients, and we regress them against some risk factors, e.g., the length of the injecting career, the age at first injection, the ever sharing of syringes, and the frequency of current injecting. In addition, we fit random-effects models that take into account the individual heterogeneity in the acquisition of the infections. For our analysis, we use cross-sectional data from two independent serological surveys, one carried out in Italy (IT) in 2005 on 856 subjects, and the other in three Spanish (ES) cities, between 2001 and 2003, on 589 subjects.

We found that the infections are positively associated within individuals, e.g., OR_IT=2.56 with 95% confidence interval (CI) (1.43, 6.68) and OR_ES= 2.42, with 95% CI (1.41, 4.30). We found that the odds ratio and the correlation between HCV and HIV infections increase positively with the length of the injecting career. Moreover, they are found to be significantly positive in case IDUs have never shared syringes or report low injecting frequencies. The variance of the individual random effects is positive, e.g., σ_b²=0.34 (0.14, 0.62), indicating that there is significant individual heterogeneity in the acquisition of the infections.

Our results show that a significant association between HCV and HIV infections within IDUs is related to significant individual heterogeneity in the acquisition of the infections. Indeed, the association between these infections in IDUs who report ever sharing syringes is not significant, which can be explained by a higher homogeneity in their behaviors and, therefore, in their acquisition of the infections.

Additionally, we analyze how a given preventative control strategy, optimized via simulation from a fitted model with assumed latent and infectious periods, is affected by such misspecification. We observe bias in the estimation of model parameters as latent and infectious periods become more misspecified, as well as a significant deviation in estimates of the basic reproduction number from those observed under the true model. Where the misspecification results in a higher basic reproduction number estimate, we also find that a more stringent control policy is required to achieve a given policy goal.

We developed a mathematical model to study the impact of a wide-scale population usage of VMB in a heterosexual population. Gender ratios of prevented infections and prevalence reduction are evaluated in 63 different intervention schedules including continuous and interrupted ARV-VMB use by HIV-positive women. The influence of different factors on population-level benefits is also studied through Monte Carlo simulations using parameters sampled from primary ranges representative of developing countries.

Our analysis indicates that women are more likely than men to benefit from ARV-VMB use since 78-80% of the total 63,000 simulations investigated (under different parameter sets) showed a female advantage whether benefit is measured as cumulative number of infections prevented, the percentage of cumulative infections prevented, or the expected reduction in prevalence. Stratified analysis by scenarios indicates that the likelihood of a male advantage with respect to the fractions of prevented infections varies from 6% to 49% among the scenarios. It is substantial only if the risk of systemic absorption and development of resistance to ARV-VMB is high and the HIV-positive women use VMB indefinitely without interruption. Therefore, the use of ARV-VMB, with successful control measures restricting usage by HIV-positive women, is still very much a female prevention tool.

The relationships are examined using data from 10 sites which were randomly selected to be proactive culling sites in the UK Randomized Badger Culling Trial. The badger data are from the initial cull only and the cattle incidence data pre-date the initial badger cull.

The analysis of the proportion of cattle herds with newly detected TB infection in a year, showed strong support for the model including significant frequency-dependent transmission between cattle herds and significant badger-to-herd transmission proportional to the proportion of M. bovis-infected badgers. Based on the model best fitting all the data, 3.4% of herds (95% CI: 0 – 6.7%) would be expected to have TB infection newly detected (i.e. to experience a TB herd breakdown) in a year, in the absence of transmission from badgers. Thus, the null hypothesis that at equilibrium herd-to-herd transmission is not sufficient to sustain TB in the cattle population, in the absence of transmission from badgers cannot be rejected (p=0.18). Omitting data from three sites in which badger carcase storage may have affected data quality; the estimate dropped to 1.3% of herds (95% CI: 0 – 6.5%) with p=0.76.

The results demonstrate close positive relationships between bovine TB in cattle herds and badgers infectious with M. bovis. The results indicate that TB in cattle herds could be substantially reduced, possibly even eliminated, in the absence of transmission from badgers to cattle. The results are based on observational data and a small data set to provide weaker inference than from a large experimental study.

We use quantitative examples and simulations to investigate this issue, focusing on effectiveness estimated by the standard intention to treat analysis approach. We also assess the application of recently developed methods for estimating the causal effect of treatment assignment, accounting for observed levels of condom use.

Results show that observed levels of condom use may have substantial impacts on the conclusions drawn from standard analyses of prevention trials, with the most serious effects observed in studies with unblinded control groups. Causal estimation methods accounting for post-randomization behaviors can help clarify these impacts by focusing attention on effectiveness for controlled levels of condom use.

Supplemental causal analyses that account for post-randomization condom use may provide useful information about possible efficacy that complement standard analyses. However, interpretation of results may be limited by the quality of available data on adherence behavior, and limited statistical power.