Voluntary universal testing and treatment is unlikely to lead to HIV elimination: a modeling analysis

Recently Granich et al. at the World Health Organization (WHO) concluded, using mathematical modeling, that HIV epidemics could be eliminated within a decade. They assumed all individuals would be tested annually and every infected individual (regardless of stage of infection) would be put on treatment. Based on this modeling study the WHO is considering using universal testing and treatment as an HIV elimination strategy. Here we examine the study by Granich et al. and assess its validity. We present new analyses of their model by varying assumptions and parameter values. We find that under certain very optimistic assumptions HIV elimination would be (theoretically) possible, but it would take at least 70 years. To obtain this result we assumed ~65% of symptomatic and ~20% of asymptomatic individuals would be treated per year; ARVs would reduce infectivity of treated individuals a hundred fold, and only 5% of symptomatic individuals would give up treatment per year. Even under optimistic assumptions we find elimination to be unlikely. For example, we show if ~65% of symptomatic individuals are treated per year and treated individuals are completely noninfectious, HIV will remain endemic with a prevalence of 34% and an incidence of 2% per year. We conclude that the model developed by Granich et al., when used with realistic parameter values, does not show HIV elimination is possible. However our modeling results show treatment could act as an effective prevention tool and significantly reduce transmission, even if only symptomatic individuals receive ARVs. Treatment should first, and foremost, be used for therapeutic purposes. Hence, we recommend – when resources are limited - targeting those in need of treatment. Such a strategy would be ethical, feasible and epidemiologically sound. We advise that models used as health policy tools should be carefully evaluated and their results interpreted with caution

Potential Public Health Impact of New Tuberculosis Vaccines

Widely deployed and highly effective pre- or postexposure vaccines would reduce the number of TB cases by only one third

Predicting the emergence of drug-resistant HSV-2: new predictions

BACKGROUND: Mathematical models can be used to predict the emergence and transmission of antiviral resistance. Previously it has been predicted that high usage of antivirals (in immunocompetent populations) to treat Herpes Simplex Virus type 2 (HSV-2) would only lead to fairly low levels of antiviral resistance. The HSV-2 predictions were based upon the assumption that drug-resistant strains of HSV-2 would be less infectious than drug-sensitive strains but that the drug-resistant strains would not be impaired in their ability to reactivate. Recent data suggest that some drug-resistant strains of HSV-2 are likely to be impaired in their ability to reactivate. Objectives: (1) To predict the effect of a high usage of antivirals on the prevalence of drug-resistant HSV-2 under the assumption that drug-resistant strains will be less infectious than drug-sensitive strains of HSV-2 and also have an impaired ability to reactivate. (2) To compare predictions with previous published predictions. METHODS: We generated theoretical drug-resistant HSV-2 strains that were attenuated (in comparison with drug-sensitive strains) in both infectivity and ability to reactivate. We then used a transmission model to predict the emergence and transmission of drug-resistant HSV-2 in the immunocompetent population assuming a high usage of antivirals. RESULTS: Our predictions are an order of magnitude lower than previous predictions; we predict that even after 25 years of high antiviral usage only 5 out of 10,000 immunocompetent individuals will be shedding drug-resistant virus. Furthermore, after 25 years, 52 cases of HSV-2 would have been prevented for each prevalent case of drug-resistant HSV-2. CONCLUSIONS: The predicted levels of drug-resistant HSV-2 for the immunocompetent population are so low that it seems unlikely that cases of drug-resistant HSV-2 will be detected

The importance of including dynamic social networks when modeling epidemics of airborne infections: does increasing complexity increase accuracy?

Mathematical models are useful tools for understanding and predicting epidemics. A recent innovative modeling study by Stehle and colleagues addressed the issue of how complex models need to be to ensure accuracy. The authors collected data on face-to-face contacts during a two-day conference. They then constructed a series of dynamic social contact networks, each of which was used to model an epidemic generated by a fast-spreading airborne pathogen. Intriguingly, Stehle and colleagues found that increasing model complexity did not always increase accuracy. Specifically, the most detailed contact network and a simplified version of this network generated very similar results. These results are extremely interesting and require further exploration to determine their generalizability